chore(deps): update python docker tag to v3.14

.gitea/workflows/ci.yml

@@ -19,64 +19,15 @@ jobs:
         uses: actions/checkout@v4
         with:
           ssl-verify: false
-          # Full history: needed to diff against github.event.before
-          fetch-depth: 0
 
       - name: Set image tag
         id: tag
         run: echo "TAG=${GITHUB_SHA::8}" >> $GITHUB_OUTPUT
 
-      - name: Detect changed components
-        id: changes
-        run: |
-          BEFORE="${{ github.event.before }}"
-          CHANGED=""
-          case "$BEFORE" in
-            ""|0000000000000000000000000000000000000000)
-              echo "First push or unknown base — building all images"
-              CHANGED="__all__"
-              ;;
-            *)
-              if git cat-file -e "$BEFORE" 2>/dev/null; then
-                CHANGED=$(git diff --name-only "$BEFORE" "$GITHUB_SHA")
-              else
-                echo "Base commit $BEFORE not in history (force push?) — building all images"
-                CHANGED="__all__"
-              fi
-              ;;
-          esac
-          echo "Changed files:"
-          echo "$CHANGED"
-
-          if [ "$CHANGED" = "__all__" ]; then
-            BOT=true; API=true; DASH=true
-          else
-            BOT=false; API=false; DASH=false
-            matches() { echo "$CHANGED" | grep -qE "$1"; }
-            # The workflow itself affects every image build
-            if matches '^\.gitea/workflows/ci\.yml$'; then BOT=true; API=true; DASH=true; fi
-            # bot and api images both COPY bot/, api/ and requirements.txt
-            if matches '^(bot/|api/|requirements\.txt$)'; then BOT=true; API=true; fi
-            if matches '^Dockerfile$'; then BOT=true; fi
-            if matches '^Dockerfile\.api$'; then API=true; fi
-            # dashboard image builds from the dashboard/ context only
-            if matches '^dashboard/'; then DASH=true; fi
-          fi
-
-          ANY=false
-          if [ "$BOT" = "true" ] || [ "$API" = "true" ] || [ "$DASH" = "true" ]; then ANY=true; fi
-          echo "build_bot=$BOT"        >> $GITHUB_OUTPUT
-          echo "build_api=$API"        >> $GITHUB_OUTPUT
-          echo "build_dashboard=$DASH" >> $GITHUB_OUTPUT
-          echo "build_any=$ANY"        >> $GITHUB_OUTPUT
-          echo "Will build: bot=$BOT api=$API dashboard=$DASH"
-
       - name: Log in to registry
-        if: steps.changes.outputs.build_any == 'true'
         run: echo "${{ secrets.CI_TOKEN }}" | docker login gitea.gitea.svc.cluster.local:3000 -u chemavx --password-stdin
 
       - name: Create buildx builder
-        if: steps.changes.outputs.build_any == 'true'
         run: |
           cat > /tmp/buildkitd.toml << 'EOF'
           [registry."registry-cache.registry-cache.svc.cluster.local:5000"]
@@ -99,7 +50,6 @@ jobs:
           docker buildx inspect --bootstrap
 
       - name: Build and push bot image
-        if: steps.changes.outputs.build_bot == 'true'
         run: |
           TAG=${{ steps.tag.outputs.TAG }}
           docker buildx build \
@@ -111,7 +61,6 @@ jobs:
             -f Dockerfile .
 
       - name: Build and push API image
-        if: steps.changes.outputs.build_api == 'true'
         run: |
           TAG=${{ steps.tag.outputs.TAG }}
           docker buildx build \
@@ -123,7 +72,6 @@ jobs:
             -f Dockerfile.api .
 
       - name: Build and push dashboard image
-        if: steps.changes.outputs.build_dashboard == 'true'
         run: |
           TAG=${{ steps.tag.outputs.TAG }}
           docker buildx build \
@@ -136,7 +84,6 @@ jobs:
             dashboard
 
       - name: Verify images in registry
-        if: steps.changes.outputs.build_any == 'true'
         run: |
           TAG=${{ steps.tag.outputs.TAG }}
           check_image() {
@@ -151,18 +98,11 @@ jobs:
             fi
             echo "OK: chemavx/${image}:${TAG} verified in registry"
           }
-          if [ "${{ steps.changes.outputs.build_bot }}" = "true" ]; then
-            check_image polymarket-bot
-          fi
-          if [ "${{ steps.changes.outputs.build_api }}" = "true" ]; then
-            check_image polymarket-bot-api
-          fi
-          if [ "${{ steps.changes.outputs.build_dashboard }}" = "true" ]; then
-            check_image polymarket-bot-dashboard
-          fi
+          check_image polymarket-bot
+          check_image polymarket-bot-api
+          check_image polymarket-bot-dashboard
 
       - name: Update k8s manifests
-        if: steps.changes.outputs.build_any == 'true'
         run: |
           pip3 install pyyaml -q
 
@@ -174,21 +114,12 @@ jobs:
           git clone ${{ env.K8S_MANIFESTS_REPO }} /tmp/k8s-manifests
           cd /tmp/k8s-manifests
 
-          # Only bump the tag of images that were actually rebuilt: the others
-          # keep their current (still existing) tag in the registry.
-          if [ "${{ steps.changes.outputs.build_bot }}" = "true" ]; then
-            sed -i "s|image: .*polymarket-bot[^-].*|image: git.chemavx.xyz/chemavx/polymarket-bot:${TAG}|g" \
-              polymarket-bot/deployment-bot.yaml \
-              polymarket-bot/cronjob-outcomes.yaml
-          fi
-          if [ "${{ steps.changes.outputs.build_api }}" = "true" ]; then
-            sed -i "s|image: .*polymarket-bot-api.*|image: git.chemavx.xyz/chemavx/polymarket-bot-api:${TAG}|g" \
-              polymarket-bot/deployment-api.yaml
-          fi
-          if [ "${{ steps.changes.outputs.build_dashboard }}" = "true" ]; then
-            sed -i "s|image: .*polymarket-bot-dashboard.*|image: git.chemavx.xyz/chemavx/polymarket-bot-dashboard:${TAG}|g" \
-              polymarket-bot/deployment-dashboard.yaml
-          fi
+          sed -i "s|image: .*polymarket-bot[^-].*|image: git.chemavx.xyz/chemavx/polymarket-bot:${TAG}|g" \
+            polymarket-bot/deployment-bot.yaml
+          sed -i "s|image: .*polymarket-bot-api.*|image: git.chemavx.xyz/chemavx/polymarket-bot-api:${TAG}|g" \
+            polymarket-bot/deployment-api.yaml
+          sed -i "s|image: .*polymarket-bot-dashboard.*|image: git.chemavx.xyz/chemavx/polymarket-bot-dashboard:${TAG}|g" \
+            polymarket-bot/deployment-dashboard.yaml
           sed -i "s|imagePullPolicy: Never|imagePullPolicy: Always|g" \
             polymarket-bot/deployment-bot.yaml \
             polymarket-bot/deployment-api.yaml \
@@ -223,21 +154,10 @@ jobs:
           TAG: ${{ steps.tag.outputs.TAG }}
           JOB_STATUS: ${{ job.status }}
           TELEGRAM_TOKEN: ${{ secrets.TELEGRAM_BOT_TOKEN }}
-          BUILD_BOT: ${{ steps.changes.outputs.build_bot }}
-          BUILD_API: ${{ steps.changes.outputs.build_api }}
-          BUILD_DASH: ${{ steps.changes.outputs.build_dashboard }}
         run: |
           TAG="${TAG:-${GITHUB_SHA:0:8}}"
-          BUILT=""
-          [ "$BUILD_BOT" = "true" ]  && BUILT="${BUILT}bot "
-          [ "$BUILD_API" = "true" ]  && BUILT="${BUILT}api "
-          [ "$BUILD_DASH" = "true" ] && BUILT="${BUILT}dashboard "
           if [ "$JOB_STATUS" = "success" ]; then
-            if [ -n "$BUILT" ]; then
-              MSG="✅ Deploy polymarket-bot:${TAG} completado (imágenes: ${BUILT% })"
-            else
-              MSG="✅ CI polymarket-bot:${TAG} OK — sin cambios de imagen, nada que desplegar"
-            fi
+            MSG="✅ Deploy polymarket-bot:${TAG} completado"
           else
             MSG="❌ Deploy polymarket-bot:${TAG} fallido (status: ${JOB_STATUS})"
           fi

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.11-slim
+FROM python:3.14-slim
 
 WORKDIR /app
 

Dockerfile.api

@@ -1,4 +1,4 @@
-FROM python:3.11-slim
+FROM python:3.14-slim
 
 WORKDIR /app
 

README.md

@@ -1,35 +0,0 @@
-# polymarket-bot
-
-Bot de paper-trading para Polymarket con estrategia bayesiana, API FastAPI y
-dashboard React. Corre en k3s vía GitOps (Gitea Actions → registry → ArgoCD).
-
-## Componentes
-
-| Componente | Código | Imagen | CMD |
-|---|---|---|---|
-| bot | `bot/` | `polymarket-bot` | `python3 -m bot.main` |
-| api | `api/` (+ `bot/` como librería) | `polymarket-bot-api` | `uvicorn api.main:app` |
-| dashboard | `dashboard/` | `polymarket-bot-dashboard` | nginx estático |
-
-Dashboard: https://polymarket.chemavx.xyz
-
-## CI/CD
-
-`.gitea/workflows/ci.yml` construye **solo las imágenes cuyos ficheros
-cambiaron** en el push (diff contra `github.event.before`):
-
-- `bot/`, `api/`, `requirements.txt` → bot + api (ambas imágenes copian las
-  mismas fuentes Python; solo cambia el CMD)
-- `Dockerfile` → bot · `Dockerfile.api` → api · `dashboard/` → dashboard
-- `.gitea/workflows/ci.yml`, primer push o force-push → todas (fallback seguro)
-- `tests/`, docs → ninguna (la CI no construye ni despliega nada)
-
-Las imágenes se tagean con `${GITHUB_SHA::8}`; el CI actualiza solo los
-deployments reconstruidos en `k8s-manifests/polymarket-bot/` y ArgoCD
-sincroniza vía webhook en segundos.
-
-## Tests
-
-```bash
-python3 -m pytest tests/ -q
-```

api/main.py

@@ -11,12 +11,6 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
 from bot.data.db import Database
-from bot.executor.paper import cash_available
-from bot.metrics.sharpe import (
-    MIN_DAYS_OBSERVED,
-    MIN_RESOLVED_TRADES,
-    sharpe_with_gate,
-)
 
 # Phase 6 format (Phase 6+): values already in log-odds space.
 # "fg_lo=+0.1200 mom_lo=+0.0000 news_lo=+0.0000 mfld_lo=-0.7483 btc_dom_lo=+0.0000"
@@ -215,66 +209,6 @@ async def get_attribution():
     return {"attribution": attribution, "total_attributed_trades": total}
 
 
-@app.get("/api/metrics/manifold-matches")
-async def get_manifold_matches():
-    """Manifold match audit — version-split summary and recent match attempts.
-
-    summary.current_version — stats for the active matcher (MANIFOLD_MATCHER_VERSION):
-      version          — the matcher version string
-      total_accepted   — matches accepted (score >= 0.40, inversion unambiguous)
-      total_rejected   — matches rejected (low score or ambiguous inversion)
-      total_no_results — no Manifold market found or API error
-      avg_match_score  — mean Jaccard score for accepted matches
-      used_in_trade    — accepted matches that were actually executed
-    summary.all_time — accepted/rejected/no_results across every matcher version.
-    summary.legacy.accepted_without_outcome_type — pre-outcome-guard accepted
-      records that the current matcher would reject (not counted in current_version).
-    summary.trades_dominated_by_mfld — non-excluded accepted-match trades where
-      feat_mfld_lo is the largest signal (consistent with attribution/features,
-      which also exclude excluded_from_metrics trades).
-    summary.unique_markets — distinct-market coverage (per-market, not per-attempt):
-      evaluated      — DISTINCT poly_market_id in manifold_match_audit (all versions)
-      accepted       — DISTINCT poly_market_id accepted by the current matcher
-      coverage_rate  — accepted / evaluated (null when evaluated=0)
-
-    recent_matches: last 50 rows from manifold_match_audit, newest first, each
-      tagged with matcher_version.
-      used_in_trade=True only when status='accepted' AND a trade was actually executed.
-    """
-    data = await db.get_manifold_matches(limit=50)
-    for match in data["recent_matches"]:
-        ts = match.get("timestamp")
-        if ts is not None and hasattr(ts, "isoformat"):
-            match["timestamp"] = ts.isoformat()
-    return data
-
-
-@app.get("/api/metrics/manifold-coverage")
-async def get_manifold_coverage():
-    """Manifold coverage by semantic market category, counted by UNIQUE market.
-
-    Unlike the raw audit counters (which count per-attempt and are inflated by the
-    trading loop re-evaluating the same markets), this measures real coverage:
-    how many DISTINCT markets in each category the matcher found an accepted
-    Manifold counterpart for. Base table is manifold_match_audit filtered to the
-    current matcher (v3_outcome_guard); category is inferred from trade family_key
-    when available, otherwise from the question text.
-
-    coverage_by_category — one entry per category, ordered by unique_evaluated DESC:
-      category          — gubernatorial | mayoral | senate | primary-republican |
-                          primary-democrat | big-tech | geopolitics | other
-      unique_evaluated  — distinct markets audited in this category
-      unique_accepted   — distinct markets with at least one accepted match
-      unique_rejected   — distinct markets with at least one rejected match
-      unique_no_results — distinct markets with at least one no_results outcome
-      coverage_rate     — unique_accepted / unique_evaluated (null if evaluated=0)
-    summary — total_unique_evaluated, total_unique_accepted, overall_coverage_rate
-      (null if nothing evaluated), categories_with_coverage (categories with
-      unique_accepted > 0).
-    """
-    return await db.get_manifold_coverage_by_category()
-
-
 @app.get("/api/summary")
 async def get_summary():
     """Dashboard summary card data.
@@ -285,49 +219,28 @@ async def get_summary():
     PnL and performance metrics come from the latest metrics_daily snapshot,
     which is written by the bot every cycle via MetricsTracker.update_daily_summary().
     After Fix 3, that snapshot is also DB-computed — not dependent on pod restarts.
-    sharpe_ratio is the exception: it is recomputed live here from the daily
-    PnL-close series (same sharpe_with_gate the tracker uses), so the
-    explanation fields (sharpe_status, days_observed) always match the value.
     """
-    latest_metrics, counts, position_data, inverted, legacy_count, daily_closes = (
-        await asyncio.gather(
-            db.get_metrics_history(days=1),
-            db.compute_metrics_from_db(),
-            db.get_open_position_data(),
-            db.get_recently_closed_inverted(hours=24),
-            db.get_legacy_incomplete_count(),
-            db.get_daily_pnl_closes(),
-        )
+    latest_metrics, open_trades, all_trades, inverted, legacy_count = await asyncio.gather(
+        db.get_metrics_history(days=1),
+        db.get_recent_trades(limit=500, status="open"),
+        db.get_recent_trades(limit=500),
+        db.get_recently_closed_inverted(hours=24),
+        db.get_legacy_incomplete_count(),
     )
 
     latest = latest_metrics[0] if latest_metrics else {}
     paper_bankroll = float(os.getenv("PAPER_BANKROLL", "10000"))
-    total_trades = int(counts["total_trades"] or 0)
-    resolved_count = int(counts.get("resolved_count") or 0)
-    # Same source PaperExecutor.initialize() uses to reconstruct cash:
-    # total_net_cost_open = SUM(net_cost) over open trades, uncapped.
-    _, total_net_cost_open = position_data
-    total_deployed = total_net_cost_open
-
-    # Sharpe: computed live from the daily PnL curve (same function the
-    # tracker uses for the snapshot). None + status while the minimum-sample
-    # gate (>=30 days observed, >=10 resolved trades) is not met — a Sharpe
-    # over 1 resolved trade is statistically meaningless.
-    days_observed = len(daily_closes)
-    sharpe, sharpe_status = sharpe_with_gate(daily_closes, paper_bankroll, resolved_count)
-
-    win_rate = latest.get("win_rate")
-    calibration = latest.get("calibration_score")
+    total_deployed = sum(t.get("net_cost", 0) for t in open_trades)
 
     return {
         # ── Portfolio state (live from DB) ──────────────────────────────────
         "paper_mode":              os.getenv("PAPER_MODE", "true") == "true",
         "paper_bankroll":          paper_bankroll,
-        "total_trades":            total_trades,              # COUNT(*), uncapped
-        "open_trades_count":       int(counts["open_count"] or 0),    # COUNT(*), uncapped
-        "closed_trades_count":     int(counts["closed_count"] or 0),  # COUNT(*), uncapped
+        "total_trades":            len(all_trades),           # exact, from DB
+        "open_trades_count":       len(open_trades),          # exact, from DB
+        "closed_trades_count":     len(all_trades) - len(open_trades),  # exact
         "total_deployed":          total_deployed,            # exact, from DB
-        "cash_available":          cash_available(paper_bankroll, total_net_cost_open),
+        "cash_available":          max(0.0, paper_bankroll - total_deployed),  # exact
         "legacy_incomplete_count": legacy_count,              # exact, from DB
         "reentry_guard_blocks_24h": len(inverted),            # exact, from DB
 
@@ -335,41 +248,31 @@ async def get_summary():
         # unrealized_pnl_est: open positions, edge_net × net_cost − fee.
         #   Estimated — uses model signal, not live price. Source: open trades.
         # realized_pnl: closed positions with known resolution.
-        #   Exact — payout − net_cost per trade (net of fee), matches logs/Telegram.
+        #   Exact — computed from (resolution − entry_price) × shares.
         # total_pnl: sum of both.
         "unrealized_pnl_est": latest.get("unrealized_pnl_est") or 0,
         "realized_pnl":       latest.get("realized_pnl") or 0,
         "total_pnl":          latest.get("total_pnl") or 0,
 
-        # ── Performance metrics ──────────────────────────────────────────────
+        # ── Performance metrics (from latest metrics_daily snapshot) ─────────
         # win_rate: fraction of resolved closed trades where close_pnl > 0.
         #   null if fewer than 5 resolved trades. Source: closed+resolved trades.
-        # sharpe_ratio: annualized Sharpe of the daily total_pnl curve, computed
-        #   live from metrics_daily. null while the minimum-sample gate fails
-        #   (sharpe_status explains why). Source: bot/metrics/sharpe.py.
+        # sharpe_ratio: 0.0 — requires daily-return time series (not yet tracked).
         # calibration_score: 1 − Brier score on resolved trades (higher = better).
         #   null if fewer than 10 resolved trades. Source: closed+resolved trades.
-        "win_rate":           win_rate,                       # null if < 5 resolved
-        "sharpe_ratio":       sharpe,                         # null if gate fails
-        "sharpe_status":      sharpe_status,  # ok | insufficient_sample | zero_variance
-        "days_observed":      days_observed,
-        "min_days_required":  MIN_DAYS_OBSERVED,
-        "min_resolved_required": MIN_RESOLVED_TRADES,
-        "calibration_score":  calibration,                    # null if < 10 resolved
+        "win_rate":           latest.get("win_rate"),         # null if < 5 resolved
+        "sharpe_ratio":       latest.get("sharpe_ratio") or 0,  # 0.0 until tracked
+        "calibration_score":  latest.get("calibration_score"),  # null if < 10 resolved
 
-        # ── Counters (live from DB) ──────────────────────────────────────────
-        "resolved_count":     resolved_count,
+        # ── Counters from snapshot ───────────────────────────────────────────
+        "resolved_count":     latest.get("resolved_count") or 0,
 
         # ── Promotion gate ───────────────────────────────────────────────────
-        # Never promote on a tiny sample: requires the resolved/days minimums
-        # AND non-null metrics AND all thresholds. A single lucky resolved
-        # trade must not flip this to true.
+        # All thresholds must pass; null metrics count as not-ready.
         "promotion_ready": (
-            resolved_count >= MIN_RESOLVED_TRADES
-            and days_observed >= MIN_DAYS_OBSERVED
-            and win_rate is not None and win_rate >= 0.52
-            and calibration is not None and calibration >= 0.7
-            and sharpe is not None and sharpe >= 0.5
-            and total_trades >= 50
+            (latest.get("sharpe_ratio") or 0) >= 0.5
+            and (latest.get("win_rate") or 0) >= 0.52
+            and (latest.get("calibration_score") or 0) >= 0.7
+            and len(all_trades) >= 50
         ),
     }

bot/data/db.py

@@ -4,8 +4,6 @@ import os
 from typing import Optional
 import asyncpg
 
-from bot.data.manifold import MANIFOLD_MATCHER_VERSION
-
 log = logging.getLogger(__name__)
 
 
@@ -38,15 +36,11 @@ class Database:
                     entry_price, shares, fee_usdc, net_cost, timestamp, reasoning, paper,
                     edge_gross, edge_net, prior_prob, final_prob,
                     mid_price, spread_estimate, commission, family_key,
-                    feat_fg_lo, feat_mom_lo, feat_news_lo, feat_mfld_lo, feat_btc_dom_lo,
-                    mfld_market_id, mfld_market_title, mfld_market_url,
-                    mfld_prob_raw, mfld_prob_final, mfld_inverted,
-                    mfld_match_score, mfld_match_reason, mfld_match_status
+                    feat_fg_lo, feat_mom_lo, feat_news_lo, feat_mfld_lo, feat_btc_dom_lo
                 ) VALUES (
                     $1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12,
                     $13,$14,$15,$16,$17,$18,$19,$20,
-                    $21,$22,$23,$24,$25,
-                    $26,$27,$28,$29,$30,$31,$32,$33,$34
+                    $21,$22,$23,$24,$25
                 )
                 ON CONFLICT (id) DO NOTHING
             """,
@@ -59,10 +53,6 @@ class Database:
                 # Phase 6 feature log-odds
                 trade.feat_fg_lo, trade.feat_mom_lo, trade.feat_news_lo,
                 trade.feat_mfld_lo, trade.feat_btc_dom_lo,
-                # Manifold audit fields
-                trade.mfld_market_id, trade.mfld_market_title, trade.mfld_market_url,
-                trade.mfld_prob_raw, trade.mfld_prob_final, trade.mfld_inverted,
-                trade.mfld_match_score, trade.mfld_match_reason, trade.mfld_match_status,
             )
 
     async def save_daily_metrics(self, metrics: dict) -> None:
@@ -152,20 +142,6 @@ class Database:
             """)
             return [dict(r) for r in rows]
 
-    async def get_open_trades_for_market(self, market_id: str) -> list[dict]:
-        """Return direction, shares and net_cost for each open trade in a market.
-
-        Used by PaperExecutor.close_position() to compute the settlement
-        payout per direction (BUY_NO pays out when resolution = 0.0).
-        """
-        async with self._pool.acquire() as conn:
-            rows = await conn.fetch(
-                "SELECT direction, shares, net_cost FROM trades "
-                "WHERE market_id = $1 AND closed_at IS NULL",
-                market_id,
-            )
-            return [dict(r) for r in rows]
-
     async def close_paper_position(
         self, market_id: str, reason: str = "", resolution: Optional[float] = None
     ) -> None:
@@ -173,29 +149,19 @@ class Database:
 
         resolution: 1.0 if YES resolved, 0.0 if NO resolved, None if unknown
         (legacy closes, inversion fixes). When resolution is provided, close_pnl
-        is computed in SQL per row as payout − net_cost — NET of fee, the single
-        PnL definition shared with PaperExecutor.close_position() (logs/Telegram):
-          BUY_YES: resolution * shares − net_cost
-          BUY_NO:  (1 − resolution) * shares − net_cost
-        paper.py aggregates payout − net_cost over these same open rows, so
-        SUM(close_pnl) per market equals the pnl it reports exactly. The
-        aggregate is intentionally NOT passed in as a parameter: writing it to
-        every row would double-count markets with more than one open trade.
+        is computed in SQL so it matches the stored entry_price and shares exactly.
         """
         async with self._pool.acquire() as conn:
-            # $3 is cast on every use: Postgres cannot infer the parameter type
-            # from a bare "$3 IS NOT NULL" and fails the prepare with
-            # AmbiguousParameterError otherwise.
             await conn.execute("""
                 UPDATE trades
                 SET closed_at    = NOW(),
                     close_reason = $2,
-                    resolution   = $3::double precision,
+                    resolution   = $3,
                     close_pnl    = CASE
-                        WHEN $3::double precision IS NOT NULL AND direction = 'BUY_YES'
-                            THEN ($3::double precision * shares) - net_cost
-                        WHEN $3::double precision IS NOT NULL AND direction = 'BUY_NO'
-                            THEN ((1.0 - $3::double precision) * shares) - net_cost
+                        WHEN $3 IS NOT NULL AND direction = 'BUY_YES'
+                            THEN ($3::double precision - entry_price) * shares
+                        WHEN $3 IS NOT NULL AND direction = 'BUY_NO'
+                            THEN ((1.0 - $3::double precision) - entry_price) * shares
                         ELSE NULL
                     END
                 WHERE market_id = $1 AND closed_at IS NULL
@@ -252,14 +218,8 @@ class Database:
                   COUNT(*)                                                AS total_trades,
                   COUNT(*) FILTER (WHERE closed_at IS NULL)              AS open_count,
                   COUNT(*) FILTER (WHERE closed_at IS NOT NULL)          AS closed_count,
-                  -- excluded_from_metrics trades are omitted from resolved_count,
-                  -- realized_pnl, wins_realized, and calibration_score.
-                  -- resolved_count does NOT require final_prob: legacy trades
-                  -- without signal data still count as resolved. Calibration
-                  -- below keeps the final_prob requirement (it needs the
-                  -- estimated probability to score).
                   COUNT(*) FILTER (WHERE resolution IS NOT NULL
-                                     AND (excluded_from_metrics IS NOT TRUE)) AS resolved_count,
+                                     AND final_prob IS NOT NULL)         AS resolved_count,
 
                   COALESCE(SUM(net_cost)
                     FILTER (WHERE closed_at IS NULL), 0)                 AS total_deployed,
@@ -272,17 +232,15 @@ class Database:
                     FILTER (WHERE closed_at IS NULL
                               AND edge_net IS NOT NULL), 0)              AS unrealized_pnl_est,
 
-                  -- Realized PnL: admin-excluded trades omitted (close_pnl=0 by convention
-                  -- but excluded explicitly so they don't skew the aggregate).
+                  -- Realized PnL: closed trades with a known resolution.
+                  -- close_pnl is computed at close time from actual resolution.
                   COALESCE(SUM(close_pnl)
                     FILTER (WHERE closed_at IS NOT NULL
-                              AND close_pnl IS NOT NULL
-                              AND (excluded_from_metrics IS NOT TRUE)), 0) AS realized_pnl,
+                              AND close_pnl IS NOT NULL), 0)             AS realized_pnl,
 
                   COUNT(*) FILTER (WHERE closed_at IS NOT NULL
                                      AND close_pnl IS NOT NULL
-                                     AND close_pnl > 0
-                                     AND (excluded_from_metrics IS NOT TRUE)) AS wins_realized,
+                                     AND close_pnl > 0)                  AS wins_realized,
 
                   -- Calibration (Brier score transformed to higher-is-better):
                   -- 1 − AVG((final_prob − resolution)²) on resolved trades.
@@ -290,15 +248,12 @@ class Database:
                   -- resolution is 1.0 (YES won) or 0.0 (NO won).
                   -- Perfect calibration → 1.0 | Random → ~0.75 | Worst → 0.0
                   -- Returns NULL if fewer than 10 resolved trades with final_prob.
-                  -- Admin-excluded trades omitted from both threshold and average.
                   CASE
                     WHEN COUNT(*) FILTER (WHERE resolution IS NOT NULL
-                                            AND final_prob IS NOT NULL
-                                            AND (excluded_from_metrics IS NOT TRUE)) >= 10
+                                            AND final_prob IS NOT NULL) >= 10
                     THEN 1.0 - AVG((final_prob - resolution) * (final_prob - resolution))
                                FILTER (WHERE resolution IS NOT NULL
-                                        AND final_prob IS NOT NULL
-                                        AND (excluded_from_metrics IS NOT TRUE))
+                                        AND final_prob IS NOT NULL)
                     ELSE NULL
                   END                                                     AS calibration_score
 
@@ -330,42 +285,12 @@ class Database:
             return result
 
     async def get_metrics_history(self, days: int = 42) -> list[dict]:
-        """Return the closing snapshot of each UTC day, newest day first.
-
-        metrics_daily receives one snapshot per trading cycle (~1/min), so a
-        plain LIMIT over raw rows would cover minutes, not days. DISTINCT ON
-        collapses each calendar day to its last snapshot, making `days` bound
-        actual days. history[0] remains the most recent snapshot overall.
-        """
         async with self._pool.acquire() as conn:
             rows = await conn.fetch(
-                """
-                SELECT DISTINCT ON (timestamp::date) *
-                FROM metrics_daily
-                ORDER BY timestamp::date DESC, timestamp DESC
-                LIMIT $1
-                """, days
+                "SELECT * FROM metrics_daily ORDER BY timestamp DESC LIMIT $1", days
             )
             return [dict(r) for r in rows]
 
-    async def get_daily_pnl_closes(self) -> list[float]:
-        """Return the closing total_pnl of every observed UTC day, oldest first.
-
-        One value per calendar day with at least one metrics_daily snapshot
-        (the day's last snapshot, same collapse rule as get_metrics_history).
-        This is the input series for the Sharpe ratio: len() = days observed,
-        consecutive deltas = daily PnL changes.
-        """
-        async with self._pool.acquire() as conn:
-            rows = await conn.fetch(
-                """
-                SELECT DISTINCT ON (timestamp::date) total_pnl
-                FROM metrics_daily
-                ORDER BY timestamp::date ASC, timestamp DESC
-                """
-            )
-            return [float(r["total_pnl"] or 0) for r in rows]
-
     async def backfill_feature_columns(self) -> int:
         """Back-populate feat_*_lo for trades created before Phase 6.
 
@@ -435,27 +360,22 @@ class Database:
                          feat_fg_lo AS fval,
                          edge_net, net_cost, fee_usdc, closed_at, close_pnl
                   FROM trades WHERE feat_fg_lo IS NOT NULL
-                    AND (excluded_from_metrics IS NOT TRUE)
                   UNION ALL
                   SELECT 'mom', 0.05, feat_mom_lo,
                          edge_net, net_cost, fee_usdc, closed_at, close_pnl
                   FROM trades WHERE feat_mom_lo IS NOT NULL
-                    AND (excluded_from_metrics IS NOT TRUE)
                   UNION ALL
                   SELECT 'news', 0.10, feat_news_lo,
                          edge_net, net_cost, fee_usdc, closed_at, close_pnl
                   FROM trades WHERE feat_news_lo IS NOT NULL
-                    AND (excluded_from_metrics IS NOT TRUE)
                   UNION ALL
                   SELECT 'mfld', 0.10, feat_mfld_lo,
                          edge_net, net_cost, fee_usdc, closed_at, close_pnl
                   FROM trades WHERE feat_mfld_lo IS NOT NULL
-                    AND (excluded_from_metrics IS NOT TRUE)
                   UNION ALL
                   SELECT 'btc_dom', 0.05, feat_btc_dom_lo,
                          edge_net, net_cost, fee_usdc, closed_at, close_pnl
                   FROM trades WHERE feat_btc_dom_lo IS NOT NULL
-                    AND (excluded_from_metrics IS NOT TRUE)
                 )
                 SELECT
                   feature,
@@ -539,7 +459,6 @@ class Database:
                     ) AS dominant
                   FROM trades
                   WHERE feat_fg_lo IS NOT NULL
-                    AND (excluded_from_metrics IS NOT TRUE)
                 )
                 SELECT
                   COALESCE(dominant, 'none')                                          AS dominant_feature,
@@ -574,530 +493,6 @@ class Database:
         return result
 
 
-    async def save_manifold_audit(
-        self,
-        audit_id: str,
-        poly_market_id: str,
-        poly_question: str,
-        search_query: str,
-        mfld_market_id: Optional[str],
-        mfld_market_title: Optional[str],
-        mfld_market_url: Optional[str],
-        prob_raw: Optional[float],
-        prob_final: Optional[float],
-        inverted: bool,
-        match_score: Optional[float],
-        match_reason: Optional[str],
-        match_status: str,
-        poly_outcome_type: Optional[str] = None,
-        mfld_outcome_type: Optional[str] = None,
-        matcher_version: Optional[str] = None,
-    ) -> None:
-        async with self._pool.acquire() as conn:
-            await conn.execute("""
-                INSERT INTO manifold_match_audit (
-                    id, poly_market_id, poly_question, search_query,
-                    mfld_market_id, mfld_market_title, mfld_market_url,
-                    prob_raw, prob_final, inverted,
-                    match_score, match_reason, match_status, used_in_trade,
-                    poly_outcome_type, mfld_outcome_type, matcher_version
-                ) VALUES ($1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12,$13,FALSE,$14,$15,$16)
-            """,
-                audit_id, poly_market_id, poly_question, search_query,
-                mfld_market_id, mfld_market_title, mfld_market_url,
-                prob_raw, prob_final, inverted,
-                match_score, match_reason, match_status,
-                poly_outcome_type, mfld_outcome_type, matcher_version,
-            )
-
-    async def get_manifold_cooldown(self, poly_market_id: str) -> Optional[dict]:
-        """Return the cooldown row for a market, or None if no cooldown is recorded.
-
-        The caller decides whether the cooldown is still active by comparing
-        now() against retry_after (see bayesian.evaluate()).
-        """
-        async with self._pool.acquire() as conn:
-            row = await conn.fetchrow(
-                "SELECT poly_market_id, last_evaluated_at, last_status, "
-                "retry_after, cooldown_reason FROM manifold_eval_cooldown "
-                "WHERE poly_market_id = $1",
-                poly_market_id,
-            )
-        return dict(row) if row else None
-
-    async def upsert_manifold_cooldown(
-        self,
-        poly_market_id: str,
-        last_status: str,
-        retry_after,
-        cooldown_reason: Optional[str] = None,
-    ) -> None:
-        """Insert or refresh the cooldown for a market after a real evaluation.
-
-        last_evaluated_at is stamped server-side with now(); retry_after is the
-        caller-computed earliest re-evaluation time.
-        """
-        async with self._pool.acquire() as conn:
-            await conn.execute("""
-                INSERT INTO manifold_eval_cooldown (
-                    poly_market_id, last_evaluated_at, last_status,
-                    retry_after, cooldown_reason
-                ) VALUES ($1, now(), $2, $3, $4)
-                ON CONFLICT (poly_market_id) DO UPDATE SET
-                    last_evaluated_at = now(),
-                    last_status       = EXCLUDED.last_status,
-                    retry_after       = EXCLUDED.retry_after,
-                    cooldown_reason   = EXCLUDED.cooldown_reason
-            """, poly_market_id, last_status, retry_after, cooldown_reason)
-
-    # ── Replay R0: snapshot recorder ─────────────────────────────────────────
-
-    async def save_ext_snapshot(self, cycle_ts, ext) -> None:
-        """Persist the ExternalSignals snapshot for one cycle (Replay R0)."""
-        async with self._pool.acquire() as conn:
-            await conn.execute("""
-                INSERT INTO ext_snapshots (
-                    cycle_ts, btc_price, btc_change_24h, eth_price, eth_change_24h,
-                    btc_dominance, fear_greed_index, fear_greed_label,
-                    total_market_cap_change, valid
-                ) VALUES ($1,$2,$3,$4,$5,$6,$7,$8,$9,$10)
-                ON CONFLICT (cycle_ts) DO NOTHING
-            """,
-                cycle_ts, ext.btc_price, ext.btc_change_24h,
-                ext.eth_price, ext.eth_change_24h, ext.btc_dominance,
-                ext.fear_greed_index, ext.fear_greed_label,
-                ext.total_market_cap_change, ext.valid,
-            )
-
-    async def upsert_markets(self, markets: list) -> None:
-        """Refresh market metadata (Replay R0) — replay rebuilds Market from here."""
-        rows = [
-            (m.id, m.condition_id, m.question, m.category, m.end_date, m.active)
-            for m in markets
-        ]
-        async with self._pool.acquire() as conn:
-            await conn.executemany("""
-                INSERT INTO markets (id, condition_id, question, category, end_date, active, last_seen)
-                VALUES ($1,$2,$3,$4,$5,$6, now())
-                ON CONFLICT (id) DO UPDATE SET
-                    condition_id = EXCLUDED.condition_id,
-                    question     = EXCLUDED.question,
-                    category     = EXCLUDED.category,
-                    end_date     = EXCLUDED.end_date,
-                    active       = EXCLUDED.active,
-                    last_seen    = now()
-            """, rows)
-
-    async def save_signal_records(self, cycle_ts, records: list[dict]) -> None:
-        """Batch-insert one cycle's decision records into signals (Replay R0)."""
-        if not records:
-            return
-        rows = [
-            (
-                r["market_id"], cycle_ts, cycle_ts,
-                r["polymarket_price"], r["category"], r["volume_24h"],
-                r["skip_reason"], r["family_key"],
-                r["prior_prob"], r["estimated_prob"], r["raw_final_prob"],
-                r["edge_gross"], r["edge_net"], r["regime_min_edge"],
-                r["days_to_resolution"], r["confidence"], r["direction"],
-                r["passed_gross"], r["passed_net"],
-                r["news_sentiment"], r["news_budget_skipped"],
-                r["guardrail_applied"], r["guardrail_changed_decision"],
-                r["feat_fg_lo"], r["feat_mom_lo"], r["feat_news_lo"],
-                r["feat_mfld_lo"], r["feat_btc_dom_lo"],
-                r["edge_gross"],  # legacy `edge` column mirrors edge_gross
-                r["acted_on"],
-            )
-            for r in records
-        ]
-        async with self._pool.acquire() as conn:
-            await conn.executemany("""
-                INSERT INTO signals (
-                    market_id, timestamp, cycle_ts,
-                    polymarket_price, category, volume_24h,
-                    skip_reason, family_key,
-                    prior_prob, estimated_prob, raw_final_prob,
-                    edge_gross, edge_net, regime_min_edge,
-                    days_to_resolution, confidence, direction,
-                    passed_gross, passed_net,
-                    news_sentiment, news_budget_skipped,
-                    guardrail_applied, guardrail_changed_decision,
-                    feat_fg_lo, feat_mom_lo, feat_news_lo,
-                    feat_mfld_lo, feat_btc_dom_lo,
-                    edge, acted_on
-                ) VALUES (
-                    $1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12,$13,$14,$15,
-                    $16,$17,$18,$19,$20,$21,$22,$23,$24,$25,$26,$27,$28,$29,$30
-                )
-            """, rows)
-
-    async def prune_signal_records(self, retention_days: int) -> int:
-        """Delete archive rows older than retention_days; returns rows deleted."""
-        async with self._pool.acquire() as conn:
-            result = await conn.execute(
-                "DELETE FROM signals WHERE timestamp < now() - ($1 || ' days')::interval",
-                str(retention_days),
-            )
-            await conn.execute(
-                "DELETE FROM ext_snapshots WHERE cycle_ts < now() - ($1 || ' days')::interval",
-                str(retention_days),
-            )
-        try:
-            return int(result.split()[-1])
-        except (ValueError, IndexError):
-            return 0
-
-    # ── Replay R1: replay core ───────────────────────────────────────────────
-
-    async def get_replay_cycles(self, from_ts, to_ts) -> list:
-        """Return the cycle_ts values with archived decisions in [from_ts, to_ts)."""
-        async with self._pool.acquire() as conn:
-            rows = await conn.fetch("""
-                SELECT DISTINCT cycle_ts FROM signals
-                WHERE cycle_ts >= $1 AND cycle_ts < $2
-                ORDER BY cycle_ts
-            """, from_ts, to_ts)
-        return [r["cycle_ts"] for r in rows]
-
-    async def get_ext_snapshot(self, cycle_ts) -> Optional[dict]:
-        """Return one cycle's ExternalSignals snapshot, or None if missing."""
-        async with self._pool.acquire() as conn:
-            row = await conn.fetchrow(
-                "SELECT * FROM ext_snapshots WHERE cycle_ts = $1", cycle_ts
-            )
-        return dict(row) if row else None
-
-    async def get_cycle_signal_rows(self, cycle_ts) -> list[dict]:
-        """Return one cycle's archived decision rows in original evaluation
-        order (id = insertion order = the order main.py evaluated them)."""
-        async with self._pool.acquire() as conn:
-            rows = await conn.fetch(
-                "SELECT * FROM signals WHERE cycle_ts = $1 ORDER BY id", cycle_ts
-            )
-        return [dict(r) for r in rows]
-
-    async def get_markets_by_ids(self, market_ids: list[str]) -> dict[str, dict]:
-        """Return market metadata rows keyed by id (for Market reconstruction)."""
-        if not market_ids:
-            return {}
-        async with self._pool.acquire() as conn:
-            rows = await conn.fetch(
-                "SELECT * FROM markets WHERE id = ANY($1::text[])", market_ids
-            )
-        return {r["id"]: dict(r) for r in rows}
-
-    async def save_replay_run(self, run: dict) -> None:
-        async with self._pool.acquire() as conn:
-            await conn.execute("""
-                INSERT INTO replay_runs (
-                    run_id, git_sha, config_hash, config_json,
-                    from_ts, to_ts, cycles, decisions, matched, mismatched, note
-                ) VALUES ($1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11)
-            """,
-                run["run_id"], run["git_sha"], run["config_hash"],
-                run["config_json"], run["from_ts"], run["to_ts"],
-                run["cycles"], run["decisions"], run["matched"],
-                run["mismatched"], run["note"],
-            )
-
-    async def save_replay_decisions(self, run_id: str, decisions: list[dict]) -> None:
-        if not decisions:
-            return
-        rows = [
-            (
-                run_id, d["cycle_ts"], d["market_id"],
-                d["skip_reason"], d["prior_prob"], d["estimated_prob"],
-                d["raw_final_prob"], d["edge_gross"], d["edge_net"],
-                d["regime_min_edge"], d["days_to_resolution"],
-                d["confidence"], d["direction"], d["would_trade"],
-                d["recorded_skip_reason"], d["matched"], d["mismatch_field"],
-            )
-            for d in decisions
-        ]
-        async with self._pool.acquire() as conn:
-            await conn.executemany("""
-                INSERT INTO replay_decisions (
-                    run_id, cycle_ts, market_id,
-                    skip_reason, prior_prob, estimated_prob,
-                    raw_final_prob, edge_gross, edge_net,
-                    regime_min_edge, days_to_resolution,
-                    confidence, direction, would_trade,
-                    recorded_skip_reason, matched, mismatch_field
-                ) VALUES ($1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12,$13,$14,$15,$16,$17)
-            """, rows)
-
-    # ── Replay R2: outcomes + calibration metrics ────────────────────────────
-
-    async def get_unresolved_archived_market_ids(self) -> list[str]:
-        """Archived markets (present in signals) with no stored outcome yet."""
-        async with self._pool.acquire() as conn:
-            rows = await conn.fetch("""
-                SELECT DISTINCT s.market_id FROM signals s
-                LEFT JOIN market_outcomes o ON o.market_id = s.market_id
-                WHERE o.market_id IS NULL
-                ORDER BY s.market_id
-            """)
-        return [r["market_id"] for r in rows]
-
-    async def upsert_market_outcome(
-        self, market_id: str, outcome: float, resolved_at
-    ) -> None:
-        async with self._pool.acquire() as conn:
-            await conn.execute("""
-                INSERT INTO market_outcomes (market_id, outcome, resolved_at)
-                VALUES ($1, $2, $3)
-                ON CONFLICT (market_id) DO UPDATE
-                SET outcome = EXCLUDED.outcome,
-                    resolved_at = EXCLUDED.resolved_at,
-                    fetched_at = NOW()
-            """, market_id, outcome, resolved_at)
-
-    async def get_outcome_coverage(self) -> dict:
-        """How much of the archive is scorable: resolved vs archived markets."""
-        async with self._pool.acquire() as conn:
-            row = await conn.fetchrow("""
-                SELECT
-                    (SELECT COUNT(DISTINCT market_id) FROM signals)  AS archived,
-                    (SELECT COUNT(*) FROM market_outcomes
-                     WHERE market_id IN (SELECT DISTINCT market_id FROM signals)
-                    ) AS resolved
-            """)
-        return dict(row)
-
-    async def get_calibration_rows(self, run_id: Optional[str] = None) -> list[dict]:
-        """Every archived evaluation with a full estimate AND a known outcome.
-
-        run_id None scores the R0 archive (signals); a run_id scores that
-        replay run's re-estimates instead (counterfactual calibration).
-        Rows without estimated_prob (skipped before estimation: prior_extreme,
-        unsupported, family, no_signals) carry no model prediction to score.
-        """
-        async with self._pool.acquire() as conn:
-            if run_id is None:
-                rows = await conn.fetch("""
-                    SELECT s.market_id, s.category,
-                           s.estimated_prob, s.prior_prob, o.outcome
-                    FROM signals s
-                    JOIN market_outcomes o ON o.market_id = s.market_id
-                    WHERE s.estimated_prob IS NOT NULL
-                      AND s.prior_prob IS NOT NULL
-                """)
-            else:
-                rows = await conn.fetch("""
-                    SELECT d.market_id, m.category,
-                           d.estimated_prob, d.prior_prob, o.outcome
-                    FROM replay_decisions d
-                    JOIN market_outcomes o ON o.market_id = d.market_id
-                    LEFT JOIN markets m ON m.id = d.market_id
-                    WHERE d.run_id = $1
-                      AND d.estimated_prob IS NOT NULL
-                      AND d.prior_prob IS NOT NULL
-                """, run_id)
-        return [dict(r) for r in rows]
-
-    async def mark_manifold_audit_used(self, audit_id: str) -> None:
-        async with self._pool.acquire() as conn:
-            await conn.execute(
-                "UPDATE manifold_match_audit SET used_in_trade = TRUE WHERE id = $1",
-                audit_id,
-            )
-
-    async def get_manifold_matches(self, limit: int = 50) -> dict:
-        """Manifold match audit, with summary split by matcher version.
-
-        The summary separates the current matcher (MANIFOLD_MATCHER_VERSION) from
-        all-time totals and from legacy pre-outcome-guard records, whose accepted
-        matches would now be rejected by the outcome-compatibility guard and so
-        must not be conflated with current-version stats.
-        """
-        async with self._pool.acquire() as conn:
-            current = await conn.fetchrow("""
-                SELECT
-                    COUNT(*) FILTER (WHERE match_status = 'accepted')   AS total_accepted,
-                    COUNT(*) FILTER (WHERE match_status = 'rejected')   AS total_rejected,
-                    COUNT(*) FILTER (WHERE match_status = 'no_results') AS total_no_results,
-                    AVG(match_score) FILTER (WHERE match_status = 'accepted') AS avg_match_score,
-                    COUNT(*) FILTER (WHERE used_in_trade = TRUE)        AS used_in_trade
-                FROM manifold_match_audit
-                WHERE matcher_version = $1
-            """, MANIFOLD_MATCHER_VERSION)
-            all_time = await conn.fetchrow("""
-                SELECT
-                    COUNT(*) FILTER (WHERE match_status = 'accepted')   AS total_accepted,
-                    COUNT(*) FILTER (WHERE match_status = 'rejected')   AS total_rejected,
-                    COUNT(*) FILTER (WHERE match_status = 'no_results') AS total_no_results
-                FROM manifold_match_audit
-            """)
-            legacy = await conn.fetchrow("""
-                SELECT COUNT(*) AS accepted_without_outcome_type
-                FROM manifold_match_audit
-                WHERE matcher_version = 'legacy_pre_outcome_guard'
-                  AND match_status = 'accepted'
-            """)
-            unique_markets = await conn.fetchrow("""
-                SELECT
-                    COUNT(DISTINCT poly_market_id) AS evaluated,
-                    COUNT(DISTINCT poly_market_id) FILTER (
-                        WHERE match_status = 'accepted'
-                          AND matcher_version = $1
-                    ) AS accepted
-                FROM manifold_match_audit
-            """, MANIFOLD_MATCHER_VERSION)
-            mfld_dominated = await conn.fetchrow("""
-                SELECT COUNT(*) AS cnt FROM trades
-                WHERE (excluded_from_metrics IS NOT TRUE)
-                  AND mfld_match_status = 'accepted'
-                  AND feat_mfld_lo IS NOT NULL
-                  AND ABS(feat_mfld_lo) > 0.0001
-                  AND ABS(feat_mfld_lo) > ABS(COALESCE(feat_fg_lo, 0))
-                  AND ABS(feat_mfld_lo) > ABS(COALESCE(feat_mom_lo, 0))
-                  AND ABS(feat_mfld_lo) > ABS(COALESCE(feat_news_lo, 0))
-                  AND ABS(feat_mfld_lo) > ABS(COALESCE(feat_btc_dom_lo, 0))
-            """)
-            rows = await conn.fetch(
-                "SELECT * FROM manifold_match_audit ORDER BY timestamp DESC LIMIT $1",
-                limit,
-            )
-        return {
-            "summary": {
-                "current_version": {
-                    "version":          MANIFOLD_MATCHER_VERSION,
-                    "total_accepted":   int(current["total_accepted"] or 0),
-                    "total_rejected":   int(current["total_rejected"] or 0),
-                    "total_no_results": int(current["total_no_results"] or 0),
-                    "avg_match_score":  _f(current["avg_match_score"]),
-                    "used_in_trade":    int(current["used_in_trade"] or 0),
-                },
-                "all_time": {
-                    "total_accepted":   int(all_time["total_accepted"] or 0),
-                    "total_rejected":   int(all_time["total_rejected"] or 0),
-                    "total_no_results": int(all_time["total_no_results"] or 0),
-                },
-                "legacy": {
-                    "accepted_without_outcome_type":
-                        int(legacy["accepted_without_outcome_type"] or 0),
-                },
-                "trades_dominated_by_mfld": int(mfld_dominated["cnt"] or 0),
-                "unique_markets": {
-                    "evaluated": int(unique_markets["evaluated"] or 0),
-                    "accepted":  int(unique_markets["accepted"] or 0),
-                    "coverage_rate": (
-                        float(unique_markets["accepted"]) / float(unique_markets["evaluated"])
-                        if unique_markets["evaluated"] else None
-                    ),
-                },
-            },
-            "recent_matches": [dict(r) for r in rows],
-        }
-
-    async def get_manifold_coverage_by_category(self) -> dict:
-        """Manifold coverage by semantic market category, counted by UNIQUE market.
-
-        Base table is manifold_match_audit filtered to the current matcher
-        (v3_outcome_guard).  Each poly_market_id is collapsed to one row first, so
-        a market is counted once regardless of how many audit attempts or trades it
-        has — this measures coverage, not retry volume.
-
-        Category is inferred from the market's trade family_key when available, else
-        from poly_question (LEFT JOIN: audited markets that never produced a trade
-        are kept).  Buckets (accepted/rejected/no_results) are not mutually exclusive
-        at the market level — a market that was no_results then later rejected counts
-        in both — matching COUNT(DISTINCT CASE WHEN status=...) semantics.
-        """
-        async with self._pool.acquire() as conn:
-            rows = await conn.fetch("""
-                WITH audit AS (
-                    SELECT
-                        poly_market_id,
-                        MAX(poly_question)                   AS poly_question,
-                        bool_or(match_status = 'accepted')   AS has_accepted,
-                        bool_or(match_status = 'rejected')   AS has_rejected,
-                        bool_or(match_status = 'no_results') AS has_no_results
-                    FROM manifold_match_audit
-                    WHERE matcher_version = 'v3_outcome_guard'
-                    GROUP BY poly_market_id
-                ),
-                fam AS (
-                    SELECT market_id, MAX(family_key) AS family_key
-                    FROM trades
-                    GROUP BY market_id
-                ),
-                categorized AS (
-                    SELECT
-                        a.has_accepted, a.has_rejected, a.has_no_results,
-                        CASE
-                            WHEN f.family_key ILIKE '%gubernatorial%' THEN 'gubernatorial'
-                            WHEN f.family_key ILIKE '%mayoral%'       THEN 'mayoral'
-                            WHEN f.family_key ILIKE '%senate%'        THEN 'senate'
-                            WHEN f.family_key ILIKE '%republican%'    THEN 'primary-republican'
-                            WHEN f.family_key ILIKE '%democrat%'      THEN 'primary-democrat'
-                            WHEN f.family_key ILIKE '%openai%'
-                              OR f.family_key ILIKE '%nvidia%'
-                              OR f.family_key ILIKE '%anthropic%'     THEN 'big-tech'
-                            -- family_key NULL or unmatched → infer from question
-                            WHEN a.poly_question ILIKE '%governor%'
-                              OR a.poly_question ILIKE '%gubernatorial%' THEN 'gubernatorial'
-                            WHEN a.poly_question ILIKE '%mayor%'
-                              OR a.poly_question ILIKE '%mayoral%'       THEN 'mayoral'
-                            WHEN a.poly_question ILIKE '%senate%'        THEN 'senate'
-                            WHEN a.poly_question ILIKE '%republican primary%' THEN 'primary-republican'
-                            WHEN a.poly_question ILIKE '%democratic primary%'
-                              OR a.poly_question ILIKE '%democrat primary%'   THEN 'primary-democrat'
-                            WHEN a.poly_question ILIKE '%openai%'
-                              OR a.poly_question ILIKE '%nvidia%'
-                              OR a.poly_question ILIKE '%anthropic%'     THEN 'big-tech'
-                            WHEN a.poly_question ILIKE '%russia%'
-                              OR a.poly_question ILIKE '%ukraine%'
-                              OR a.poly_question ILIKE '%israel%'
-                              OR a.poly_question ILIKE '%ceasefire%'
-                              OR a.poly_question ILIKE '%military%'      THEN 'geopolitics'
-                            ELSE 'other'
-                        END AS category
-                    FROM audit a
-                    LEFT JOIN fam f ON f.market_id = a.poly_market_id
-                )
-                SELECT
-                    category,
-                    COUNT(*)                              AS unique_evaluated,
-                    COUNT(*) FILTER (WHERE has_accepted)   AS unique_accepted,
-                    COUNT(*) FILTER (WHERE has_rejected)   AS unique_rejected,
-                    COUNT(*) FILTER (WHERE has_no_results) AS unique_no_results
-                FROM categorized
-                GROUP BY category
-                ORDER BY unique_evaluated DESC
-            """)
-
-        coverage_by_category = []
-        total_evaluated = 0
-        total_accepted = 0
-        categories_with_coverage = 0
-        for r in rows:
-            evaluated = int(r["unique_evaluated"] or 0)
-            accepted = int(r["unique_accepted"] or 0)
-            total_evaluated += evaluated
-            total_accepted += accepted
-            if accepted > 0:
-                categories_with_coverage += 1
-            coverage_by_category.append({
-                "category":          r["category"],
-                "unique_evaluated":  evaluated,
-                "unique_accepted":   accepted,
-                "unique_rejected":   int(r["unique_rejected"] or 0),
-                "unique_no_results": int(r["unique_no_results"] or 0),
-                "coverage_rate":     (accepted / evaluated) if evaluated else None,
-            })
-
-        return {
-            "coverage_by_category": coverage_by_category,
-            "summary": {
-                "total_unique_evaluated":  total_evaluated,
-                "total_unique_accepted":   total_accepted,
-                "overall_coverage_rate":   (total_accepted / total_evaluated) if total_evaluated else None,
-                "categories_with_coverage": categories_with_coverage,
-            },
-        }
-
-
 def _f(v) -> Optional[float]:
     """None-safe float cast for asyncpg Decimal/None values."""
     return float(v) if v is not None else None

bot/data/manifold.py

@@ -2,49 +2,34 @@
 Manifold Markets client — cross-platform prediction market probability signals.
 
 For each Polymarket question, searches Manifold for a matching binary market
-by keyword overlap and returns a ManifoldMatchResult with full audit metadata.
+by keyword overlap and returns its probability as a calibration signal.
 
-Match threshold: >= 0.40 Jaccard overlap (raised from 0.25 for stricter semantics).
+Inversion guard: if the Manifold market's winning side (Republican / Democrat)
+is the complement of the Polymarket question's winning side, the probability is
+automatically inverted (1 - prob).  This prevents "Democrats win Ohio governor"
+from consuming the probability of a Manifold market titled "Republicans win Ohio
+governor" without adjustment.
 
-Outcome compatibility guard (conservative):
-  - Conditional Manifold markets ("If X, will Y?" / "Conditional on..." / "Assuming..."
-    / "Given that..." / mid-sentence "...if X is nominated, will...") are rejected:
-    a premise-gated question is not equivalent to a direct outcome question even when
-    token overlap is high. reason='conditional_market'.
-  - Each side is classified into an outcome_type (nomination | primary_win |
-    general_win | conditional | other). Matches with differing outcome_type — or any
-    conditional side — are rejected. reason='outcome_mismatch: poly=... manifold=...'.
+Rejection guard: if the match score falls below _MATCH_THRESHOLD the market is
+rejected, even if inversion would otherwise apply.  All decisions are logged at
+INFO so they can be audited per-cycle.
 
-Inversion guard (conservative):
-  - If Polymarket question names a party (democrat/republican) AND the matched
-    Manifold market names the OPPOSITE party → invert probability (1 - prob).
-  - If Polymarket question names a party AND Manifold market has NO party keyword
-    → reject with reason='ambiguous_inversion' (can't determine if inversion applies).
-  - All other cases: no inversion, accept if score >= threshold.
-  - Ante duda, reject.
-
-Cache TTL: 30 minutes.
+Cache TTL: 30 minutes (Manifold markets move slowly vs our 60 s cycle).
+Match threshold: >= 0.25 keyword overlap ratio between significant tokens.
 """
 import logging
 import re
 import time
-from dataclasses import dataclass, field
 from typing import Optional
 
 import httpx
 
-# Version tag for every audit record this matcher produces. Persisted to
-# manifold_match_audit.matcher_version so metrics can isolate current-version
-# stats from legacy/pre-versioning records. Do NOT change this value once set;
-# bump to a new string only when matcher semantics change materially.
-MANIFOLD_MATCHER_VERSION = "v3_outcome_guard"
-
 MANIFOLD_API = "https://api.manifold.markets/v0"
 CACHE_TTL_SEC = 1800  # 30 minutes
 
 log = logging.getLogger(__name__)
 
-_MATCH_THRESHOLD = 0.40  # raised from 0.25
+_MATCH_THRESHOLD = 0.25
 
 _STOP_WORDS = frozenset([
     "will", "the", "a", "an", "is", "are", "was", "were", "be", "been",
@@ -58,24 +43,9 @@ _STOP_WORDS = frozenset([
     "before", "during", "until", "against", "between", "through",
 ])
 
+# Mutually exclusive political parties used for complement detection
 _REPUBLICAN_WORDS = frozenset(["republican", "republicans", "gop"])
-_DEMOCRAT_WORDS   = frozenset(["democrat", "democrats", "democratic"])
-
-
-@dataclass
-class ManifoldMatchResult:
-    status: str                        # 'accepted' | 'rejected' | 'no_results'
-    prob_final: Optional[float] = None
-    prob_raw:   Optional[float] = None
-    market_id:    Optional[str] = None  # Manifold internal market ID
-    market_title: Optional[str] = None
-    market_url:   Optional[str] = None
-    match_score:  Optional[float] = None  # 0-1 Jaccard
-    match_reason: Optional[str] = None   # human-readable explanation
-    inverted: bool = False
-    search_query: str = ""
-    poly_outcome_type: Optional[str] = None  # nomination|primary_win|general_win|conditional|other
-    mfld_outcome_type: Optional[str] = None
+_DEMOCRAT_WORDS = frozenset(["democrat", "democrats", "democratic"])
 
 
 def _significant_words(text: str) -> set[str]:
@@ -99,53 +69,27 @@ def _detect_party(text: str) -> Optional[str]:
     return None
 
 
-# ── Conditional-market detection (Task 1) ──────────────────────────────────────
-# A market is "conditional" when its resolution is gated on a premise rather than
-# asking the outcome directly (e.g. "If X is the nominee, will he win?"). Such a
-# market is NOT equivalent to a direct outcome question even with high token overlap.
-_CONDITIONAL_PREFIXES = ("if ", "conditional on", "assuming ", "given that")
-# " if <clause>," — a mid-sentence conditional clause closed by a comma.
-_CONDITIONAL_CLAUSE_RE = re.compile(r"\sif\s[^,]*,")
-
-
-def _is_conditional(text: str) -> bool:
-    """True if the question is phrased conditionally (premise-gated)."""
-    t = (text or "").strip().lower()
-    if t.startswith(_CONDITIONAL_PREFIXES):
-        return True
-    return bool(_CONDITIONAL_CLAUSE_RE.search(t))
-
-
-def _classify_outcome(text: str) -> str:
+def _best_match_with_audit(
+    poly_question: str,
+    results: list[dict],
+) -> tuple[Optional[dict], float, bool]:
     """
-    Coarse classification of what a question is *asking about*, used to reject
-    matches whose outcomes are not equivalent even when tokens overlap.
+    Find the best-matching open binary Manifold market.
 
-    Returns one of: nomination | primary_win | general_win | conditional | other.
-    Order matters: conditional is checked first (premise-gated), then nomination
-    (which subsumes "primary nominee"), then primary, then general election.
+    Returns (match, score, needs_inversion):
+      match            — best result dict, or None if below threshold
+      score            — keyword overlap score of best candidate (even if rejected)
+      needs_inversion  — True when Manifold market favours the OPPOSITE party/side
+                         to the Polymarket question (probability should be 1 - prob)
     """
-    t = (text or "").strip().lower()
-    if t.startswith(_CONDITIONAL_PREFIXES):
-        return "conditional"
-    if any(k in t for k in ("nominee", "nominated", "nomination")):
-        return "nomination"
-    if any(k in t for k in ("primary", "win the primary", "first round")):
-        return "primary_win"
-    if any(k in t for k in ("win the election", "win the race",
-                            "win the seat", "general election")):
-        return "general_win"
-    return "other"
-
-
-def _find_best_candidate(poly_question: str, results: list[dict]) -> tuple[Optional[dict], float]:
-    """Find the highest-scoring open binary Manifold market by Jaccard overlap."""
     poly_words = _significant_words(poly_question)
+    poly_party = _detect_party(poly_question)
     if not poly_words:
-        return None, 0.0
+        return None, 0.0, False
 
     best_score = 0.0
     best: Optional[dict] = None
+    best_needs_inv = False
 
     for result in results:
         if result.get("outcomeType") != "BINARY":
@@ -162,14 +106,18 @@ def _find_best_candidate(poly_question: str, results: list[dict]) -> tuple[Optio
         if score > best_score:
             best_score = score
             best = result
+            manifold_party = _detect_party(title)
+            # Inversion is warranted only when both sides are unambiguously detected
+            # and they are confirmed opposites (republican ≠ democrat).
+            best_needs_inv = (
+                poly_party is not None
+                and manifold_party is not None
+                and poly_party != manifold_party
+            )
 
-    return best, best_score
-
-
-def _market_url(match: dict) -> Optional[str]:
-    slug    = match.get("slug", "")
-    creator = match.get("creatorUsername", "")
-    return f"https://manifold.markets/{creator}/{slug}" if slug else None
+    if best_score >= _MATCH_THRESHOLD and best is not None:
+        return best, best_score, best_needs_inv
+    return None, best_score, False
 
 
 class ManifoldClient:
@@ -177,32 +125,27 @@ class ManifoldClient:
 
     def __init__(self) -> None:
         self._client = httpx.AsyncClient(timeout=15)
-        # question → (fetched_at_monotonic, ManifoldMatchResult)
-        self._cache: dict[str, tuple[float, ManifoldMatchResult]] = {}
+        # question → (fetched_at_monotonic, probability_or_None)
+        self._cache: dict[str, tuple[float, Optional[float]]] = {}
 
-    async def get_match(self, question: str) -> ManifoldMatchResult:
+    async def get_probability(self, question: str) -> Optional[float]:
         """
-        Return a ManifoldMatchResult for the given Polymarket question.
+        Return Manifold probability for a matching market, or None.
 
-        status='accepted'   → prob_final is set and ready to use as signal
-        status='rejected'   → match found but failed quality/inversion check
-        status='no_results' → API returned no results or call failed
+        Probability is already adjusted for party-direction inversion when
+        the matched Manifold market is the complement of our question.
+
+        Full audit log is emitted at INFO for every resolved query.
         """
         now = time.monotonic()
         cached = self._cache.get(question)
         if cached and (now - cached[0]) < CACHE_TTL_SEC:
             return cached[1]
 
-        poly_outcome = _classify_outcome(question)
-
         query = _build_search_query(question)
         if not query:
-            result = ManifoldMatchResult(
-                status="no_results", search_query="",
-                poly_outcome_type=poly_outcome,
-            )
-            self._cache[question] = (now, result)
-            return result
+            self._cache[question] = (now, None)
+            return None
 
         try:
             resp = await self._client.get(
@@ -211,140 +154,45 @@ class ManifoldClient:
             )
             resp.raise_for_status()
             results = resp.json()
-        except Exception as exc:
-            log.warning("Manifold API error for %r: %s", question[:40], exc)
-            result = ManifoldMatchResult(
-                status="no_results", search_query=query,
-                poly_outcome_type=poly_outcome,
-            )
-            self._cache[question] = (now, result)
-            return result
+        except Exception as e:
+            log.warning("Manifold API error for %r: %s", question[:40], e)
+            self._cache[question] = (now, None)
+            return None
 
-        if not results:
-            result = ManifoldMatchResult(
-                status="no_results", search_query=query,
-                poly_outcome_type=poly_outcome,
-            )
-            self._cache[question] = (now, result)
-            return result
+        match, score, needs_inv = _best_match_with_audit(question, results)
 
-        best, score = _find_best_candidate(question, results)
-
-        # ── Score threshold ───────────────────────────────────────────────────
-        if best is None or score < _MATCH_THRESHOLD:
-            reason = f"jaccard={score:.2f}<{_MATCH_THRESHOLD:.2f}"
+        if match is None:
             log.info(
-                "Manifold REJECTED  %-50s | score=%.2f < threshold=%.2f | query=%r",
+                "Manifold no_match: %-50s | best_score=%.2f < %.2f | query=%r",
                 question[:50], score, _MATCH_THRESHOLD, query,
             )
-            result = ManifoldMatchResult(
-                status="rejected",
-                market_title=best.get("question") if best else None,
-                match_score=score if best else None,
-                match_reason=reason,
-                search_query=query,
-                poly_outcome_type=poly_outcome,
-                mfld_outcome_type=_classify_outcome(best.get("question", "")) if best else None,
-            )
-            self._cache[question] = (now, result)
-            return result
+            self._cache[question] = (now, None)
+            return None
 
-        # ── Outcome compatibility + inversion analysis (conservative) ─────────
-        mfld_title     = best.get("question", "")
-        mfld_outcome   = _classify_outcome(mfld_title)
-        poly_party     = _detect_party(question)
-        manifold_party = _detect_party(mfld_title)
+        prob_raw = float(match["probability"])
+        prob_final = (1.0 - prob_raw) if needs_inv else prob_raw
 
-        poly_words    = _significant_words(question)
-        mfld_words    = _significant_words(mfld_title)
-        matched_tokens = sorted(poly_words & mfld_words)[:6]
-
-        inverted = False
-        rejection_reason: Optional[str] = None
-
-        # Task 1 — conditional Manifold market is never equivalent to a direct
-        # outcome question, regardless of token overlap.
-        if _is_conditional(mfld_title):
-            rejection_reason = "conditional_market: manifold question is conditional"
-        # Task 2 — outcome types must match; any conditional side is rejected.
-        elif (poly_outcome == "conditional" or mfld_outcome == "conditional"
-              or poly_outcome != mfld_outcome):
-            rejection_reason = (
-                f"outcome_mismatch: poly={poly_outcome} manifold={mfld_outcome}"
-            )
-        elif poly_party is not None:
-            if manifold_party is None:
-                # Poly specifies a party; Manifold does not → can't verify inversion safety
-                rejection_reason = (
-                    f"ambiguous_inversion: poly_party={poly_party}, mfld_party=none"
-                )
-            elif manifold_party != poly_party:
-                # Clear opposite parties — apply inversion
-                inverted = True
-            # manifold_party == poly_party → same party, no inversion needed
-
-        if rejection_reason is not None:
-            url = _market_url(best)
-            log.info(
-                "Manifold REJECTED  %-50s | score=%.2f | reason=%s\n"
-                "  mfld_title: %s",
-                question[:50], score, rejection_reason, best.get("question", "")[:70],
-            )
-            result = ManifoldMatchResult(
-                status="rejected",
-                market_id=str(best.get("id", "")) or None,
-                market_title=best.get("question"),
-                market_url=url,
-                match_score=score,
-                match_reason=(
-                    f"jaccard={score:.2f}, tokens={matched_tokens}, {rejection_reason}"
-                ),
-                search_query=query,
-                poly_outcome_type=poly_outcome,
-                mfld_outcome_type=mfld_outcome,
-            )
-            self._cache[question] = (now, result)
-            return result
-
-        # ── Accepted ──────────────────────────────────────────────────────────
-        prob_raw   = float(best["probability"])
-        prob_final = (1.0 - prob_raw) if inverted else prob_raw
-        url        = _market_url(best)
-
-        match_reason = f"jaccard={score:.2f}, tokens={matched_tokens}"
-        if inverted:
-            match_reason += f", inverted=party({poly_party}≠{manifold_party})"
+        # Build market URL from slug (best-effort; may be missing)
+        slug = match.get("slug", "")
+        creator = match.get("creatorUsername", "")
+        url = f"https://manifold.markets/{creator}/{slug}" if slug else "n/a"
 
         log.info(
-            "Manifold %s %-50s\n"
-            "  poly: %s\n"
-            "  mfld: %s\n"
-            "  url:  %s\n"
-            "  score=%.2f | raw=%.3f | inverted=%s | final=%.3f",
-            "ACCEPTED_INVERTED" if inverted else "ACCEPTED  ",
+            "Manifold %s: %-50s\n"
+            "  poly_question: %s\n"
+            "  manifold_title: %s\n"
+            "  manifold_url:   %s\n"
+            "  match_score: %.2f | prob_raw=%.3f | inverted=%s | prob_final=%.3f",
+            "MATCH_INVERTED" if needs_inv else "MATCH",
             question[:50],
             question,
-            best.get("question", ""),
-            url or "n/a",
-            score, prob_raw, inverted, prob_final,
+            match.get("question", ""),
+            url,
+            score, prob_raw, needs_inv, prob_final,
         )
 
-        result = ManifoldMatchResult(
-            status="accepted",
-            prob_final=prob_final,
-            prob_raw=prob_raw,
-            market_id=str(best.get("id", "")) or None,
-            market_title=best.get("question"),
-            market_url=url,
-            match_score=score,
-            match_reason=match_reason,
-            inverted=inverted,
-            search_query=query,
-            poly_outcome_type=poly_outcome,
-            mfld_outcome_type=mfld_outcome,
-        )
-        self._cache[question] = (now, result)
-        return result
+        self._cache[question] = (now, prob_final)
+        return prob_final
 
     async def close(self) -> None:
         await self._client.aclose()

bot/data/news.py

@@ -51,11 +51,7 @@ _DATE_RE = re.compile(
     r"|\bQ[1-4]\b",
     flags=re.IGNORECASE,
 )
-# Hyphens/dashes are GNews query operators (a leading '-' means "exclude the
-# next term"), so a token like "El-Sayed" makes the API return HTTP 400.  Strip
-# them to spaces along with the rest of the punctuation so the query stays a
-# plain keyword list.  – = en dash, — = em dash.
-_PUNCT_RE = re.compile(r"[?!\"'.,;:()\[\]{}\-–—]")
+_PUNCT_RE = re.compile(r"[?!\"'.,;:()\[\]{}]")
 
 
 class NewsClient:
@@ -83,18 +79,6 @@ class NewsClient:
     # Public API
     # ------------------------------------------------------------------
 
-    @property
-    def enabled(self) -> bool:
-        """True only when a GNews API key is configured.
-
-        When False, get_sentiment() is a no-op that returns 0.0 without any
-        network call, so callers must skip GNews entirely — including the
-        per-cycle query budget accounting — instead of "spending" a query that
-        never reaches the API (which inflated gnews_queries_used to a phantom
-        5/5 while the key was missing).
-        """
-        return bool(self._api_key)
-
     async def get_sentiment(self, question: str) -> float:
         """
         Return a sentiment score ∈ [-1.0, +1.0] for the market question.

bot/data/polymarket.py

@@ -211,32 +211,6 @@ class Market:
     category: str = ""
 
 
-@dataclass
-class MarketResolution:
-    """Resolution state of a market, from Gamma API.
-
-    resolution is the final YES outcome price: 1.0 = YES won, 0.0 = NO won.
-    resolved is True only when the outcome is definitive — a market that is
-    closed but still in UMA dispute/proposal reports resolved=False.
-    """
-    resolved: bool
-    resolution: Optional[float] = None
-    resolved_at: Optional[datetime] = None
-
-
-def _parse_resolution_timestamp(raw: Optional[str]) -> Optional[datetime]:
-    """Parse Gamma timestamps: '2026-06-11 13:15:01+00' or '2026-06-11T13:15:01Z'."""
-    if not raw:
-        return None
-    try:
-        dt = datetime.fromisoformat(raw.replace("Z", "+00:00"))
-        if dt.tzinfo is None:
-            dt = dt.replace(tzinfo=timezone.utc)
-        return dt
-    except (ValueError, TypeError):
-        return None
-
-
 @dataclass
 class OrderBook:
     market_id: str
@@ -473,74 +447,6 @@ class PolymarketClient:
         )
         return markets
 
-    async def get_market_resolution(self, market_id: str) -> Optional[MarketResolution]:
-        """Fetch resolution state for a market by Gamma market id.
-
-        Observed Gamma API behaviour (GET /markets/{id}):
-          open market     → closed=false, umaResolutionStatus absent
-          resolved market → closed=true, umaResolutionStatus="resolved",
-                            outcomePrices='["0", "1"]' (final YES price = outcome)
-          unknown id      → HTTP 404
-
-        Returns None on API errors (caller retries next check). A closed market
-        whose outcome prices are not degenerate (0/1) or whose UMA status is not
-        "resolved" yet (proposed/disputed) reports resolved=False — we never
-        settle a position on an ambiguous outcome.
-        """
-        try:
-            resp = await self._client.get(f"{GAMMA_API}/markets/{market_id}")
-            if resp.status_code == 404:
-                log.warning("get_market_resolution: market %s not found (404)", market_id)
-                return None
-            resp.raise_for_status()
-            m = resp.json()
-        except httpx.HTTPError as e:
-            log.warning("get_market_resolution: API error for %s: %s", market_id, e)
-            return None
-
-        if not m.get("closed"):
-            return MarketResolution(resolved=False)
-
-        uma_status = (m.get("umaResolutionStatus") or "").lower()
-        if uma_status and uma_status != "resolved":
-            # Closed but UMA outcome still proposed/disputed — wait for finality
-            return MarketResolution(resolved=False)
-
-        raw_prices = m.get("outcomePrices", [])
-        if isinstance(raw_prices, str):
-            import json as _json
-            try:
-                raw_prices = _json.loads(raw_prices)
-            except ValueError:
-                raw_prices = []
-        try:
-            yes_final = float(raw_prices[0])
-        except (IndexError, TypeError, ValueError):
-            log.warning(
-                "get_market_resolution: market %s closed but outcomePrices "
-                "unparseable: %r", market_id, m.get("outcomePrices"),
-            )
-            return MarketResolution(resolved=False)
-
-        if yes_final >= 0.99:
-            resolution = 1.0
-        elif yes_final <= 0.01:
-            resolution = 0.0
-        else:
-            # Closed but prices not settled at 0/1 (partial / ambiguous outcome)
-            log.warning(
-                "get_market_resolution: market %s closed with non-binary final "
-                "price %.3f — not settling", market_id, yes_final,
-            )
-            return MarketResolution(resolved=False)
-
-        resolved_at = (
-            _parse_resolution_timestamp(m.get("closedTime"))
-            or _parse_resolution_timestamp(m.get("umaEndDate"))
-            or _parse_resolution_timestamp(m.get("endDate"))
-        )
-        return MarketResolution(resolved=True, resolution=resolution, resolved_at=resolved_at)
-
     async def get_order_book(self, token_id: str) -> Optional[OrderBook]:
         """Get order book for a specific token."""
         try:

bot/data/schema.sql

@@ -113,10 +113,9 @@ CREATE INDEX IF NOT EXISTS idx_trades_closed ON trades(closed_at) WHERE closed_a
 -- Fix 3: market resolution and realized P&L per trade
 --
 -- resolution: 1.0 if YES resolved, 0.0 if NO resolved, NULL if not yet settled.
--- close_pnl:  realized P&L in USDC at close time — NET of fee (payout − net_cost),
---   the same definition PaperExecutor.close_position() reports in logs/Telegram.
---   BUY_YES: resolution * shares - net_cost
---   BUY_NO:  (1 - resolution) * shares - net_cost
+-- close_pnl:  realized P&L in USDC at close time.
+--   BUY_YES: (resolution - entry_price) * shares
+--   BUY_NO:  ((1 - resolution) - entry_price) * shares
 --   NULL if closed without a known resolution (legacy closes, inversion fixes).
 -- ─────────────────────────────────────────────────────────────────────────────
 ALTER TABLE trades ADD COLUMN IF NOT EXISTS close_pnl  DOUBLE PRECISION;
@@ -169,103 +168,6 @@ ALTER TABLE trades ADD COLUMN IF NOT EXISTS feat_btc_dom_lo DOUBLE PRECISION;
 CREATE INDEX IF NOT EXISTS idx_trades_feat_fg   ON trades(feat_fg_lo)  WHERE feat_fg_lo IS NOT NULL;
 CREATE INDEX IF NOT EXISTS idx_trades_feat_mfld ON trades(feat_mfld_lo) WHERE feat_mfld_lo IS NOT NULL;
 
--- ─────────────────────────────────────────────────────────────────────────────
--- Manifold match audit — per-trade columns in trades
---
--- Persisted for every trade where Manifold was queried (status='accepted').
--- mfld_match_status: 'accepted' | 'rejected' | 'no_results'
--- mfld_inverted: TRUE when prob_final = 1 - prob_raw (party complement match)
--- ─────────────────────────────────────────────────────────────────────────────
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_market_id    TEXT;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_market_title TEXT;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_market_url   TEXT;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_prob_raw     DOUBLE PRECISION;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_prob_final   DOUBLE PRECISION;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_inverted     BOOLEAN;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_match_score  DOUBLE PRECISION;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_match_reason TEXT;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS mfld_match_status TEXT;
-
--- ─────────────────────────────────────────────────────────────────────────────
--- Manifold match audit table — records every Manifold query attempt
---
--- Populated for ALL queries: accepted, rejected, and no_results.
--- used_in_trade=TRUE is set after executor confirms a trade was executed.
--- poly_market_id: Market.id from the Polymarket Market dataclass (never NULL).
--- ─────────────────────────────────────────────────────────────────────────────
-CREATE TABLE IF NOT EXISTS manifold_match_audit (
-    id                TEXT PRIMARY KEY,
-    timestamp         TIMESTAMPTZ DEFAULT NOW(),
-    poly_market_id    TEXT NOT NULL,
-    poly_question     TEXT NOT NULL,
-    search_query      TEXT,
-    mfld_market_id    TEXT,
-    mfld_market_title TEXT,
-    mfld_market_url   TEXT,
-    prob_raw          DOUBLE PRECISION,
-    prob_final        DOUBLE PRECISION,
-    inverted          BOOLEAN DEFAULT FALSE,
-    match_score       DOUBLE PRECISION,
-    match_reason      TEXT,
-    match_status      TEXT NOT NULL,
-    used_in_trade     BOOLEAN DEFAULT FALSE,
-    poly_outcome_type TEXT,
-    mfld_outcome_type TEXT
-);
-
-CREATE INDEX IF NOT EXISTS idx_mfld_audit_timestamp ON manifold_match_audit(timestamp DESC);
-CREATE INDEX IF NOT EXISTS idx_mfld_audit_status    ON manifold_match_audit(match_status);
-CREATE INDEX IF NOT EXISTS idx_mfld_audit_poly_mkt  ON manifold_match_audit(poly_market_id);
-
--- Backfill outcome-type columns on pre-existing tables (idempotent).
-ALTER TABLE manifold_match_audit ADD COLUMN IF NOT EXISTS poly_outcome_type TEXT;
-ALTER TABLE manifold_match_audit ADD COLUMN IF NOT EXISTS mfld_outcome_type TEXT;
-
--- ─────────────────────────────────────────────────────────────────────────────
--- Matcher versioning — separate current-matcher metrics from legacy records
---
--- matcher_version tags each audit row with the matcher that produced it
--- (MANIFOLD_MATCHER_VERSION in bot/data/manifold.py).  This lets the metrics
--- endpoint isolate current_version stats from pre-versioning records, whose
--- accepted matches would now be rejected by the outcome-compatibility guard.
---
--- Backfill is one-shot and idempotent (only touches NULL matcher_version rows):
---   * rows with no outcome types  → 'legacy_pre_outcome_guard' (pre outcome-guard;
---     accepted without any outcome-type validation)
---   * rows with an outcome type   → 'v2_outcome_guard_no_version' (existed between
---     the outcome-guard and this versioning; real version not persisted)
--- We tag rather than infer the exact version that wasn't recorded.
--- ─────────────────────────────────────────────────────────────────────────────
-ALTER TABLE manifold_match_audit ADD COLUMN IF NOT EXISTS matcher_version TEXT;
-
-UPDATE manifold_match_audit
-SET matcher_version = 'legacy_pre_outcome_guard'
-WHERE matcher_version IS NULL
-  AND poly_outcome_type IS NULL
-  AND mfld_outcome_type IS NULL;
-
-UPDATE manifold_match_audit
-SET matcher_version = 'v2_outcome_guard_no_version'
-WHERE matcher_version IS NULL
-  AND (poly_outcome_type IS NOT NULL OR mfld_outcome_type IS NOT NULL);
-
-CREATE INDEX IF NOT EXISTS idx_mfld_audit_version ON manifold_match_audit(matcher_version);
-
--- ─────────────────────────────────────────────────────────────────────────────
--- Metric exclusion — administrative closure flag
---
--- excluded_from_metrics: TRUE for trades closed for non-signal reasons
---   (bad matcher, data error, admin close).  These trades are excluded from
---   win_rate, calibration_score, realized_pnl, and feature attribution.
--- exclusion_reason: free-text label for the exclusion cause.
---   e.g. 'invalid_manifold_match_legacy'
--- ─────────────────────────────────────────────────────────────────────────────
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS excluded_from_metrics BOOLEAN DEFAULT FALSE;
-ALTER TABLE trades ADD COLUMN IF NOT EXISTS exclusion_reason TEXT;
-
-CREATE INDEX IF NOT EXISTS idx_trades_excluded ON trades(excluded_from_metrics)
-  WHERE excluded_from_metrics = TRUE;
-
 -- ─────────────────────────────────────────────────────────────────────────────
 -- Fix 3: extended metrics_daily columns for DB-computed metrics
 --
@@ -280,175 +182,3 @@ ALTER TABLE metrics_daily ADD COLUMN IF NOT EXISTS realized_pnl       DOUBLE PRE
 ALTER TABLE metrics_daily ADD COLUMN IF NOT EXISTS open_count         INTEGER;
 ALTER TABLE metrics_daily ADD COLUMN IF NOT EXISTS closed_count       INTEGER;
 ALTER TABLE metrics_daily ADD COLUMN IF NOT EXISTS resolved_count     INTEGER;
-
--- ─────────────────────────────────────────────────────────────────────────────
--- Checkpoint alerts — one-shot and rate-limited Telegram observation alerts
---
--- fired_at:      timestamp of the first fire (immutable for one-shot checkpoints)
--- last_fired_at: updated on every fire (used for rate-limiting repeatable alerts)
--- ─────────────────────────────────────────────────────────────────────────────
-CREATE TABLE IF NOT EXISTS checkpoint_alerts (
-    checkpoint_name TEXT PRIMARY KEY,
-    fired_at        TIMESTAMPTZ NOT NULL,
-    last_fired_at   TIMESTAMPTZ
-);
-
--- ─────────────────────────────────────────────────────────────────────────────
--- Manifold evaluation cooldown — per-market backoff for the Manifold matcher
---
--- The trading loop re-evaluates the same ~stable set of politics/tech markets
--- every cycle (~60s).  Most resolve to a stable terminal verdict (no Manifold
--- coverage, low-score, outcome mismatch, conditional market) that will not change
--- on the next cycle.  Re-querying them every minute floods manifold_match_audit
--- with redundant rows and makes the metrics uninterpretable.
---
--- This table records, per poly_market_id, when the market was last evaluated and
--- the earliest time it should be evaluated again (retry_after).  evaluate() in
--- bot/strategy/bayesian.py consults it BEFORE calling the matcher and skips the
--- call (and the audit write) entirely while now() < retry_after.
---
--- last_status / cooldown_reason are stored for observability only.
--- ─────────────────────────────────────────────────────────────────────────────
-CREATE TABLE IF NOT EXISTS manifold_eval_cooldown (
-    poly_market_id    TEXT PRIMARY KEY,
-    last_evaluated_at TIMESTAMPTZ NOT NULL,
-    last_status       TEXT NOT NULL,
-    retry_after       TIMESTAMPTZ NOT NULL,
-    cooldown_reason   TEXT
-);
-
-CREATE INDEX IF NOT EXISTS idx_mfld_cooldown_retry ON manifold_eval_cooldown(retry_after);
-
--- ─────────────────────────────────────────────────────────────────────────────
--- Replay R0: snapshot recorder — the archive the replay engine reads from
---
--- The signals table (Phase 2/5 schema) never had a writer; R0 makes it the
--- per-(market, cycle) decision archive.  One row per evaluated market per
--- cycle, carrying both the INPUTS the strategy saw (external signals, news
--- sentiment, per-feature log-odds) and the OUTPUTS it produced (probs, edges,
--- gates, skip_reason).  A replay run rebuilds Market/ExternalSignals from
--- these rows plus ext_snapshots and re-executes evaluate() deterministically.
---
--- cycle_ts groups all rows of one trading cycle and joins them to their
--- ext_snapshots row (same timestamp; no FK to keep writes independent).
--- days_to_resolution is persisted so replay does not depend on wall-clock.
--- news_budget_skipped distinguishes "GNews had nothing" from "GNews was not
--- asked this cycle" (5-query budget) — without it politics replay would treat
--- budget starvation as absence of news.
--- Retention: rows older than SIGNALS_RETENTION_DAYS (default 90) are pruned.
--- ─────────────────────────────────────────────────────────────────────────────
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS cycle_ts            TIMESTAMPTZ;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS category            TEXT;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS prior_prob          DOUBLE PRECISION;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS raw_final_prob      DOUBLE PRECISION;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS days_to_resolution  INTEGER;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS volume_24h          DOUBLE PRECISION;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS news_sentiment      DOUBLE PRECISION;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS news_budget_skipped BOOLEAN;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS guardrail_applied   BOOLEAN;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS guardrail_changed_decision BOOLEAN;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS feat_fg_lo          DOUBLE PRECISION;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS feat_mom_lo         DOUBLE PRECISION;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS feat_news_lo        DOUBLE PRECISION;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS feat_mfld_lo        DOUBLE PRECISION;
-ALTER TABLE signals ADD COLUMN IF NOT EXISTS feat_btc_dom_lo     DOUBLE PRECISION;
-
-CREATE INDEX IF NOT EXISTS idx_signals_cycle ON signals(cycle_ts);
-
--- One row per trading cycle: the ExternalSignals snapshot every market in
--- that cycle was evaluated against.  Written once per cycle before the
--- evaluation loop; signals rows join on cycle_ts.
-CREATE TABLE IF NOT EXISTS ext_snapshots (
-    cycle_ts                TIMESTAMPTZ PRIMARY KEY,
-    btc_price               DOUBLE PRECISION,
-    btc_change_24h          DOUBLE PRECISION,
-    eth_price               DOUBLE PRECISION,
-    eth_change_24h          DOUBLE PRECISION,
-    btc_dominance           DOUBLE PRECISION,
-    fear_greed_index        INTEGER,
-    fear_greed_label        TEXT,
-    total_market_cap_change DOUBLE PRECISION,
-    valid                   BOOLEAN
-);
-
--- ─────────────────────────────────────────────────────────────────────────────
--- Replay R1: replay core — re-execute evaluate() over the R0 archive
---
--- A replay run reads cycles from signals + ext_snapshots + markets, rebuilds
--- the exact inputs (including archived news_sentiment — GNews is never called),
--- re-runs BayesianStrategy.evaluate() with the archived cycle_ts as clock, and
--- writes one replay_decisions row per (cycle, market).
---
--- replay_runs tags every run with the code (git_sha) and strategy constants
--- (config_hash) that produced it: two runs over the same window with different
--- config_hash values are a counterfactual comparison; same config_hash against
--- the recorded rows is a determinism check (mismatches should be 0, modulo
--- day-boundary crossings between cycle_ts and the original wall-clock).
---
--- matched: replayed decision equals the recorded one (skip_reason, probs,
---   confidence, direction).  NULL when not comparable — e.g. reentry_guard
---   rows, recorded outside evaluate() with no decision fields to compare;
---   the replay still re-evaluates them, which is extra calibration data.
--- mismatch_field: first field that differed, for triage.
--- ─────────────────────────────────────────────────────────────────────────────
-CREATE TABLE IF NOT EXISTS replay_runs (
-    run_id      TEXT PRIMARY KEY,
-    created_at  TIMESTAMPTZ DEFAULT NOW(),
-    git_sha     TEXT,
-    config_hash TEXT,
-    config_json TEXT,
-    from_ts     TIMESTAMPTZ,
-    to_ts       TIMESTAMPTZ,
-    cycles      INTEGER,
-    decisions   INTEGER,
-    matched     INTEGER,
-    mismatched  INTEGER,
-    note        TEXT
-);
-
-CREATE TABLE IF NOT EXISTS replay_decisions (
-    id                   SERIAL PRIMARY KEY,
-    run_id               TEXT NOT NULL,
-    cycle_ts             TIMESTAMPTZ NOT NULL,
-    market_id            TEXT NOT NULL,
-    -- replayed outputs (same semantics as the signals columns)
-    skip_reason          TEXT,
-    prior_prob           DOUBLE PRECISION,
-    estimated_prob       DOUBLE PRECISION,
-    raw_final_prob       DOUBLE PRECISION,
-    edge_gross           DOUBLE PRECISION,
-    edge_net             DOUBLE PRECISION,
-    regime_min_edge      DOUBLE PRECISION,
-    days_to_resolution   INTEGER,
-    confidence           DOUBLE PRECISION,
-    direction            TEXT,
-    would_trade          BOOLEAN,
-    -- fidelity vs the recorded signals row
-    recorded_skip_reason TEXT,
-    matched              BOOLEAN,
-    mismatch_field       TEXT
-);
-
-CREATE INDEX IF NOT EXISTS idx_replay_decisions_run ON replay_decisions(run_id);
-CREATE INDEX IF NOT EXISTS idx_replay_decisions_mkt ON replay_decisions(market_id);
-
--- ─────────────────────────────────────────────────────────────────────────────
--- Replay R2: outcomes + calibration metrics
---
--- One row per resolved market, fetched from the Gamma API via
--- get_market_resolution() (UMA-final only: a market closed but still in
--- proposal/dispute is not stored).  outcome is the final YES price:
--- 1.0 = YES won, 0.0 = NO won.
---
--- Joining signals (or replay_decisions) to market_outcomes scores every
--- archived estimate against reality — Brier / log-loss of estimated_prob
--- benchmarked against the market price (prior_prob) on the same rows,
--- answering "does the model add value over the market?" across ALL
--- evaluations, not just executed trades.
--- ─────────────────────────────────────────────────────────────────────────────
-CREATE TABLE IF NOT EXISTS market_outcomes (
-    market_id   TEXT PRIMARY KEY,
-    outcome     DOUBLE PRECISION NOT NULL,
-    resolved_at TIMESTAMPTZ,
-    fetched_at  TIMESTAMPTZ DEFAULT NOW()
-);

bot/executor/paper.py

@@ -22,30 +22,6 @@ log = logging.getLogger(__name__)
 # NOTE: this is a heuristic — see COMMISSION_RATE in bayesian.py for context.
 POLYMARKET_FEE = 0.02  # 2%
 
-# Strong references to in-flight notification tasks.  The event loop only
-# keeps a weak reference to tasks created via create_task(), so without this
-# set a pending Telegram notification could be garbage-collected before it
-# runs.  Tasks remove themselves from the set on completion.
-_background_tasks: set[asyncio.Task] = set()
-
-
-def _notify_in_background(coro) -> None:
-    """Fire-and-forget a Telegram notification, keeping the task referenced."""
-    task = asyncio.create_task(coro)
-    _background_tasks.add(task)
-    task.add_done_callback(_background_tasks.discard)
-
-
-def cash_available(bankroll: float, total_net_cost_open: float) -> float:
-    """Cash left after the net cost (fees included) of all open positions.
-
-    Single source of truth for the cash figure, shared by
-    PaperExecutor.initialize() and the /api/summary endpoint so both always
-    report the same number for the same DB state.
-    total_net_cost_open comes from Database.get_open_position_data().
-    """
-    return max(0.0, bankroll - total_net_cost_open)
-
 
 @dataclass
 class Trade:
@@ -81,16 +57,6 @@ class Trade:
     feat_news_lo:    float = 0.0
     feat_mfld_lo:    float = 0.0
     feat_btc_dom_lo: float = 0.0
-    # ── Manifold match audit ──────────────────────────────────────────────────
-    mfld_market_id:   Optional[str]   = None
-    mfld_market_title: Optional[str]  = None
-    mfld_market_url:  Optional[str]   = None
-    mfld_prob_raw:    Optional[float] = None
-    mfld_prob_final:  Optional[float] = None
-    mfld_inverted:    bool            = False
-    mfld_match_score: Optional[float] = None
-    mfld_match_reason: Optional[str]  = None
-    mfld_match_status: Optional[str]  = None
 
     def __str__(self) -> str:
         return (
@@ -132,7 +98,7 @@ class PaperExecutor:
 
         positions_value = sum(positions_size.values())
         self._portfolio.positions = positions_size
-        self._portfolio.cash = cash_available(self._portfolio.cash, total_net_cost)
+        self._portfolio.cash = max(0.0, self._portfolio.cash - total_net_cost)
 
         total_value = self._portfolio.cash + positions_value
         exposure_pct = positions_value / total_value if total_value > 0 else 0.0
@@ -210,16 +176,6 @@ class PaperExecutor:
             feat_news_lo=order.feat_news_lo,
             feat_mfld_lo=order.feat_mfld_lo,
             feat_btc_dom_lo=order.feat_btc_dom_lo,
-            # Manifold audit
-            mfld_market_id=order.mfld_market_id,
-            mfld_market_title=order.mfld_market_title,
-            mfld_market_url=order.mfld_market_url,
-            mfld_prob_raw=order.mfld_prob_raw,
-            mfld_prob_final=order.mfld_prob_final,
-            mfld_inverted=order.mfld_inverted,
-            mfld_match_score=order.mfld_match_score,
-            mfld_match_reason=order.mfld_match_reason,
-            mfld_match_status=order.mfld_match_status,
         )
 
         # Update paper portfolio
@@ -229,7 +185,7 @@ class PaperExecutor:
         # Persist to DB
         await self._db.save_trade(trade)
 
-        _notify_in_background(
+        asyncio.create_task(
             telegram.trade_opened(trade.question, trade.direction, trade.size_usdc, trade.edge_net)
         )
 
@@ -250,7 +206,7 @@ class PaperExecutor:
             "LEGACY_CLOSE market=%s | returned $%.2f to cash | %s",
             market_id, cost, reason[:80],
         )
-        _notify_in_background(
+        asyncio.create_task(
             telegram.trade_legacy_closed(question or market_id, cost, reason)
         )
         return cost
@@ -259,53 +215,24 @@ class PaperExecutor:
         """Close a paper position after market resolution.
 
         resolution: 1.0 if YES won, 0.0 if NO won.
-        Settlement payout per trade:
-          BUY_YES: shares * resolution
-          BUY_NO:  shares * (1 - resolution)
-        pnl = payout - net_cost.
-        Persists resolution and close_pnl to DB. Returns realized P&L for
-        logging, or None if no position is open.
+        Persists resolution and close_pnl to DB (computed via SQL from stored
+        entry_price and shares). Returns approximate P&L for logging.
         """
         if market_id not in self._portfolio.positions:
             return None
 
-        position_cost = self._portfolio.positions[market_id]
-        open_trades = await self._db.get_open_trades_for_market(market_id)
+        position_cost = self._portfolio.positions.pop(market_id)
+        self._portfolio.cash += position_cost * resolution  # pay out winnings
 
-        if open_trades:
-            payout = sum(
-                float(t["shares"])
-                * (resolution if t["direction"] == "BUY_YES" else 1.0 - resolution)
-                for t in open_trades
-            )
-            net_cost = sum(float(t["net_cost"]) for t in open_trades)
-            pnl = payout - net_cost
-        else:
-            # In-memory position with no open DB trades: direction/shares are
-            # unknown, so settle at break-even instead of guessing the payout.
-            log.warning(
-                "close_position: no open DB trades for market %s — "
-                "settling at break-even", market_id,
-            )
-            payout = position_cost
-            pnl = 0.0
-
-        # Persist first, mutate memory after: if the DB write fails, the
-        # in-memory portfolio must keep the position so the next resolution
-        # check can retry the close.
         await self._db.close_paper_position(
             market_id,
-            reason="resolved",
+            reason=f"market_resolved resolution={resolution:.1f}",
             resolution=resolution,
         )
-
-        self._portfolio.positions.pop(market_id)
-        self._portfolio.cash += payout
-        log.info(
-            "Closed position in %s, resolution=%.1f payout=$%.2f pnl=%+.2f",
-            market_id, resolution, payout, pnl,
+        approx_pnl = position_cost * resolution - position_cost
+        log.info("Closed position in %s, resolution=%.1f", market_id, resolution)
+        asyncio.create_task(
+            telegram.trade_closed(question or market_id, approx_pnl)
         )
-        _notify_in_background(
-            telegram.trade_closed(question or market_id, pnl)
-        )
-        return pnl
+        # Approximate PnL: settlement value minus cost. Exact value is in close_pnl.
+        return approx_pnl

bot/main.py

@@ -11,89 +11,21 @@ from bot.data.polymarket import PolymarketClient, Market, market_family_key
 from bot.data.external import ExternalDataClient
 from bot.data.news import NewsClient
 from bot.data.manifold import ManifoldClient
-from bot.strategy.bayesian import (
-    BayesianStrategy,
-    gnews_priority,
-    MAX_NEWS_QUERIES_PER_CYCLE,
-    MANIFOLD_SIGNAL_ENABLED,
-)
+from bot.strategy.bayesian import BayesianStrategy, gnews_priority, MAX_NEWS_QUERIES_PER_CYCLE
 from bot.risk.manager import RiskManager
 from bot.executor.paper import PaperExecutor
 from bot.metrics.tracker import MetricsTracker
 from bot.data.db import Database
-from bot.notify.checkpoints import CheckpointMonitor
 
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
 )
-# httpx logs every request URL at INFO, and the GNews URL carries the API key as
-# a `?token=` query param — that would leak GNEWS_API_KEY in plaintext into the
-# pod logs.  Raise httpx/httpcore to WARNING so request URLs never reach INFO.
-# The bot's own GNews log lines only print the sanitised query, not the token.
-logging.getLogger("httpx").setLevel(logging.WARNING)
-logging.getLogger("httpcore").setLevel(logging.WARNING)
 log = logging.getLogger("bot.main")
 
 PAPER_MODE = os.getenv("PAPER_MODE", "true").lower() == "true"
 PAPER_BANKROLL = float(os.getenv("PAPER_BANKROLL", "10000"))
 
-# Check open positions for market resolution every N trading cycles (~N minutes
-# at the 60s cycle cadence). Keeps Gamma API load at ~1 request per open
-# position per 10 minutes.
-RESOLUTION_CHECK_INTERVAL = 10
-
-# Replay R0: persist per-(market, cycle) decision records + the ExternalSignals
-# snapshot each cycle, so the replay engine can re-run past decisions.  The
-# recorder must never break trading — every write is wrapped in try/except.
-SIGNAL_RECORDER_ENABLED = os.getenv("SIGNAL_RECORDER_ENABLED", "true").lower() == "true"
-SIGNALS_RETENTION_DAYS = int(os.getenv("SIGNALS_RETENTION_DAYS", "90"))
-# Prune the archive roughly once a day at the 60s cycle cadence.
-SIGNALS_PRUNE_INTERVAL_CYCLES = 1440
-
-
-async def check_resolutions(
-    poly: PolymarketClient,
-    executor: PaperExecutor,
-    db: Database,
-) -> None:
-    """Detect resolved markets and settle their open paper positions.
-
-    For each open position, asks the Gamma API whether the market resolved.
-    On a definitive resolution, PaperExecutor.close_position() settles the
-    payout, persists close_reason='resolved' + resolution + close_pnl, and
-    sends the Telegram notification.
-    """
-    positions = await db.get_open_position_details()
-    checked = 0
-    resolved = 0
-    for pos in positions:
-        market_id = str(pos["market_id"])
-        try:
-            res = await poly.get_market_resolution(market_id)
-        except Exception as exc:
-            log.warning("Resolution check failed for market %s: %s", market_id, exc)
-            continue
-        checked += 1
-        if res is None or not res.resolved or res.resolution is None:
-            continue
-        try:
-            pnl = await executor.close_position(
-                market_id, res.resolution, question=pos.get("question") or "",
-            )
-        except Exception as exc:
-            log.error("Failed to close resolved market %s: %s", market_id, exc)
-            continue
-        resolved += 1
-        log.info(
-            "MARKET_RESOLVED market_id=%s resolution=%.1f pnl=%s | %s",
-            market_id,
-            res.resolution,
-            f"{pnl:+.2f}" if pnl is not None else "n/a",
-            (pos.get("question") or "")[:60],
-        )
-    log.info("Resolution check: %d positions checked, %d resolved", checked, resolved)
-
 
 async def run_trading_loop(
     poly: PolymarketClient,
@@ -106,23 +38,9 @@ async def run_trading_loop(
 ) -> None:
     """Main trading loop — runs every 60 seconds."""
     log.info("Trading loop started. PAPER_MODE=%s", PAPER_MODE)
-    checkpoint_monitor = CheckpointMonitor()
-    cycle_count = 0
 
     while True:
         try:
-            cycle_count += 1
-
-            # 0. Resolution detector — every RESOLUTION_CHECK_INTERVAL cycles,
-            #    settle paper positions whose market resolved on Polymarket.
-            #    Runs before evaluation so freed cash/families are usable this cycle.
-            if (
-                PAPER_MODE
-                and isinstance(executor, PaperExecutor)
-                and cycle_count % RESOLUTION_CHECK_INTERVAL == 0
-            ):
-                await check_resolutions(poly, executor, db)
-
             # 1. Fetch active markets (90-day window)
             markets = await poly.get_active_markets()
             log.info("Found %d active markets", len(markets))
@@ -130,16 +48,6 @@ async def run_trading_loop(
             # 2. Get external signals
             ext_data = await external.get_all_signals()
 
-            # 2b. Replay R0: archive this cycle's inputs (ext snapshot + market
-            # metadata).  cycle_ts groups all signals rows of this cycle.
-            cycle_ts = datetime.now(timezone.utc)
-            if SIGNAL_RECORDER_ENABLED:
-                try:
-                    await db.save_ext_snapshot(cycle_ts, ext_data)
-                    await db.upsert_markets(markets)
-                except Exception as exc:
-                    log.warning("Signal recorder (inputs) failed: %s", exc)
-
             # 3. Build occupied_families from the current open portfolio positions.
             #    This prevents re-entering a family where we already hold a position.
             #    We also pull from DB to survive pod restarts.
@@ -194,7 +102,6 @@ async def run_trading_loop(
 
             reentry_guard_count = 0
             cycle_trades = 0
-            traded_market_ids: set[str] = set()
             for market in markets:
                 if market.id in inverted_guard:
                     log.info(
@@ -202,7 +109,6 @@ async def run_trading_loop(
                         market.id, market.question[:60],
                     )
                     reentry_guard_count += 1
-                    strategy.record_skip(market, "reentry_guard")
                     continue
 
                 # evaluate() returns None for all skips — reasons are logged internally
@@ -230,39 +136,11 @@ async def run_trading_loop(
                 # 7. Execute (paper)
                 trade = await executor.execute(order)
                 if trade:
+                    await metrics.record_trade(trade)
                     log.info("Trade executed: %s", trade)
                     # Block this family for the rest of the cycle (Phase 2)
                     occupied_families.add(signal.family_key)
                     cycle_trades += 1
-                    traded_market_ids.add(market.id)
-                    # Mark manifold audit record as used in this trade
-                    if signal.mfld_audit_id:
-                        try:
-                            await db.mark_manifold_audit_used(signal.mfld_audit_id)
-                        except Exception as exc:
-                            log.warning("Failed to mark manifold audit used: %s", exc)
-
-            # 7b. Replay R0: flush this cycle's decision records to the archive.
-            # acted_on marks records whose signal actually became a trade
-            # (evaluate() can emit a signal that risk sizing later rejects).
-            records = strategy.drain_cycle_records()
-            if SIGNAL_RECORDER_ENABLED and records:
-                for rec in records:
-                    if rec["market_id"] in traded_market_ids:
-                        rec["acted_on"] = True
-                try:
-                    await db.save_signal_records(cycle_ts, records)
-                except Exception as exc:
-                    log.warning("Signal recorder (records) failed: %s", exc)
-                if cycle_count % SIGNALS_PRUNE_INTERVAL_CYCLES == 1:
-                    try:
-                        pruned = await db.prune_signal_records(SIGNALS_RETENTION_DAYS)
-                        log.info(
-                            "Signal archive pruned: %d rows older than %d days removed",
-                            pruned, SIGNALS_RETENTION_DAYS,
-                        )
-                    except Exception as exc:
-                        log.warning("Signal archive prune failed: %s", exc)
 
             # 8. [CYCLE SUMMARY] — one block per cycle, stable format for grep/compare
             stats = strategy.get_cycle_stats()
@@ -274,17 +152,7 @@ async def run_trading_loop(
                 if denom == 0:
                     return "0% (0/0)"
                 return f"{n * 100 // denom}% ({n}/{denom})"
-
-            # The accepted/rejected counters only increment on the active-signal
-            # path, so with the signal disabled they always print 0/0 — say
-            # "disabled" instead of pretending the matcher found nothing.
-            if MANIFOLD_SIGNAL_ENABLED:
-                manifold_summary = (
-                    f"  manifold_matches_accepted:      {stats['manifold_matches_accepted']}\n"
-                    f"  manifold_matches_rejected:      {stats['manifold_matches_rejected']}"
-                )
-            else:
-                manifold_summary = "  manifold_signal:                disabled"
+            gnews_cap = strategy._news_queries_this_cycle  # already updated by reset below
 
             log.info(
                 "[CYCLE SUMMARY]\n"
@@ -302,7 +170,9 @@ async def run_trading_loop(
                 "  gnews_queries_used:             %d/%d\n"
                 "  reentry_guard_blocked:          %d\n"
                 "  legacy_incomplete_seen:         %d\n"
-                "%s",
+                "  family_conflicts_prevented:     %d\n"
+                "  manifold_matches_accepted:      %d\n"
+                "  manifold_matches_rejected:      %d",
                 n_total,
                 n_uncertainty,
                 stats["max_edge_gross"],
@@ -317,37 +187,14 @@ async def run_trading_loop(
                 stats["gnews_queries_used"], MAX_NEWS_QUERIES_PER_CYCLE,
                 reentry_guard_count,
                 legacy_incomplete_count,
-                manifold_summary,
+                stats["skip_family"],
+                stats["manifold_matches_accepted"],
+                stats["manifold_matches_rejected"],
             )
 
-            # NEWS SUMMARY — one compact line, only on cycles where at least
-            # one market had a material GNews contribution (never an empty
-            # section on news-less cycles).
-            if stats["news_with_material"] > 0:
-                log.info(
-                    "NEWS SUMMARY | with_news=%d | avg_shift=%+.2f | "
-                    "max_shift=%+.2f | guardrail_applied=%d | changed_decisions=%d",
-                    stats["news_with_material"],
-                    stats["news_avg_shift"],
-                    stats["news_max_shift"],
-                    stats["news_guardrail_applied"],
-                    stats["news_changed_decisions"],
-                )
-
             # 9. Update daily metrics
             await metrics.update_daily_summary()
 
-            # 10. Checkpoint alerts — one-shot / rate-limited Telegram notifications
-            current_portfolio = executor.get_portfolio()
-            try:
-                await checkpoint_monitor.check_all(
-                    db,
-                    exposure_pct=current_portfolio.exposure_pct,
-                    exposure_cap_pct=risk.max_exposure_pct,
-                )
-            except Exception as exc:
-                log.warning("checkpoint_monitor.check_all failed: %s", exc)
-
         except Exception as e:
             log.error("Error in trading loop: %s", e, exc_info=True)
 
@@ -357,17 +204,14 @@ async def run_trading_loop(
 async def run_legacy_scan(
     db: Database,
     markets: list,
+    manifold: ManifoldClient,
     executor: PaperExecutor,
     paper_mode: bool,
 ) -> None:
     """
     One-time startup scan: re-key all open DB positions with the current
-    market_family_key() logic, detect family conflicts, and report
-    KEEP / REVIEW / CLOSE_RECOMMENDED per position.
-
-    Manifold is intentionally not consulted here: with
-    MANIFOLD_SIGNAL_ENABLED=false it is observational-only and must not
-    drive position closures.
+    market_family_key() logic, detect contradictions, re-validate Manifold
+    signals, and report KEEP / REVIEW / CLOSE_RECOMMENDED per position.
 
     In paper_mode: auto-closes all CLOSE_RECOMMENDED positions after logging.
     """
@@ -406,6 +250,8 @@ async def run_legacy_scan(
             "family_key_old": old_fk,
             "family_key_new": new_fk,
             "fk_changed": new_fk != old_fk,
+            "manifold_prob_new": None,
+            "manifold_inverted": False,
             "recommendation": "legacy_incomplete" if is_legacy_incomplete else "OK",
             "rec_reason": "edge_net and live market unavailable" if is_legacy_incomplete else "no family conflict",
         })
@@ -443,7 +289,31 @@ async def run_legacy_scan(
                 p["market_id"], p["family_key_old"] or "none", p["family_key_new"],
             )
 
-    # Step 3: log the full scan report (before any closures)
+    # Step 3: Manifold re-query for positions whose family key changed
+    for p in enriched:
+        if p["live_market"] and p["fk_changed"]:
+            prob = await manifold.get_probability(p["question"])
+            p["manifold_prob_new"] = prob
+            if prob is not None:
+                # Detect if original trade direction conflicts with corrected Manifold signal
+                if prob < 0.40 and p["direction"] == "BUY_YES":
+                    p["manifold_inverted"] = True
+                    note = f"Manifold:{prob:.3f} contradicts BUY_YES (inversion bug confirmed)"
+                    if p["recommendation"] in ("OK", "REVIEW"):
+                        p["recommendation"] = "CLOSE_RECOMMENDED"
+                        p["rec_reason"] = note
+                    else:
+                        p["rec_reason"] += f" | {note}"
+                elif prob > 0.60 and p["direction"] == "BUY_NO":
+                    p["manifold_inverted"] = True
+                    note = f"Manifold:{prob:.3f} contradicts BUY_NO (inversion bug confirmed)"
+                    if p["recommendation"] in ("OK", "REVIEW"):
+                        p["recommendation"] = "CLOSE_RECOMMENDED"
+                        p["rec_reason"] = note
+                    else:
+                        p["rec_reason"] += f" | {note}"
+
+    # Step 4: log the full scan report (before any closures)
     n_close    = sum(1 for p in enriched if p["recommendation"] == "CLOSE_RECOMMENDED")
     n_keep     = sum(1 for p in enriched if p["recommendation"] == "KEEP")
     n_ok       = sum(1 for p in enriched if p["recommendation"] == "OK")
@@ -459,6 +329,7 @@ async def run_legacy_scan(
             "  [%-18s] market=%-8s | dir=%-8s | edge_net=%+.3f\n"
             "                        stored_family: %s\n"
             "                        new_family:    %s%s\n"
+            "                        manifold_new:  %s\n"
             "                        reason: %s",
             p["recommendation"],
             p["market_id"], p["direction"],
@@ -466,11 +337,12 @@ async def run_legacy_scan(
             p["family_key_old"] or "none",
             p["family_key_new"],
             " [CHANGED]" if p["fk_changed"] else "",
+            f"{p['manifold_prob_new']:.3f}" if p["manifold_prob_new"] is not None else "n/a",
             p["rec_reason"],
         )
     log.warning("━" * 70)
 
-    # Step 4: auto-close in paper mode
+    # Step 5: auto-close in paper mode
     if paper_mode and n_close > 0 and isinstance(executor, PaperExecutor):
         log.warning("PAPER MODE: auto-closing %d CLOSE_RECOMMENDED position(s)...", n_close)
         for p in enriched:
@@ -503,7 +375,7 @@ async def main() -> None:
     external = ExternalDataClient()
     news = NewsClient()
     manifold = ManifoldClient()
-    strategy = BayesianStrategy(news=news, manifold=manifold, db=db)
+    strategy = BayesianStrategy(news=news, manifold=manifold)
     risk = RiskManager(max_position_pct=0.05, max_exposure_pct=0.30)
     executor = PaperExecutor(db=db, bankroll=PAPER_BANKROLL) if PAPER_MODE else None
     metrics = MetricsTracker(db=db)
@@ -523,7 +395,7 @@ async def main() -> None:
     except Exception as e:
         log.warning("Could not fetch markets for legacy scan: %s — scan skipped", e)
         scan_markets = []
-    await run_legacy_scan(db, scan_markets, executor, PAPER_MODE)
+    await run_legacy_scan(db, scan_markets, manifold, executor, PAPER_MODE)
 
     try:
         await run_trading_loop(poly, external, strategy, risk, executor, metrics, db)

bot/metrics/sharpe.py

@@ -1,79 +0,0 @@
-"""
-Sharpe ratio from the paper portfolio's daily PnL curve, with a minimum-sample gate.
-
-The input series is the closing total_pnl of each observed UTC day
-(Database.get_daily_pnl_closes). Daily returns are PnL deltas normalized by
-the paper bankroll:
-
-    r_t = (pnl_t − pnl_{t−1}) / bankroll
-
-Sharpe = mean(r) / sample_std(r) × √365, annualized — prediction markets
-resolve every calendar day, so 365 is used instead of 252 trading days.
-Risk-free rate is taken as 0.
-
-Gate: with a tiny sample (e.g. 1 resolved trade over a flat curve plus one
-+299 jump) any Sharpe value is statistically meaningless — artificially huge
-or tiny depending on where the jump lands. So no numeric Sharpe is exposed
-until BOTH minimums are met:
-
-    days observed     >= MIN_DAYS_OBSERVED   (30)
-    resolved trades   >= MIN_RESOLVED_TRADES (10)
-
-Below either minimum the value is None with status "insufficient_sample".
-A perfectly flat curve (zero variance) also yields None ("zero_variance"):
-Sharpe is undefined there, not infinite.
-"""
-from statistics import mean, stdev
-from typing import Optional
-
-MIN_DAYS_OBSERVED = 30
-MIN_RESOLVED_TRADES = 10
-ANNUALIZATION_DAYS = 365
-
-SHARPE_OK = "ok"
-SHARPE_INSUFFICIENT = "insufficient_sample"
-SHARPE_ZERO_VARIANCE = "zero_variance"
-
-
-def daily_returns(daily_pnl_closes: list[float], bankroll: float) -> list[float]:
-    """Bankroll-normalized day-over-day returns from a daily PnL-close series."""
-    return [
-        (curr - prev) / bankroll
-        for prev, curr in zip(daily_pnl_closes, daily_pnl_closes[1:])
-    ]
-
-
-def compute_sharpe(daily_pnl_closes: list[float], bankroll: float) -> Optional[float]:
-    """Annualized Sharpe of the daily PnL curve, or None if undefined.
-
-    None when there are fewer than 2 returns (need 3+ daily closes) or the
-    return series has zero variance. No sample-size gate here — see
-    sharpe_with_gate() for the exposed value.
-    """
-    returns = daily_returns(daily_pnl_closes, bankroll)
-    if len(returns) < 2:
-        return None
-    sd = stdev(returns)
-    if sd == 0:
-        return None
-    return mean(returns) / sd * ANNUALIZATION_DAYS ** 0.5
-
-
-def sharpe_with_gate(
-    daily_pnl_closes: list[float],
-    bankroll: float,
-    resolved_count: int,
-) -> tuple[Optional[float], str]:
-    """Return (sharpe, status) applying the minimum-sample gate.
-
-    status: "ok"                  — sharpe is a meaningful float
-            "insufficient_sample" — sample below minimums, sharpe is None
-            "zero_variance"       — sample OK but flat curve, sharpe is None
-    """
-    days_observed = len(daily_pnl_closes)
-    if days_observed < MIN_DAYS_OBSERVED or resolved_count < MIN_RESOLVED_TRADES:
-        return None, SHARPE_INSUFFICIENT
-    sharpe = compute_sharpe(daily_pnl_closes, bankroll)
-    if sharpe is None:
-        return None, SHARPE_ZERO_VARIANCE
-    return sharpe, SHARPE_OK

bot/metrics/tracker.py

@@ -15,16 +15,13 @@ win_rate            Fraction of resolved closed trades with close_pnl > 0.
                     NULL if fewer than 5 resolved trades.
 calibration_score   1 − AVG((final_prob − resolution)²) on resolved trades.
                     Brier score (higher = better calibration). NULL if < 10 resolved.
-sharpe_ratio        Annualized Sharpe of the daily total_pnl curve (see
-                    bot/metrics/sharpe.py). NULL until the sample gate passes:
-                    >= 30 days observed AND >= 10 resolved trades.
+sharpe_ratio        0.0 — requires a daily-return time series, not yet tracked.
 """
 import logging
-import os
 from datetime import datetime, UTC
 
 from bot.data.db import Database
-from bot.metrics.sharpe import sharpe_with_gate
+from bot.executor.paper import Trade
 
 log = logging.getLogger(__name__)
 
@@ -33,6 +30,11 @@ class MetricsTracker:
     def __init__(self, db: Database) -> None:
         self._db = db
 
+    async def record_trade(self, trade: Trade) -> None:
+        """Persist a trade to the DB. No in-memory accumulation."""
+        await self._db.save_trade(trade)
+        log.info("Trade recorded: %s", trade)
+
     async def update_daily_summary(self) -> None:
         """Compute metrics from DB and write a metrics_daily snapshot.
 
@@ -65,12 +67,6 @@ class MetricsTracker:
 
         avg_edge = total_pnl / total_deployed if total_deployed > 0 else 0.0
 
-        # Sharpe: real value from the daily PnL curve, NULL while the sample
-        # gate (>=30 days observed, >=10 resolved) is not met.
-        bankroll = float(os.getenv("PAPER_BANKROLL", "10000"))
-        daily_closes = await self._db.get_daily_pnl_closes()
-        sharpe, sharpe_status = sharpe_with_gate(daily_closes, bankroll, resolved)
-
         metrics = {
             "timestamp":          datetime.now(UTC),
             "total_trades":       int(raw["total_trades"]),
@@ -84,7 +80,7 @@ class MetricsTracker:
             "total_pnl":          total_pnl,
             "win_rate":           win_rate,
             "avg_edge":           avg_edge,
-            "sharpe_ratio":       sharpe,   # NULL until sample gate passes
+            "sharpe_ratio":       0.0,   # requires daily-return series (not yet tracked)
             "calibration_score":  calibration,
             "paper_mode":         True,
         }
@@ -93,10 +89,9 @@ class MetricsTracker:
         log.info(
             "Daily metrics | trades=%d (open=%d closed=%d resolved=%d) | "
             "unrealized=$%.2f realized=$%.2f total=$%.2f | "
-            "win_rate=%s calibration=%s sharpe=%s",
+            "win_rate=%s calibration=%s",
             metrics["total_trades"], open_count, closed_count, resolved,
             unrealized, realized, total_pnl,
             f"{win_rate:.1%}" if win_rate is not None else "n/a (<5)",
             f"{calibration:.3f}" if calibration is not None else "n/a (<10)",
-            f"{sharpe:.2f}" if sharpe is not None else f"n/a ({sharpe_status})",
         )

bot/notify/checkpoints.py

@@ -1,184 +0,0 @@
-"""One-shot and rate-limited Telegram checkpoint alerts.
-
-Called from the main trading loop at the end of each cycle.
-Errors are swallowed — checkpoint failures must never break the loop.
-"""
-import logging
-from datetime import datetime, timezone
-from typing import Optional
-
-from bot.notify import telegram
-
-log = logging.getLogger(__name__)
-
-_EXPOSURE_COOLDOWN_HOURS = 6
-
-
-class CheckpointMonitor:
-
-    async def check_all(
-        self,
-        db,
-        exposure_pct: float,
-        exposure_cap_pct: float,
-    ) -> None:
-        for name, coro in [
-            ("primer_match_accepted", self._check_primer_match_accepted(db)),
-            ("primer_trade_phase6",   self._check_primer_trade_phase6(db)),
-            ("primer_resolved",       self._check_primer_resolved(db)),
-            ("exposure_cerca_cap",    self._check_exposure_cerca_cap(db, exposure_pct, exposure_cap_pct)),
-        ]:
-            try:
-                await coro
-            except Exception as exc:
-                log.warning("checkpoint %s failed: %s", name, exc)
-
-    # ── helpers ──────────────────────────────────────────────────────────────
-
-    async def _one_shot_fired(self, db, name: str) -> bool:
-        async with db._pool.acquire() as conn:
-            row = await conn.fetchrow(
-                "SELECT 1 FROM checkpoint_alerts WHERE checkpoint_name = $1", name
-            )
-            return row is not None
-
-    async def _mark_one_shot(self, db, name: str) -> None:
-        async with db._pool.acquire() as conn:
-            await conn.execute(
-                "INSERT INTO checkpoint_alerts (checkpoint_name, fired_at) VALUES ($1, NOW())",
-                name,
-            )
-
-    async def _last_fired_at(self, db, name: str) -> Optional[datetime]:
-        async with db._pool.acquire() as conn:
-            row = await conn.fetchrow(
-                "SELECT last_fired_at FROM checkpoint_alerts WHERE checkpoint_name = $1",
-                name,
-            )
-        if row is None:
-            return None
-        return row["last_fired_at"]
-
-    async def _upsert_repeatable(self, db, name: str) -> None:
-        async with db._pool.acquire() as conn:
-            await conn.execute(
-                """
-                INSERT INTO checkpoint_alerts (checkpoint_name, fired_at, last_fired_at)
-                VALUES ($1, NOW(), NOW())
-                ON CONFLICT (checkpoint_name) DO UPDATE SET last_fired_at = NOW()
-                """,
-                name,
-            )
-
-    # ── checkpoints ──────────────────────────────────────────────────────────
-
-    async def _check_primer_match_accepted(self, db) -> None:
-        if await self._one_shot_fired(db, "primer_match_accepted"):
-            return
-
-        async with db._pool.acquire() as conn:
-            row = await conn.fetchrow(
-                """
-                SELECT match_score, poly_question, mfld_market_title
-                FROM manifold_match_audit
-                WHERE match_status = 'accepted'
-                ORDER BY timestamp ASC
-                LIMIT 1
-                """
-            )
-        if not row:
-            return
-
-        score  = float(row["match_score"] or 0.0)
-        poly_q = (row["poly_question"] or "")[:60]
-        mfld_t = (row["mfld_market_title"] or "")[:60]
-
-        await telegram._send(
-            f"✅ Primer match Manifold accepted — score={score:.2f} "
-            f"poly='{poly_q}' mfld='{mfld_t}'"
-        )
-        await self._mark_one_shot(db, "primer_match_accepted")
-        log.info("checkpoint primer_match_accepted fired")
-
-    async def _check_primer_trade_phase6(self, db) -> None:
-        if await self._one_shot_fired(db, "primer_trade_phase6"):
-            return
-
-        async with db._pool.acquire() as conn:
-            row = await conn.fetchrow(
-                """
-                SELECT question, mfld_match_score, edge_net
-                FROM trades
-                WHERE mfld_match_score IS NOT NULL
-                  AND (excluded_from_metrics IS NOT TRUE)
-                ORDER BY timestamp ASC
-                LIMIT 1
-                """
-            )
-        if not row:
-            return
-
-        question = (row["question"] or "")[:70]
-        score    = float(row["mfld_match_score"] or 0.0)
-        edge     = float(row["edge_net"] or 0.0)
-
-        await telegram._send(
-            f"🎯 Primer trade Phase-6 limpio — {question} "
-            f"score={score:.2f} edge={edge:.3f}"
-        )
-        await self._mark_one_shot(db, "primer_trade_phase6")
-        log.info("checkpoint primer_trade_phase6 fired")
-
-    async def _check_primer_resolved(self, db) -> None:
-        if await self._one_shot_fired(db, "primer_resolved"):
-            return
-
-        async with db._pool.acquire() as conn:
-            row = await conn.fetchrow(
-                """
-                SELECT question, resolution, close_pnl
-                FROM trades
-                WHERE resolution IS NOT NULL
-                  AND (excluded_from_metrics IS NOT TRUE)
-                ORDER BY closed_at ASC
-                LIMIT 1
-                """
-            )
-        if not row:
-            return
-
-        question   = (row["question"] or "")[:70]
-        resolution = float(row["resolution"] or 0.0)
-        pnl        = float(row["close_pnl"] or 0.0)
-
-        await telegram._send(
-            f"🏁 Primer mercado resuelto — {question} "
-            f"result={resolution} pnl={pnl:.2f}"
-        )
-        await self._mark_one_shot(db, "primer_resolved")
-        log.info("checkpoint primer_resolved fired")
-
-    async def _check_exposure_cerca_cap(
-        self, db, exposure_pct: float, exposure_cap_pct: float
-    ) -> None:
-        if exposure_pct < 0.80 * exposure_cap_pct:
-            return
-
-        last = await self._last_fired_at(db, "exposure_cerca_cap")
-        if last is not None:
-            now_utc = datetime.now(timezone.utc)
-            if last.tzinfo is None:
-                last = last.replace(tzinfo=timezone.utc)
-            elapsed_hours = (now_utc - last).total_seconds() / 3600
-            if elapsed_hours < _EXPOSURE_COOLDOWN_HOURS:
-                return
-
-        await telegram._send(
-            f"⚠️ Exposure al 80% del cap — revisar posiciones "
-            f"({exposure_pct * 100:.1f}% / {exposure_cap_pct * 100:.1f}%)"
-        )
-        await self._upsert_repeatable(db, "exposure_cerca_cap")
-        log.info(
-            "checkpoint exposure_cerca_cap fired (%.1f%% / %.1f%%)",
-            exposure_pct * 100, exposure_cap_pct * 100,
-        )

bot/outcomes.py

@@ -1,208 +0,0 @@
-"""
-Replay R2 — outcomes + calibration metrics.
-
-Two phases, one CLI:
-
-1. Fetch: for every archived market (present in `signals`) without a stored
-   outcome, ask the Gamma API via PolymarketClient.get_market_resolution()
-   — the same UMA-finality gate the trading loop uses to settle positions.
-   Definitive resolutions are upserted into `market_outcomes`; open, disputed
-   or ambiguous markets are simply retried on the next invocation.  There is
-   no data-loss urgency here (unlike the R0 recorder): Gamma reports past
-   resolutions at any time, so running this lazily loses nothing.
-
-2. Score: join archived estimates to outcomes and compute Brier / log-loss of
-   estimated_prob, benchmarked against the market price (prior_prob) on the
-   same rows.  This scores ALL evaluations with a full estimate — the sample
-   multiplier the phase plan calls for — not just executed trades.  With
-   --run-id it scores a replay run's re-estimates instead (counterfactual
-   calibration: did config X predict better than the market?).
-
-Reading the numbers: lower is better for both metrics; model < prior means
-the model added information over the market price.  Micro averages weight
-every evaluation equally, so long-lived markets (~1 evaluation/min while in
-the universe) dominate; macro averages score each market once (mean of its
-evaluations) and answer the same question per market.  Evaluations of one
-market minutes apart are highly autocorrelated — n_evaluations overstates
-the effective sample size, n_markets is the honest one.
-
-CLI:
-    python -m bot.outcomes                  # fetch new outcomes, then score archive
-    python -m bot.outcomes --fetch-only
-    python -m bot.outcomes --metrics-only
-    python -m bot.outcomes --run-id UUID    # score a replay run (implies no fetch)
-"""
-import argparse
-import asyncio
-import logging
-import math
-from collections import defaultdict
-from typing import Optional
-
-from bot.data.db import Database
-from bot.data.polymarket import PolymarketClient
-
-log = logging.getLogger(__name__)
-
-# Clip probabilities before log() so a (theoretical) hard 0/1 estimate on a
-# wrong outcome scores ~20.7 nats instead of infinity poisoning the mean.
-LOGLOSS_EPS = 1e-9
-
-
-async def fetch_outcomes(poly, market_ids: list[str]) -> list[dict]:
-    """Resolve archived markets against Gamma; returns only definitive ones.
-
-    Sequential on purpose: ~50 markets per invocation, and the Gamma API has
-    no bulk endpoint.  get_market_resolution() already returns None on API
-    errors and resolved=False on open/disputed/ambiguous markets.
-    """
-    resolved = []
-    for market_id in market_ids:
-        res = await poly.get_market_resolution(market_id)
-        if res is None or not res.resolved or res.resolution is None:
-            continue
-        resolved.append({
-            "market_id": market_id,
-            "outcome": res.resolution,
-            "resolved_at": res.resolved_at,
-        })
-    return resolved
-
-
-def _logloss(p: float, outcome: float) -> float:
-    p = min(max(p, LOGLOSS_EPS), 1.0 - LOGLOSS_EPS)
-    return -math.log(p) if outcome == 1.0 else -math.log(1.0 - p)
-
-
-def compute_calibration(rows: list[dict]) -> Optional[dict]:
-    """Score estimated_prob vs prior_prob against outcomes; None if no rows.
-
-    rows: dicts with market_id, category, estimated_prob, prior_prob, outcome.
-    Pure function — the CLI feeds it DB rows, tests feed it literals.
-    """
-    if not rows:
-        return None
-
-    n = len(rows)
-    brier_model = sum((r["estimated_prob"] - r["outcome"]) ** 2 for r in rows) / n
-    brier_prior = sum((r["prior_prob"] - r["outcome"]) ** 2 for r in rows) / n
-    logloss_model = sum(_logloss(r["estimated_prob"], r["outcome"]) for r in rows) / n
-    logloss_prior = sum(_logloss(r["prior_prob"], r["outcome"]) for r in rows) / n
-
-    by_market: dict[str, list[dict]] = defaultdict(list)
-    for r in rows:
-        by_market[r["market_id"]].append(r)
-    market_briers = [
-        (
-            sum((r["estimated_prob"] - r["outcome"]) ** 2 for r in mrows) / len(mrows),
-            sum((r["prior_prob"] - r["outcome"]) ** 2 for r in mrows) / len(mrows),
-        )
-        for mrows in by_market.values()
-    ]
-    brier_model_macro = sum(b[0] for b in market_briers) / len(market_briers)
-    brier_prior_macro = sum(b[1] for b in market_briers) / len(market_briers)
-
-    by_category: dict[str, list[dict]] = defaultdict(list)
-    for r in rows:
-        by_category[r["category"] or "unknown"].append(r)
-    per_category = {
-        cat: {
-            "n": len(crows),
-            "markets": len({r["market_id"] for r in crows}),
-            "brier_model": sum((r["estimated_prob"] - r["outcome"]) ** 2
-                               for r in crows) / len(crows),
-            "brier_prior": sum((r["prior_prob"] - r["outcome"]) ** 2
-                               for r in crows) / len(crows),
-        }
-        for cat, crows in sorted(by_category.items())
-    }
-
-    return {
-        "n_evaluations": n,
-        "n_markets": len(by_market),
-        "brier_model": brier_model,
-        "brier_prior": brier_prior,
-        "brier_model_macro": brier_model_macro,
-        "brier_prior_macro": brier_prior_macro,
-        "logloss_model": logloss_model,
-        "logloss_prior": logloss_prior,
-        "per_category": per_category,
-    }
-
-
-def print_report(metrics: Optional[dict], source: str) -> None:
-    if metrics is None:
-        print(f"calibration : no scorable rows yet for {source} "
-              "(no archived estimate has a resolved outcome)")
-        return
-    print(f"calibration : {source} — {metrics['n_evaluations']} evaluations, "
-          f"{metrics['n_markets']} markets")
-    print(f"{'':14s}{'model':>10s}{'market':>10s}{'delta':>10s}")
-    for label, m_key, p_key in (
-        ("Brier micro", "brier_model", "brier_prior"),
-        ("Brier macro", "brier_model_macro", "brier_prior_macro"),
-        ("logloss micro", "logloss_model", "logloss_prior"),
-    ):
-        m, p = metrics[m_key], metrics[p_key]
-        print(f"  {label:12s}{m:>10.4f}{p:>10.4f}{m - p:>+10.4f}")
-    print("  (delta < 0 = model beats the market price)")
-    for cat, c in metrics["per_category"].items():
-        print(f"  {cat:12s}n={c['n']:<6d} markets={c['markets']:<3d} "
-              f"brier model {c['brier_model']:.4f} vs market {c['brier_prior']:.4f}")
-
-
-async def _amain(args: argparse.Namespace) -> None:
-    db = Database()
-    await db.connect()
-    try:
-        if not args.metrics_only and args.run_id is None:
-            pending = await db.get_unresolved_archived_market_ids()
-            poly = PolymarketClient()
-            try:
-                resolved = await fetch_outcomes(poly, pending)
-            finally:
-                await poly.close()
-            for out in resolved:
-                await db.upsert_market_outcome(
-                    out["market_id"], out["outcome"], out["resolved_at"]
-                )
-            print(f"outcomes    : {len(resolved)} newly resolved "
-                  f"(of {len(pending)} pending markets checked)")
-
-        coverage = await db.get_outcome_coverage()
-        print(f"coverage    : {coverage['resolved']}/{coverage['archived']} "
-              "archived markets resolved")
-
-        if args.fetch_only:
-            return
-        rows = await db.get_calibration_rows(run_id=args.run_id)
-        source = f"replay run {args.run_id}" if args.run_id else "R0 archive"
-        print_report(compute_calibration(rows), source)
-    finally:
-        await db.disconnect()
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        prog="python -m bot.outcomes",
-        description="Fetch market resolutions and score archived estimates.",
-    )
-    parser.add_argument("--fetch-only", action="store_true",
-                        help="only fetch/store outcomes, skip metrics")
-    parser.add_argument("--metrics-only", action="store_true",
-                        help="skip the Gamma fetch, score what is stored")
-    parser.add_argument("--run-id", default=None,
-                        help="score a replay run's re-estimates instead of "
-                             "the R0 archive (implies --metrics-only)")
-    args = parser.parse_args()
-    if args.fetch_only and args.metrics_only:
-        parser.error("--fetch-only and --metrics-only are mutually exclusive")
-    logging.basicConfig(
-        level=logging.INFO,
-        format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
-    )
-    asyncio.run(_amain(args))
-
-
-if __name__ == "__main__":
-    main()

bot/replay.py

@@ -1,394 +0,0 @@
-"""
-Replay R1 — replay core.
-
-Re-executes BayesianStrategy.evaluate() over the R0 archive (signals +
-ext_snapshots + markets) and stores the outcome in replay_runs /
-replay_decisions.
-
-Determinism contract: evaluate() is a pure function of
-(market, ext, occupied_families, as_of) plus the news client, so a replay
-rebuilds exactly those four inputs from the archive:
-
-  market   — metadata from `markets`, per-cycle price/volume from `signals`
-  ext      — the cycle's `ext_snapshots` row
-  families — a family-skipped row replays with its own family_key occupied;
-             every other row replays with no occupancy (the recorded
-             skip_reason already reflects the original portfolio state)
-  as_of    — the archived cycle_ts (clock injection, Replay R1)
-
-GNews is never called: ReplayNews feeds back the archived news_sentiment.
-The per-cycle query budget is bypassed (reset before every market) because
-the archived sentiment already encodes the budget's effect — a
-budget-skipped market was recorded with sentiment 0.0.
-
-Manifold and the DB are not wired into the replayed strategy (manifold=None,
-db=None): the signal is observational-only in production (feat_mfld_lo is
-always 0.0 in the archive), so the replay reproduces decisions without
-touching cooldowns or audit tables.  If MANIFOLD_SIGNAL_ENABLED is ever
-turned on, replayed decisions will diverge from recorded ones and the
-matched/mismatch_field columns will say so.
-
-Run tagging: every run stores the git sha and a hash of the strategy
-constants.  Same config_hash vs the archive = determinism check (expect 0
-mismatches, modulo UTC-day-boundary crossings between cycle_ts and the
-original wall-clock).  Different config_hash = counterfactual run.
-
-CLI:
-    python -m bot.replay --from 2026-07-02T00:00:00Z --to 2026-07-03 --note "..."
-"""
-import argparse
-import asyncio
-import hashlib
-import json
-import logging
-import os
-import subprocess
-import uuid
-from collections import Counter
-from datetime import datetime, timedelta, timezone
-from typing import Optional
-
-import bot.strategy.bayesian as bayesian
-from bot.data.db import Database
-from bot.data.external import ExternalSignals
-from bot.data.polymarket import Market
-from bot.strategy.bayesian import BayesianStrategy
-
-log = logging.getLogger(__name__)
-
-# Absolute float tolerance for recorded-vs-replayed comparison.  Archived
-# values are float8 (exact IEEE-754 round-trip of Python floats), so any real
-# divergence is far larger than this.
-FLOAT_TOL = 1e-9
-
-# Strategy constants that define a replay configuration.  Hashed into
-# replay_runs.config_hash; read from the module at call time so a
-# counterfactual run can monkeypatch them and be tagged distinctly.
-CONFIG_KEYS = (
-    "SPREAD_ESTIMATE",
-    "COMMISSION_RATE",
-    "MIN_CONFIDENCE",
-    "NEWS_LOGODDS_WEIGHT",
-    "MANIFOLD_LOGODDS_WEIGHT",
-    "MANIFOLD_SIGNAL_ENABLED",
-    "NEWS_GUARDRAIL_ENABLED",
-    "MAX_NEWS_ONLY_PROB_SHIFT",
-    "NEWS_MATERIAL_LOGODDS_THRESHOLD",
-    "MAX_NEWS_QUERIES_PER_CYCLE",
-)
-
-# Rows recorded outside evaluate() (via record_skip) carry no decision fields;
-# the replay still re-evaluates them for calibration but cannot compare.
-NON_COMPARABLE_SKIPS = {"reentry_guard"}
-
-
-def strategy_config() -> dict:
-    return {k: getattr(bayesian, k) for k in CONFIG_KEYS}
-
-
-def strategy_config_hash() -> str:
-    blob = json.dumps(strategy_config(), sort_keys=True)
-    return hashlib.sha256(blob.encode()).hexdigest()[:12]
-
-
-def _git_sha() -> str:
-    sha = os.getenv("GIT_SHA", "")
-    if sha:
-        return sha
-    try:
-        return subprocess.run(
-            ["git", "rev-parse", "--short", "HEAD"],
-            capture_output=True, text=True, timeout=5,
-        ).stdout.strip() or "unknown"
-    except (OSError, subprocess.SubprocessError):
-        return "unknown"
-
-
-class ReplayNews:
-    """NewsClient stand-in that feeds archived sentiment back into evaluate().
-
-    No HTTP, no cache: the engine sets `sentiment` to the archived value
-    before each evaluate() call.  Values below evaluate()'s 0.05 materiality
-    threshold were archived as 0.0, so the round-trip is exact.
-    """
-    enabled = True
-
-    def __init__(self) -> None:
-        self.sentiment: float = 0.0
-
-    async def get_sentiment(self, question: str) -> float:
-        return self.sentiment
-
-    def get_freshness(self, question: str) -> float:
-        return 1.0  # only used by gnews_priority(), which replay never calls
-
-
-def build_ext(snapshot: dict) -> ExternalSignals:
-    """Rebuild the ExternalSignals a cycle was evaluated against."""
-    return ExternalSignals(
-        btc_price=snapshot["btc_price"],
-        btc_change_24h=snapshot["btc_change_24h"],
-        eth_price=snapshot["eth_price"],
-        eth_change_24h=snapshot["eth_change_24h"],
-        btc_dominance=snapshot["btc_dominance"],
-        fear_greed_index=snapshot["fear_greed_index"],
-        fear_greed_label=snapshot["fear_greed_label"],
-        total_market_cap_change=snapshot["total_market_cap_change"],
-        valid=snapshot["valid"],
-    )
-
-
-def build_market(market_row: dict, signal_row: dict) -> Market:
-    """Rebuild a Market: metadata from `markets`, per-cycle state from `signals`.
-
-    Token ids are irrelevant to evaluate() and left empty; no_price is the
-    YES complement (evaluate() never reads it either).
-    """
-    yes_price = signal_row["polymarket_price"]
-    return Market(
-        id=market_row["id"],
-        condition_id=market_row["condition_id"] or "",
-        question=market_row["question"],
-        yes_token_id="",
-        no_token_id="",
-        yes_price=yes_price,
-        no_price=1.0 - yes_price,
-        volume_24h=signal_row["volume_24h"] or 0.0,
-        end_date=market_row["end_date"] or "",
-        active=True,
-        category=signal_row["category"] or (market_row["category"] or ""),
-    )
-
-
-def _compare(recorded: dict, replayed: dict) -> Optional[str]:
-    """Return the first field where replayed diverges from recorded, or None."""
-    if recorded["skip_reason"] != replayed["skip_reason"]:
-        return "skip_reason"
-    for field in ("prior_prob", "estimated_prob", "raw_final_prob",
-                  "edge_net", "confidence"):
-        a, b = recorded[field], replayed[field]
-        if a is None and b is None:
-            continue
-        if a is None or b is None or abs(a - b) > FLOAT_TOL:
-            return field
-    if recorded["direction"] != replayed["direction"]:
-        return "direction"
-    return None
-
-
-async def replay_cycle(
-    cycle_ts: datetime,
-    snapshot: dict,
-    signal_rows: list[dict],
-    market_rows: dict[str, dict],
-) -> list[dict]:
-    """Re-evaluate one archived cycle; returns one decision dict per row.
-
-    Pure with respect to the DB — everything it needs is passed in, so tests
-    can drive it with synthetic rows.
-    """
-    news = ReplayNews()
-    strategy = BayesianStrategy(news=news, manifold=None, db=None)
-    ext = build_ext(snapshot)
-    decisions: list[dict] = []
-
-    for row in signal_rows:
-        recorded_skip = row["skip_reason"]
-        decision = {
-            "cycle_ts": cycle_ts,
-            "market_id": row["market_id"],
-            "skip_reason": None,
-            "prior_prob": None,
-            "estimated_prob": None,
-            "raw_final_prob": None,
-            "edge_gross": None,
-            "edge_net": None,
-            "regime_min_edge": None,
-            "days_to_resolution": None,
-            "confidence": None,
-            "direction": None,
-            "would_trade": None,
-            "recorded_skip_reason": recorded_skip,
-            "matched": None,
-            "mismatch_field": None,
-        }
-
-        market_row = market_rows.get(row["market_id"])
-        if market_row is None:
-            # Should not happen (R0 upserts markets every cycle) — record the
-            # gap instead of crashing the run.
-            decision["matched"] = False
-            decision["mismatch_field"] = "market_missing"
-            decisions.append(decision)
-            continue
-
-        market = build_market(market_row, row)
-        # A family-skipped row replays against its own occupied family; all
-        # other rows replay unoccupied — their recorded skip_reason already
-        # reflects whatever portfolio state existed, and evaluate() checks
-        # the family gate before anything portfolio-dependent.
-        families = (
-            {row["family_key"]}
-            if recorded_skip == "family" and row["family_key"]
-            else set()
-        )
-        news.sentiment = row["news_sentiment"] or 0.0
-        # Bypass the per-cycle GNews budget: archived sentiment already
-        # encodes it (budget-skipped markets were recorded with 0.0).
-        strategy._news_queries_this_cycle = 0
-
-        signal = await strategy.evaluate(market, ext, families, as_of=cycle_ts)
-        rec = strategy.drain_cycle_records()[-1]
-
-        decision.update(
-            skip_reason=rec["skip_reason"],
-            prior_prob=rec["prior_prob"],
-            estimated_prob=rec["estimated_prob"],
-            raw_final_prob=rec["raw_final_prob"],
-            edge_gross=rec["edge_gross"],
-            edge_net=rec["edge_net"],
-            regime_min_edge=rec["regime_min_edge"],
-            days_to_resolution=rec["days_to_resolution"],
-            confidence=rec["confidence"],
-            direction=rec["direction"],
-            would_trade=signal is not None,
-        )
-        if recorded_skip in NON_COMPARABLE_SKIPS:
-            decision["matched"] = None  # re-evaluated for calibration only
-        else:
-            mismatch = _compare(row, rec)
-            decision["matched"] = mismatch is None
-            decision["mismatch_field"] = mismatch
-        decisions.append(decision)
-
-    return decisions
-
-
-async def run_replay(
-    db: Database,
-    from_ts: datetime,
-    to_ts: datetime,
-    note: str = "",
-    limit_cycles: Optional[int] = None,
-) -> dict:
-    """Replay every archived cycle in [from_ts, to_ts) and persist the run.
-
-    Returns the replay_runs row (plus a mismatch_fields Counter) for reporting.
-    """
-    run_id = str(uuid.uuid4())
-    cycles = await db.get_replay_cycles(from_ts, to_ts)
-    if limit_cycles:
-        cycles = cycles[:limit_cycles]
-
-    decisions_total = 0
-    matched = 0
-    mismatched = 0
-    mismatch_fields: Counter = Counter()
-    skipped_cycles = 0
-
-    for cycle_ts in cycles:
-        snapshot = await db.get_ext_snapshot(cycle_ts)
-        if snapshot is None:
-            skipped_cycles += 1
-            log.warning("Replay: no ext_snapshot for cycle %s — skipped", cycle_ts)
-            continue
-        signal_rows = await db.get_cycle_signal_rows(cycle_ts)
-        market_rows = await db.get_markets_by_ids(
-            [r["market_id"] for r in signal_rows]
-        )
-        decisions = await replay_cycle(cycle_ts, snapshot, signal_rows, market_rows)
-        await db.save_replay_decisions(run_id, decisions)
-
-        decisions_total += len(decisions)
-        for d in decisions:
-            if d["matched"] is True:
-                matched += 1
-            elif d["matched"] is False:
-                mismatched += 1
-                mismatch_fields[d["mismatch_field"]] += 1
-
-    run = {
-        "run_id": run_id,
-        "git_sha": _git_sha(),
-        "config_hash": strategy_config_hash(),
-        "config_json": json.dumps(strategy_config(), sort_keys=True),
-        "from_ts": from_ts,
-        "to_ts": to_ts,
-        "cycles": len(cycles) - skipped_cycles,
-        "decisions": decisions_total,
-        "matched": matched,
-        "mismatched": mismatched,
-        "note": note,
-    }
-    await db.save_replay_run(run)
-    run["mismatch_fields"] = dict(mismatch_fields)
-    run["skipped_cycles"] = skipped_cycles
-    return run
-
-
-def _parse_ts(value: str) -> datetime:
-    dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
-    if dt.tzinfo is None:
-        dt = dt.replace(tzinfo=timezone.utc)
-    return dt
-
-
-async def _amain(args: argparse.Namespace) -> None:
-    db = Database()
-    await db.connect()
-    try:
-        run = await run_replay(
-            db,
-            from_ts=args.from_ts,
-            to_ts=args.to_ts,
-            note=args.note,
-            limit_cycles=args.limit_cycles,
-        )
-    finally:
-        await db.disconnect()
-
-    comparable = run["matched"] + run["mismatched"]
-    print(f"run_id      : {run['run_id']}")
-    print(f"git_sha     : {run['git_sha']}   config_hash: {run['config_hash']}")
-    print(f"window      : {run['from_ts'].isoformat()} → {run['to_ts'].isoformat()}")
-    print(f"cycles      : {run['cycles']} (skipped: {run['skipped_cycles']})")
-    print(f"decisions   : {run['decisions']} ({comparable} comparable)")
-    print(f"matched     : {run['matched']}")
-    print(f"mismatched  : {run['mismatched']}")
-    if run["mismatch_fields"]:
-        for field, count in sorted(run["mismatch_fields"].items(), key=lambda x: -x[1]):
-            print(f"  {field}: {count}")
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        prog="python -m bot.replay",
-        description="Replay archived trading cycles through the current strategy.",
-    )
-    now = datetime.now(timezone.utc)
-    parser.add_argument(
-        "--from", dest="from_ts", type=_parse_ts,
-        default=now - timedelta(hours=24),
-        help="window start, ISO-8601 (default: 24h ago)",
-    )
-    parser.add_argument(
-        "--to", dest="to_ts", type=_parse_ts, default=now,
-        help="window end, ISO-8601, exclusive (default: now)",
-    )
-    parser.add_argument("--note", default="", help="free-text tag for replay_runs")
-    parser.add_argument(
-        "--limit-cycles", type=int, default=None,
-        help="replay at most N cycles (smoke runs)",
-    )
-    args = parser.parse_args()
-    logging.basicConfig(
-        level=logging.INFO,
-        format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
-    )
-    # evaluate() logs one INFO line per market — thousands per replay window.
-    logging.getLogger("bot.strategy.bayesian").setLevel(logging.WARNING)
-    asyncio.run(_amain(args))
-
-
-if __name__ == "__main__":
-    main()

bot/risk/manager.py

@@ -62,17 +62,6 @@ class Order:
     feat_news_lo:    float = 0.0
     feat_mfld_lo:    float = 0.0
     feat_btc_dom_lo: float = 0.0
-    # Manifold audit fields (propagated from TradingSignal → Trade → DB)
-    mfld_audit_id:    Optional[str]   = None
-    mfld_market_id:   Optional[str]   = None
-    mfld_market_title: Optional[str]  = None
-    mfld_market_url:  Optional[str]   = None
-    mfld_prob_raw:    Optional[float] = None
-    mfld_prob_final:  Optional[float] = None
-    mfld_inverted:    bool            = False
-    mfld_match_score: Optional[float] = None
-    mfld_match_reason: Optional[str]  = None
-    mfld_match_status: Optional[str]  = None
 
 
 class RiskManager:
@@ -170,15 +159,4 @@ class RiskManager:
             feat_news_lo=signal.feat_news_lo,
             feat_mfld_lo=signal.feat_mfld_lo,
             feat_btc_dom_lo=signal.feat_btc_dom_lo,
-            # Manifold audit
-            mfld_audit_id=signal.mfld_audit_id,
-            mfld_market_id=signal.mfld_market_id,
-            mfld_market_title=signal.mfld_market_title,
-            mfld_market_url=signal.mfld_market_url,
-            mfld_prob_raw=signal.mfld_prob_raw,
-            mfld_prob_final=signal.mfld_prob_final,
-            mfld_inverted=signal.mfld_inverted,
-            mfld_match_score=signal.mfld_match_score,
-            mfld_match_reason=signal.mfld_match_reason,
-            mfld_match_status=signal.mfld_match_status,
         )

bot/strategy/bayesian.py

@@ -12,21 +12,16 @@ Polymarket might reflect in a slow-moving order book.
 """
 import logging
 import math
-import os
-import re
-import uuid
 from dataclasses import dataclass, field
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timezone
 from typing import Optional, TYPE_CHECKING
 
 from bot.data.polymarket import Market, market_family_key
 from bot.data.external import ExternalSignals
-from bot.data.manifold import MANIFOLD_MATCHER_VERSION, ManifoldMatchResult
 
 if TYPE_CHECKING:
     from bot.data.news import NewsClient
     from bot.data.manifold import ManifoldClient
-    from bot.data.db import Database
 
 log = logging.getLogger(__name__)
 
@@ -63,81 +58,11 @@ NEWS_LOGODDS_WEIGHT = 1.5
 # Weaker than NEWS_LOGODDS_WEIGHT because Manifold can have illiquid/stale markets.
 MANIFOLD_LOGODDS_WEIGHT = 0.6
 
-
-def _env_bool(name: str, default: bool) -> bool:
-    return os.getenv(name, str(default)).strip().lower() in ("1", "true", "yes", "on")
-
-
-# ── Manifold activation flags ──────────────────────────────────────────────────
-# Manifold has been retired as an ACTIVE trading signal: a per-category coverage
-# audit (see /api/metrics/manifold-coverage) showed coverage_rate=0.0 across every
-# category in the bot's current universe, so any edge it produced was false edge.
-#
-# MANIFOLD_SIGNAL_ENABLED (default False): when False, Manifold is observational
-#   only — its probability never touches the edge model: no manifold_log_adj, no
-#   confidence bump, feat_mfld_lo stays 0.0 (so it can never be the dominant
-#   feature), and it never contributes to a trade.
-# MANIFOLD_AUDIT_ENABLED (default True): when True the matcher still runs and
-#   audit/coverage rows + cooldowns are written, preserving the trail so we can
-#   decide later whether to reactivate Manifold in a universe with real coverage.
-# The matcher is only called when at least one flag is on.
-MANIFOLD_SIGNAL_ENABLED = _env_bool("MANIFOLD_SIGNAL_ENABLED", False)
-MANIFOLD_AUDIT_ENABLED  = _env_bool("MANIFOLD_AUDIT_ENABLED", True)
-
-# ── GNews guardrail (catastrophic fuse) ────────────────────────────────────────
-# Post-mortem NVIDIA 631181: a single strong signal (legacy Manifold 0.13 at
-# weight 0.6) flipped a 0.845 market to 0.431 and lost.  With Manifold now
-# observational-only and macro signals gated behind is_non_price, GNews
-# (weight 1.5) is the only live signal that can move politics markets 20-30 pp
-# against the order-book consensus.  This is NOT a fine calibration — it is a
-# fuse against the extreme case: one uncorroborated signal violently inverting
-# the market.
-#
-# NEWS_GUARDRAIL_ENABLED: master switch for the fuse.
-# MAX_NEWS_ONLY_PROB_SHIFT: when GNews is the ONLY material signal, the final
-#   probability is clamped to prior ± this value.  0.25 still allows a 25 pp
-#   move (edge_net 0.21 after costs) — trades still happen, sizing is bounded.
-# NEWS_MATERIAL_LOGODDS_THRESHOLD: a signal counts as *material* iff its
-#   |log-odds contribution| >= this value.  Below it, a signal is noise and
-#   does NOT count as corroboration.  If ANY other signal (fg, momentum,
-#   btc_dom, manifold) is material, the fuse does not apply.
-NEWS_GUARDRAIL_ENABLED = _env_bool("NEWS_GUARDRAIL_ENABLED", True)
-MAX_NEWS_ONLY_PROB_SHIFT = float(os.getenv("MAX_NEWS_ONLY_PROB_SHIFT", "0.25"))
-NEWS_MATERIAL_LOGODDS_THRESHOLD = float(os.getenv("NEWS_MATERIAL_LOGODDS_THRESHOLD", "0.10"))
-
 # GNews free tier: 100 req/day.  We limit to 5 queries per trading cycle
 # (politics markets only) and rely on 6 h cache to stay within budget.
 MAX_NEWS_QUERIES_PER_CYCLE = 5
 
 
-# ─────────────────────────────────────────────────────────────────────────────
-# Manifold evaluation cooldown
-#
-# Per-market backoff so the trading loop stops re-querying Manifold (and flooding
-# manifold_match_audit) for markets whose verdict is stable.  Computed from the
-# match result; longer for verdicts that essentially never change.
-#   no_results            → 24 h  (Manifold has no market on this topic)
-#   rejected/low_score    → 24 h  (best candidate below Jaccard threshold)
-#   rejected/outcome_mism. → 24 h  (outcome types differ)
-#   rejected/ambiguous    → 24 h  (party named but inversion unverifiable)
-#   rejected/conditional  →  7 d  (premise-gated market; structural, won't change)
-#   accepted              →  1 h  (signal is live; refresh probability hourly)
-# ─────────────────────────────────────────────────────────────────────────────
-def _cooldown_for(result: ManifoldMatchResult) -> tuple[timedelta, str]:
-    """Map a Manifold match result to (retry_delay, cooldown_reason)."""
-    if result.status == "accepted":
-        return timedelta(hours=1), "accepted"
-    if result.status == "no_results":
-        return timedelta(hours=24), "no_results"
-    # rejected — classify by the reason text the matcher produced
-    reason = result.match_reason or "rejected"
-    if "conditional_market" in reason:
-        return timedelta(days=7), reason
-    # outcome_mismatch, ambiguous_inversion, and low_score (jaccard<threshold)
-    # all settle in 24 h.
-    return timedelta(hours=24), reason
-
-
 # ─────────────────────────────────────────────────────────────────────────────
 # Phase 4 — Regime-based minimum edge (uses edge_NET, not edge_gross)
 # ─────────────────────────────────────────────────────────────────────────────
@@ -167,82 +92,24 @@ def _regime_min_edge(category: str, days_to_resolution: int) -> float:
     return 0.10  # tech, crypto/finance, events, default
 
 
-def _days_to_resolution(end_date: str, as_of: Optional[datetime] = None) -> int:
-    """Return calendar days until market resolution, or 30 if unknown.
-
-    as_of (Replay R1): reference clock for the computation.  None (production)
-    means wall-clock now; a replay run passes the archived cycle_ts so
-    days-to-resolution — and therefore the regime edge threshold — is computed
-    against the moment the decision was originally made.
-    """
+def _days_to_resolution(end_date: str) -> int:
+    """Return calendar days until market resolution, or 30 if unknown."""
     if not end_date:
         return 30  # conservative: treat as medium-term
     try:
         dt = datetime.fromisoformat(end_date.replace("Z", "+00:00"))
         if dt.tzinfo is None:
             dt = dt.replace(tzinfo=timezone.utc)
-        now = as_of if as_of is not None else datetime.now(timezone.utc)
-        days = (dt - now).days
+        days = (dt - datetime.now(timezone.utc)).days
         return max(0, days)
     except (ValueError, TypeError):
         return 30
 
 
-def has_token(text: str, token: str) -> bool:
-    """
-    True if `token` appears in `text` as a standalone word.
-
-    Short crypto tickers (eth, sol, ada, …) must NOT match inside ordinary
-    words — "Seth", "dissolved", "Canada" — but must still match the usual
-    market phrasings: "ETH", "$ETH", "ETH/USD", "SOL reach $200".  Boundaries
-    are any non-alphanumeric character (or start/end of string), so "$" and
-    "/" delimit correctly.
-    """
-    return re.search(
-        rf"(?<![A-Za-z0-9]){re.escape(token)}(?![A-Za-z0-9])", text, re.IGNORECASE
-    ) is not None
-
-
 # ─────────────────────────────────────────────────────────────────────────────
 # Phase 3 — GNews priority scoring
 # ─────────────────────────────────────────────────────────────────────────────
 
-def apply_news_guardrail(
-    prior: float,
-    raw_final_prob: float,
-    feat_news_lo: float,
-    other_feats_lo: tuple[float, ...],
-) -> tuple[float, bool]:
-    """
-    GNews guardrail (catastrophic fuse).
-
-    Clamp raw_final_prob to prior ± MAX_NEWS_ONLY_PROB_SHIFT when ALL hold:
-      1. NEWS_GUARDRAIL_ENABLED
-      2. |feat_news_lo| >= NEWS_MATERIAL_LOGODDS_THRESHOLD (news is material)
-      3. every other signal's |log-odds contribution| is below the threshold
-         (GNews is the ONLY material signal — no corroboration)
-
-    Returns (final_prob, guardrail_applied).  guardrail_applied is True only
-    when the clamp actually changed the value; a raw_final_prob already inside
-    the band passes through untouched with applied=False.
-
-    Module globals are read at call time so tests can monkeypatch them.
-    """
-    if not NEWS_GUARDRAIL_ENABLED:
-        return raw_final_prob, False
-    if abs(feat_news_lo) < NEWS_MATERIAL_LOGODDS_THRESHOLD:
-        return raw_final_prob, False
-    if any(abs(v) >= NEWS_MATERIAL_LOGODDS_THRESHOLD for v in other_feats_lo):
-        return raw_final_prob, False  # corroborated — fuse does not apply
-    clamped = min(
-        max(raw_final_prob, prior - MAX_NEWS_ONLY_PROB_SHIFT),
-        prior + MAX_NEWS_ONLY_PROB_SHIFT,
-    )
-    if clamped == raw_final_prob:
-        return raw_final_prob, False
-    return clamped, True
-
-
 def gnews_priority(market: Market, news: "NewsClient") -> float:
     """
     Score a market for GNews query priority (higher = more valuable to query).
@@ -303,19 +170,6 @@ class TradingSignal:
     feat_news_lo:    float = 0.0
     feat_mfld_lo:    float = 0.0
     feat_btc_dom_lo: float = 0.0
-    # ── Manifold match audit (propagated → Order → Trade → DB) ───────────────
-    # mfld_audit_id: UUID of the manifold_match_audit row; used to mark
-    #   used_in_trade=TRUE after executor confirms the trade was executed.
-    mfld_audit_id:    Optional[str]   = None
-    mfld_market_id:   Optional[str]   = None
-    mfld_market_title: Optional[str]  = None
-    mfld_market_url:  Optional[str]   = None
-    mfld_prob_raw:    Optional[float] = None
-    mfld_prob_final:  Optional[float] = None
-    mfld_inverted:    bool            = False
-    mfld_match_score: Optional[float] = None
-    mfld_match_reason: Optional[str]  = None
-    mfld_match_status: Optional[str]  = None
 
 
 class BayesianStrategy:
@@ -347,12 +201,10 @@ class BayesianStrategy:
         self,
         news: Optional["NewsClient"] = None,
         manifold: Optional["ManifoldClient"] = None,
-        db: Optional["Database"] = None,
     ) -> None:
         self._signal_count = 0
         self._news = news
         self._manifold = manifold
-        self._db = db
         self._news_queries_this_cycle = 0
         # Per-cycle counters — reset by reset_cycle(), read by get_cycle_stats()
         self._skip_family: int = 0
@@ -364,13 +216,6 @@ class BayesianStrategy:
         # (edge_gross, edge_net, regime_min) for every market that reached the
         # edge computation stage (passed prior-extreme, family, unsupported filters)
         self._evaluated_edges: list[tuple[float, float, float]] = []
-        # GNews guardrail observability — only markets with material news
-        self._news_shifts: list[float] = []       # final_prob - prior, signed
-        self._news_guardrail_applied: int = 0
-        self._news_changed_decisions: int = 0
-        # Replay R0: per-(market, cycle) decision records, drained by main.py
-        # into the signals table after each evaluation loop.
-        self._cycle_records: list[dict] = []
 
     def reset_cycle(self) -> None:
         """Call once at the start of each trading cycle to reset per-cycle counters."""
@@ -382,54 +227,6 @@ class BayesianStrategy:
         self._manifold_fetched = 0
         self._manifold_on_trade = 0
         self._evaluated_edges = []
-        self._news_shifts = []
-        self._news_guardrail_applied = 0
-        self._news_changed_decisions = 0
-        self._cycle_records = []
-
-    def record_skip(self, market: Market, skip_reason: str) -> None:
-        """Record a skip decided OUTSIDE evaluate() (e.g. reentry_guard in main)."""
-        self._record(market, skip_reason=skip_reason)
-
-    def drain_cycle_records(self) -> list[dict]:
-        """Return and clear this cycle's decision records (Replay R0)."""
-        records, self._cycle_records = self._cycle_records, []
-        return records
-
-    def _record(self, market: Market, skip_reason: Optional[str], **fields) -> None:
-        """Append one decision record. Early skips leave most fields None —
-        the archive still shows the market existed and why it went no further."""
-        rec = {
-            "market_id": market.id,
-            "polymarket_price": market.yes_price,
-            "category": market.category,
-            "volume_24h": market.volume_24h,
-            "skip_reason": skip_reason,
-            "family_key": None,
-            "prior_prob": None,
-            "estimated_prob": None,
-            "raw_final_prob": None,
-            "edge_gross": None,
-            "edge_net": None,
-            "regime_min_edge": None,
-            "days_to_resolution": None,
-            "confidence": None,
-            "direction": None,
-            "passed_gross": None,
-            "passed_net": None,
-            "news_sentiment": None,
-            "news_budget_skipped": None,
-            "guardrail_applied": None,
-            "guardrail_changed_decision": None,
-            "feat_fg_lo": None,
-            "feat_mom_lo": None,
-            "feat_news_lo": None,
-            "feat_mfld_lo": None,
-            "feat_btc_dom_lo": None,
-            "acted_on": False,
-        }
-        rec.update(fields)
-        self._cycle_records.append(rec)
 
     def get_cycle_stats(self) -> dict:
         """Return per-cycle counters for the [CYCLE SUMMARY] log block."""
@@ -449,14 +246,6 @@ class BayesianStrategy:
             "gross_gt_004": sum(1 for g in all_gross if g > 0.04),
             "manifold_matches_accepted": self._manifold_on_trade,
             "manifold_matches_rejected": self._manifold_fetched - self._manifold_on_trade,
-            # GNews guardrail — markets with |news_lo| >= NEWS_MATERIAL_LOGODDS_THRESHOLD
-            "news_with_material":     len(self._news_shifts),
-            "news_avg_shift":         (sum(self._news_shifts) / len(self._news_shifts))
-                                      if self._news_shifts else 0.0,
-            "news_max_shift":         max(self._news_shifts, key=abs)
-                                      if self._news_shifts else 0.0,
-            "news_guardrail_applied": self._news_guardrail_applied,
-            "news_changed_decisions": self._news_changed_decisions,
         }
 
     async def evaluate(
@@ -464,17 +253,10 @@ class BayesianStrategy:
         market: Market,
         ext: ExternalSignals,
         occupied_families: set[str],
-        as_of: Optional[datetime] = None,
     ) -> Optional[TradingSignal]:
         """
         Evaluate a market and return a TradingSignal if actionable.
 
-        as_of (Replay R1): clock injection — None in production (wall-clock
-        now); a replay passes the archived cycle_ts so the regime threshold
-        matches the original decision moment.  Only days-to-resolution
-        depends on the clock; everything else is a pure function of
-        (market, ext, occupied_families) and the news/manifold clients.
-
         Returns None with a structured log line in all skip cases.
         Skip reasons (Phase 5 observability):
           SKIP_UNSUPPORTED  — category not supported
@@ -495,18 +277,13 @@ class BayesianStrategy:
             "below", "under", "less than", "lower", "drop",
         ])
 
-        # Short tickers need word boundaries: "Seth" contains "eth",
-        # "dissolved" contains "sol", "Canada" contains "ada".  Long
-        # unambiguous names (bitcoin, ethereum, …) stay as substrings.
-        is_btc = has_token(question_lower, "btc") or "bitcoin" in question_lower
-        is_eth = has_token(question_lower, "eth") or "ethereum" in question_lower
-        is_sol = has_token(question_lower, "sol") or "solana" in question_lower
-        is_xrp = has_token(question_lower, "xrp") or "ripple" in question_lower
-        is_doge = has_token(question_lower, "doge") or "dogecoin" in question_lower
+        is_btc = "btc" in question_lower or "bitcoin" in question_lower
+        is_eth = "eth" in question_lower or "ethereum" in question_lower
+        is_sol = "sol" in question_lower or "solana" in question_lower
+        is_xrp = "xrp" in question_lower or "ripple" in question_lower
+        is_doge = "doge" in question_lower or "dogecoin" in question_lower
         is_altcoin = is_sol or is_xrp or is_doge or any(
-            has_token(question_lower, t) for t in ["ltc", "bnb", "ada", "avax"]
-        ) or any(
-            w in question_lower for w in ["litecoin", "cardano", "avalanche"]
+            w in question_lower for w in ["ltc", "litecoin", "bnb", "ada", "cardano", "avax", "avalanche"]
         )
         is_general_crypto = any(
             w in question_lower for w in ["crypto", "market cap", "total market", "altcoin", "defi"]
@@ -529,7 +306,6 @@ class BayesianStrategy:
                 "SKIP_UNSUPPORTED  %-50s | cat=%r",
                 market.question[:50], category,
             )
-            self._record(market, skip_reason="unsupported")
             return None
 
         if not ext.valid:
@@ -537,7 +313,6 @@ class BayesianStrategy:
                 "SKIP_NO_SIGNALS   %-50s | reason=external data unavailable",
                 market.question[:50],
             )
-            self._record(market, skip_reason="no_signals")
             return None
 
         # ── Phase 1: prior + prior-extreme filter ────────────────────────────
@@ -549,7 +324,6 @@ class BayesianStrategy:
                 "SKIP_PRIOR_EXTREME %-50s | cat=%-12s | prior=%.3f | reason=prior<0.08",
                 market.question[:50], category, market.yes_price,
             )
-            self._record(market, skip_reason="prior_extreme", prior_prob=prior)
             return None
         if market.yes_price > 0.92:
             self._skip_prior_extreme += 1
@@ -557,7 +331,6 @@ class BayesianStrategy:
                 "SKIP_PRIOR_EXTREME %-50s | cat=%-12s | prior=%.3f | reason=prior>0.92",
                 market.question[:50], category, market.yes_price,
             )
-            self._record(market, skip_reason="prior_extreme", prior_prob=prior)
             return None
 
         # ── Phase 2: family deduplication ────────────────────────────────────
@@ -568,98 +341,76 @@ class BayesianStrategy:
                 "SKIP_FAMILY        %-50s | cat=%-12s | family=%s",
                 market.question[:50], category, family,
             )
-            self._record(market, skip_reason="family", prior_prob=prior, family_key=family)
             return None
 
         # ── Phase 4: regime min-edge ─────────────────────────────────────────
-        days = _days_to_resolution(market.end_date, as_of)
+        days = _days_to_resolution(market.end_date)
         regime_min = _regime_min_edge(category, days)
 
         # ── Bayesian probability estimation ──────────────────────────────────
         sources: list[str] = [f"Prior=poly({prior:.3f})"]
         adjustments: list[float] = []
 
-        # Momentum and Fear & Greed only make sense for price markets, where
-        # is_price_above gives the adjustment a meaningful sign.  For
-        # politics/tech/events there is no above/below notion — is_price_above
-        # defaults to False (or flips on accidental wording like "reach"), so
-        # applying these signals just injected sign noise.  Skip them entirely;
-        # their contributions stay 0.0 → feat_mom_lo / feat_fg_lo = 0.0.
-        is_non_price = is_politics or is_tech or is_events
+        # Signal 1: price momentum (asset-specific or BTC as sentiment proxy)
+        if is_btc:
+            momentum = ext.btc_change_24h
+            asset_label = "BTC"
+        elif is_eth:
+            momentum = ext.eth_change_24h
+            asset_label = "ETH"
+        elif is_politics or is_tech or is_events:
+            momentum = ext.btc_change_24h
+            asset_label = "BTC(sentiment)"
+        else:
+            momentum = ext.total_market_cap_change
+            asset_label = "total mktcap"
 
-        # Signal 1: price momentum (asset-specific; price markets only)
         _momentum_contribution = 0.0
-        if not is_non_price:
-            if is_btc:
-                momentum = ext.btc_change_24h
-                asset_label = "BTC"
-            elif is_eth:
-                momentum = ext.eth_change_24h
-                asset_label = "ETH"
-            else:
-                momentum = ext.total_market_cap_change
-                asset_label = "total mktcap"
+        if abs(momentum) > 2:
+            momentum_adj = math.tanh(momentum / 20) * 0.15
+            if is_politics or is_tech or is_events:
+                momentum_adj *= 0.5
+            _momentum_contribution = momentum_adj if is_price_above else -momentum_adj
+            adjustments.append(_momentum_contribution)
+            sources.append(f"{asset_label} 24h: {momentum:+.1f}%")
 
-            if abs(momentum) > 2:
-                momentum_adj = math.tanh(momentum / 20) * 0.15
-                _momentum_contribution = momentum_adj if is_price_above else -momentum_adj
-                adjustments.append(_momentum_contribution)
-                sources.append(f"{asset_label} 24h: {momentum:+.1f}%")
+        # Signal 2: Fear & Greed
+        fg = ext.fear_greed_index
+        if fg > 70:
+            fg_adj = 0.06
+            sources.append(f"Fear&Greed: {fg} (greed)")
+        elif fg < 30:
+            fg_adj = -0.06
+            sources.append(f"Fear&Greed: {fg} (fear)")
+        else:
+            fg_adj = (fg - 50) / 50 * 0.04
+            sources.append(f"Fear&Greed: {fg} (neutral)")
+        _fg_contribution = fg_adj if is_price_above else -fg_adj
+        adjustments.append(_fg_contribution)
 
-        # Signal 2: Fear & Greed (price markets only)
-        _fg_contribution = 0.0
-        if not is_non_price:
-            fg = ext.fear_greed_index
-            if fg > 70:
-                fg_adj = 0.06
-                sources.append(f"Fear&Greed: {fg} (greed)")
-            elif fg < 30:
-                fg_adj = -0.06
-                sources.append(f"Fear&Greed: {fg} (fear)")
-            else:
-                fg_adj = (fg - 50) / 50 * 0.04
-                sources.append(f"Fear&Greed: {fg} (neutral)")
-            _fg_contribution = fg_adj if is_price_above else -fg_adj
-            adjustments.append(_fg_contribution)
-
-        # Signal 3: BTC dominance — hurts altcoins when high (price markets only)
-        # Like momentum and Fear & Greed above: no demonstrated causality for
-        # politics/tech/events, even when they legitimately mention a ticker
-        # ("Will the ETH ETF be approved?").  For non-price markets the
-        # contribution stays 0.0 → feat_btc_dom_lo = 0.0.
+        # Signal 3: BTC dominance — hurts altcoins when high
         _btc_dom_contribution = 0.0
-        if not is_non_price:
-            if (is_eth or is_altcoin or is_general_crypto) and ext.btc_dominance > 55:
-                _btc_dom_contribution = -0.03 if is_price_above else 0.03
-                adjustments.append(_btc_dom_contribution)
-                sources.append(f"BTC dom: {ext.btc_dominance:.1f}% (high → alt pressure)")
-            elif (is_eth or is_altcoin or is_general_crypto) and ext.btc_dominance < 45:
-                _btc_dom_contribution = 0.03 if is_price_above else -0.03
-                adjustments.append(_btc_dom_contribution)
-                sources.append(f"BTC dom: {ext.btc_dominance:.1f}% (low → alt season)")
+        if (is_eth or is_altcoin or is_general_crypto) and ext.btc_dominance > 55:
+            _btc_dom_contribution = -0.03 if is_price_above else 0.03
+            adjustments.append(_btc_dom_contribution)
+            sources.append(f"BTC dom: {ext.btc_dominance:.1f}% (high → alt pressure)")
+        elif (is_eth or is_altcoin or is_general_crypto) and ext.btc_dominance < 45:
+            _btc_dom_contribution = 0.03 if is_price_above else -0.03
+            adjustments.append(_btc_dom_contribution)
+            sources.append(f"BTC dom: {ext.btc_dominance:.1f}% (low → alt season)")
 
         # Signal 4: GNews sentiment (politics only, budget-gated)
         # Phase 3: caller has pre-sorted markets by gnews_priority() so the
         # highest-value markets reach this block first.
         news_log_adj = 0.0
-        news_sentiment = 0.0
-        # Replay R0: True when GNews was never consulted for this market this
-        # cycle (budget exhausted) — a replay must not read feat_news_lo=0.0 as
-        # "there was no news".
-        news_budget_skipped = False
-        # self._news.enabled gates the whole block: with no GNews API key the
-        # client is a no-op, so we must not consume (or report) query budget for
-        # it — see NewsClient.enabled.
-        if is_politics and self._news is not None and self._news.enabled:
+        if is_politics and self._news is not None:
             if self._news_queries_this_cycle < MAX_NEWS_QUERIES_PER_CYCLE:
                 self._news_queries_this_cycle += 1
                 sentiment = await self._news.get_sentiment(market.question)
                 if abs(sentiment) > 0.05:
-                    news_sentiment = sentiment
                     news_log_adj = sentiment * NEWS_LOGODDS_WEIGHT
                     sources.append(f"GNews: {sentiment:+.2f}")
             else:
-                news_budget_skipped = True
                 log.info(
                     "SKIP_GNEWS_PRIORITY %-50s | reason=cycle budget %d reached",
                     market.question[:50], MAX_NEWS_QUERIES_PER_CYCLE,
@@ -668,129 +419,18 @@ class BayesianStrategy:
         # Signal 5: Manifold cross-market probability (politics + tech)
         # Applies a log-odds adjustment proportional to divergence from prior.
         # No query budget — 30 min cache means network cost is paid once per cycle.
-        # Now uses ManifoldMatchResult for stricter semantic validation and audit.
         manifold_log_adj = 0.0
         manifold_used = False
-        manifold_result: Optional[ManifoldMatchResult] = None
-        audit_id: Optional[str] = None
-
-        if ((is_politics or is_tech) and self._manifold is not None
-                and (MANIFOLD_AUDIT_ENABLED or MANIFOLD_SIGNAL_ENABLED)):
-            # ── Cooldown gate ────────────────────────────────────────────────
-            # Skip markets whose Manifold verdict was recently settled to a
-            # stable value.  A skip is equivalent to a no-signal: the matcher is
-            # NOT called and NO manifold_match_audit row is written, so only real
-            # evaluations are recorded.  See _cooldown_for() and the
-            # manifold_eval_cooldown table.
-            in_cooldown = False
-            if self._db is not None and market.id:
-                try:
-                    cd = await self._db.get_manifold_cooldown(market.id)
-                except Exception as exc:
-                    log.warning("Failed to read manifold cooldown: %s", exc)
-                    cd = None
-                if cd is not None and datetime.now(timezone.utc) < cd["retry_after"]:
-                    in_cooldown = True
-                    log.info(
-                        "MANIFOLD_COOLDOWN skip market=%s | last_status=%s "
-                        "retry_after=%s | %s",
-                        market.id, cd["last_status"],
-                        cd["retry_after"].isoformat(), market.question[:50],
-                    )
-
-            if not in_cooldown:
-                manifold_result = await self._manifold.get_match(market.question)
-
-                # Persist audit record for ALL outcomes (accepted / rejected / no_results).
-                # Gated by MANIFOLD_AUDIT_ENABLED so the audit/coverage trail and
-                # cooldowns can be kept even while Manifold is observational-only.
-                if MANIFOLD_AUDIT_ENABLED and self._db is not None:
-                    if not market.id:
-                        log.error(
-                            "MANIFOLD_AUDIT: market.id is None/empty — skipping audit save | "
-                            "question=%r", market.question[:60],
-                        )
-                    else:
-                        audit_id = str(uuid.uuid4())
-                        try:
-                            await self._db.save_manifold_audit(
-                                audit_id=audit_id,
-                                poly_market_id=market.id,
-                                poly_question=market.question,
-                                search_query=manifold_result.search_query,
-                                mfld_market_id=manifold_result.market_id,
-                                mfld_market_title=manifold_result.market_title,
-                                mfld_market_url=manifold_result.market_url,
-                                prob_raw=manifold_result.prob_raw,
-                                prob_final=manifold_result.prob_final,
-                                inverted=manifold_result.inverted,
-                                match_score=manifold_result.match_score,
-                                match_reason=manifold_result.match_reason,
-                                match_status=manifold_result.status,
-                                poly_outcome_type=manifold_result.poly_outcome_type,
-                                mfld_outcome_type=manifold_result.mfld_outcome_type,
-                                matcher_version=MANIFOLD_MATCHER_VERSION,
-                            )
-                        except Exception as exc:
-                            log.warning("Failed to save manifold audit: %s", exc)
-                            audit_id = None
-
-                    # Record the cooldown so this market is not re-queried every
-                    # cycle.  Written even if the audit save above failed — we
-                    # still performed a real evaluation.
-                    if market.id:
-                        delay, cd_reason = _cooldown_for(manifold_result)
-                        try:
-                            await self._db.upsert_manifold_cooldown(
-                                poly_market_id=market.id,
-                                last_status=manifold_result.status,
-                                retry_after=datetime.now(timezone.utc) + delay,
-                                cooldown_reason=cd_reason,
-                            )
-                        except Exception as exc:
-                            log.warning("Failed to save manifold cooldown: %s", exc)
-
-                # Structured log — both forms for compatibility
-                log.info(
-                    "MANIFOLD_MATCH poly='%s' mfld='%s' score=%s raw=%s final=%s"
-                    " inverted=%s status=%s reason=%s",
-                    market.question, manifold_result.market_title,
-                    manifold_result.match_score, manifold_result.prob_raw,
-                    manifold_result.prob_final, manifold_result.inverted,
-                    manifold_result.status, manifold_result.match_reason,
-                )
-                log.info("MANIFOLD_MATCH", extra={
-                    "poly_question": market.question,
-                    "mfld_title":    manifold_result.market_title,
-                    "score":         manifold_result.match_score,
-                    "prob_raw":      manifold_result.prob_raw,
-                    "prob_final":    manifold_result.prob_final,
-                    "inverted":      manifold_result.inverted,
-                    "status":        manifold_result.status,
-                    "reason":        manifold_result.match_reason,
-                })
-
-                if (MANIFOLD_SIGNAL_ENABLED
-                        and manifold_result.status == "accepted"
-                        and manifold_result.prob_final is not None):
-                    # ACTIVE signal path — only when explicitly enabled.
-                    manifold_used = True
-                    self._manifold_fetched += 1
-                    m_clamped = max(0.05, min(0.95, manifold_result.prob_final))
-                    m_log = math.log(m_clamped / (1 - m_clamped))
-                    p_log = math.log(prior / (1 - prior))
-                    manifold_log_adj = (m_log - p_log) * MANIFOLD_LOGODDS_WEIGHT
-                    sources.append(f"Manifold:{manifold_result.prob_final:.2f}")
-                elif not MANIFOLD_SIGNAL_ENABLED:
-                    # Observational-only: matched/audited but NEVER fed to the edge
-                    # model.  manifold_log_adj stays 0.0 → no confidence bump,
-                    # feat_mfld_lo=0.0 (cannot be dominant), no trade contribution.
-                    log.info(
-                        "Manifold: observational_only — signal disabled "
-                        "(MANIFOLD_SIGNAL_ENABLED=false) | market=%s status=%s",
-                        market.id, manifold_result.status,
-                    )
-                    sources.append("Manifold: observational_only")
+        if (is_politics or is_tech) and self._manifold is not None:
+            manifold_prob = await self._manifold.get_probability(market.question)
+            if manifold_prob is not None:
+                manifold_used = True
+                self._manifold_fetched += 1
+                m_clamped = max(0.05, min(0.95, manifold_prob))
+                m_log = math.log(m_clamped / (1 - m_clamped))
+                p_log = math.log(prior / (1 - prior))
+                manifold_log_adj = (m_log - p_log) * MANIFOLD_LOGODDS_WEIGHT
+                sources.append(f"Manifold:{manifold_prob:.2f}")
 
         # Confidence cap: macro/politics/tech signals are weaker proxies
         confidence_cap = 0.65 if (is_macro or is_politics or is_tech or is_events) else 0.90
@@ -798,31 +438,8 @@ class BayesianStrategy:
         # Posterior via log-odds updating
         log_odds_prior = math.log(prior / (1 - prior))
         total_adj = sum(adjustments)
-        # raw_final_prob: posterior BEFORE the news guardrail.
-        raw_final_prob = _sigmoid(log_odds_prior + total_adj * 2 + news_log_adj + manifold_log_adj)
-        raw_final_prob = max(0.05, min(0.95, raw_final_prob))
-
-        # Per-feature log-odds contributions (Phase 6) — computed here (not
-        # after the edge gate) because the guardrail below needs them to decide
-        # signal materiality.
-        # fg / mom / btc_dom: probability-delta × 2 → log-odds.
-        # news / mfld: already log-odds (LOGODDS_WEIGHT already applied).
-        feat_fg_lo      = _fg_contribution * 2
-        feat_mom_lo     = _momentum_contribution * 2
-        feat_news_lo    = news_log_adj
-        feat_mfld_lo    = manifold_log_adj
-        feat_btc_dom_lo = _btc_dom_contribution * 2
-
-        # ── GNews guardrail (catastrophic fuse) ──────────────────────────────
-        # When GNews is the ONLY material signal, clamp the posterior to
-        # prior ± MAX_NEWS_ONLY_PROB_SHIFT.  estimated_prob (post-guardrail) is
-        # what edge/trading uses; raw_final_prob is kept for observability.
-        estimated_prob, news_guardrail_applied = apply_news_guardrail(
-            prior,
-            raw_final_prob,
-            feat_news_lo,
-            (feat_fg_lo, feat_mom_lo, feat_btc_dom_lo, feat_mfld_lo),
-        )
+        estimated_prob = _sigmoid(log_odds_prior + total_adj * 2 + news_log_adj + manifold_log_adj)
+        estimated_prob = max(0.05, min(0.95, estimated_prob))
 
         # ── Phase 1: edge_gross and edge_net ─────────────────────────────────
         raw_edge = estimated_prob - market.yes_price
@@ -844,6 +461,15 @@ class BayesianStrategy:
         if manifold_log_adj != 0.0:
             confidence = min(confidence_cap, confidence + 0.08)
 
+        # Per-feature log-odds contributions (Phase 6).
+        # fg / mom / btc_dom: probability-delta × 2 → log-odds.
+        # news / mfld: already log-odds (LOGODDS_WEIGHT already applied).
+        feat_fg_lo      = _fg_contribution * 2
+        feat_mom_lo     = _momentum_contribution * 2
+        feat_news_lo    = news_log_adj
+        feat_mfld_lo    = manifold_log_adj
+        feat_btc_dom_lo = _btc_dom_contribution * 2
+
         feat_str = (
             f"fg_lo={feat_fg_lo:+.4f} mom_lo={feat_mom_lo:+.4f} "
             f"news_lo={feat_news_lo:+.4f} mfld_lo={feat_mfld_lo:+.4f} "
@@ -855,80 +481,6 @@ class BayesianStrategy:
         passed_net = edge_net >= regime_min
         can_trade = passed_net and confidence >= MIN_CONFIDENCE
 
-        # ── Guardrail decision impact ────────────────────────────────────────
-        # True when the un-clamped posterior's edge crossed the regime gate but
-        # the clamped one no longer does — i.e. the fuse PREVENTED a trade.
-        # Confidence is invariant under the clamp (it depends only on signal
-        # agreement), so the edge gate is the only component that can flip.
-        guardrail_changed_trade_decision = False
-        if news_guardrail_applied:
-            raw_edge_net = abs(raw_final_prob - market.yes_price) - TOTAL_COST_RATE
-            guardrail_changed_trade_decision = (
-                raw_edge_net >= regime_min and edge_net < regime_min
-            )
-
-        # ── Guardrail observability — ONLY markets with material news ───────
-        # Gated on materiality so the ~145 markets/cycle without news don't
-        # flood the logs.  posterior_before_news = everything except GNews.
-        news_is_material = abs(feat_news_lo) >= NEWS_MATERIAL_LOGODDS_THRESHOLD
-        if news_is_material:
-            posterior_before_news = max(0.05, min(0.95, _sigmoid(
-                log_odds_prior + total_adj * 2 + manifold_log_adj
-            )))
-            self._news_shifts.append(estimated_prob - prior)
-            if news_guardrail_applied:
-                self._news_guardrail_applied += 1
-            if guardrail_changed_trade_decision:
-                self._news_changed_decisions += 1
-            log.info(
-                "NEWS_MATERIAL      %-50s | cat=%-12s | family=%-28s | "
-                "prior=%.3f | before_news=%.3f | raw=%.3f | final=%.3f | "
-                "sent=%+.2f | news_lo=%+.4f | "
-                "edge_before_news=%.3f | edge_after_raw=%.3f | edge_after_guardrail=%.3f | "
-                "guardrail=%s | changed_decision=%s | max_shift=%.2f",
-                market.question[:50], category, family,
-                prior, posterior_before_news, raw_final_prob, estimated_prob,
-                news_sentiment, feat_news_lo,
-                abs(posterior_before_news - market.yes_price),
-                abs(raw_final_prob - market.yes_price),
-                edge_gross,
-                "applied" if news_guardrail_applied else "none",
-                str(guardrail_changed_trade_decision).lower(),
-                MAX_NEWS_ONLY_PROB_SHIFT,
-            )
-
-        # Replay R0: full decision record — same fields for skip and trade paths.
-        # skip_reason granularity: "edge_net" when the edge gate failed,
-        # "confidence" when only the confidence gate blocked the trade.
-        self._record(
-            market,
-            skip_reason=(
-                None if can_trade
-                else ("edge_net" if not passed_net else "confidence")
-            ),
-            family_key=family,
-            prior_prob=prior,
-            estimated_prob=estimated_prob,
-            raw_final_prob=raw_final_prob,
-            edge_gross=edge_gross,
-            edge_net=edge_net,
-            regime_min_edge=regime_min,
-            days_to_resolution=days,
-            confidence=confidence,
-            direction=direction,
-            passed_gross=passed_gross,
-            passed_net=passed_net,
-            news_sentiment=news_sentiment,
-            news_budget_skipped=news_budget_skipped,
-            guardrail_applied=news_guardrail_applied,
-            guardrail_changed_decision=guardrail_changed_trade_decision,
-            feat_fg_lo=feat_fg_lo,
-            feat_mom_lo=feat_mom_lo,
-            feat_news_lo=feat_news_lo,
-            feat_mfld_lo=feat_mfld_lo,
-            feat_btc_dom_lo=feat_btc_dom_lo,
-        )
-
         if not can_trade:
             # Increment the appropriate edge-net counter
             if edge_net <= 0:
@@ -957,21 +509,8 @@ class BayesianStrategy:
             )
             return None
 
-        # When GNews participated, expose raw vs final and the guardrail verdict
-        # (Task 4 of the guardrail spec); otherwise keep the legacy format.
-        if news_log_adj != 0.0:
-            prob_part = (
-                f"Prior=poly({prior:.3f}) → raw={raw_final_prob:.3f} "
-                f"→ final={estimated_prob:.3f} | "
-                f"GNews sent={news_sentiment:+.2f} | "
-                f"guardrail={'applied' if news_guardrail_applied else 'none'} | "
-                f"changed_decision={str(guardrail_changed_trade_decision).lower()} | "
-                f"max_shift={MAX_NEWS_ONLY_PROB_SHIFT:.2f} | "
-            )
-        else:
-            prob_part = f"Prior=poly({prior:.3f}) → estimate={estimated_prob:.3f} | "
         reasoning = (
-            prob_part +
+            f"Prior=poly({prior:.3f}) → estimate={estimated_prob:.3f} | "
             f"Poly price={market.yes_price:.3f} | "
             f"edge_gross={edge_gross:+.3f} | edge_net={edge_net:+.3f} | "
             f"regime_min={regime_min:.2f} | days={days} | "
@@ -1021,21 +560,6 @@ class BayesianStrategy:
             feat_news_lo=feat_news_lo,
             feat_mfld_lo=feat_mfld_lo,
             feat_btc_dom_lo=feat_btc_dom_lo,
-            # Manifold match audit — propagated through Order → Trade → DB.
-            # mfld_audit_id is the hook main.py uses to flip the audit row's
-            # used_in_trade=TRUE; suppress it when observational so the trail
-            # truthfully shows Manifold drove no trades. The mfld_* fields below
-            # stay as observational record (feat_mfld_lo is already 0.0).
-            mfld_audit_id=(audit_id if MANIFOLD_SIGNAL_ENABLED else None),
-            mfld_market_id=manifold_result.market_id if manifold_result else None,
-            mfld_market_title=manifold_result.market_title if manifold_result else None,
-            mfld_market_url=manifold_result.market_url if manifold_result else None,
-            mfld_prob_raw=manifold_result.prob_raw if manifold_result else None,
-            mfld_prob_final=manifold_result.prob_final if manifold_result else None,
-            mfld_inverted=manifold_result.inverted if manifold_result else False,
-            mfld_match_score=manifold_result.match_score if manifold_result else None,
-            mfld_match_reason=manifold_result.match_reason if manifold_result else None,
-            mfld_match_status=manifold_result.status if manifold_result else None,
         )
 
 

dashboard/src/App.jsx

@@ -200,12 +200,8 @@ export default function App() {
         <MetricCard
           title="Sharpe"
           value={fmt(summary.sharpe_ratio)}
-          subtitle={
-            summary.sharpe_ratio == null
-              ? `Muestra insuficiente: ${summary.resolved_count}/${summary.min_resolved_required} resueltos, ${summary.days_observed}/${summary.min_days_required} días`
-              : 'Objetivo ≥ 0.5'
-          }
-          progress={summary.sharpe_ratio == null ? 0 : Math.min(1, summary.sharpe_ratio / 2)}
+          subtitle="Objetivo ≥ 0.5"
+          progress={Math.min(1, summary.sharpe_ratio / 2)}
           progressColor={summary.sharpe_ratio >= 0.5 ? 'var(--green)' : 'var(--amber)'}
         />
         <MetricCard
@@ -220,12 +216,6 @@ export default function App() {
           value={fmtUSD(summary.total_deployed)}
           subtitle={`${summary.total_trades} trades`}
         />
-        <MetricCard
-          title="Cash Disponible"
-          value={fmtUSD(summary.cash_available)}
-          subtitle={`${fmtPct(summary.cash_available / summary.paper_bankroll)} del bankroll`}
-          progress={summary.cash_available / summary.paper_bankroll}
-        />
       </div>
 
       {/* Performance chart */}

tests/conftest.py

@@ -1,10 +0,0 @@
-"""Test environment shims.
-
-The bot runs on python:3.11-slim in production; local dev machines may have
-3.10, which lacks datetime.UTC (added in 3.11). Alias it so modules using
-`from datetime import UTC` import cleanly under 3.10.
-"""
-import datetime
-
-if not hasattr(datetime, "UTC"):
-    datetime.UTC = datetime.timezone.utc

tests/test_api_summary_consistency.py

@@ -1,106 +0,0 @@
-"""
-Tests for bug #7 — /api/summary must agree with the executor's cash model.
-
-Regression: /api/summary computed total_trades as len() over a LIMIT-500
-query (capped once history grows) and reimplemented cash as
-bankroll - sum(net_cost of open trades) from that same capped query.
-
-Fix: counts come from COUNT(*) (compute_metrics_from_db) and cash comes from
-cash_available() — the same helper PaperExecutor.initialize() uses — fed by
-the same source (get_open_position_data). This test runs both consumers
-against one fake DB state and asserts they report identical cash.
-"""
-import asyncio
-
-import pytest
-
-import api.main as api_main
-from bot.executor.paper import PaperExecutor, cash_available
-
-
-BANKROLL = 10_000.0  # PAPER_BANKROLL default used by both bot and API
-
-
-class FakeDB:
-    """One DB state served to both the API endpoint and the executor."""
-
-    def __init__(self, positions: dict[str, float], total_net_cost: float,
-                 total_trades: int, open_count: int):
-        self._positions = positions
-        self._total_net_cost = total_net_cost
-        self._total = total_trades
-        self._open = open_count
-
-    # Shared source: executor.initialize() and /api/summary both call this.
-    async def get_open_position_data(self):
-        return dict(self._positions), self._total_net_cost
-
-    # /api/summary only:
-    async def get_metrics_history(self, days=1):
-        return []
-
-    async def compute_metrics_from_db(self):
-        return {
-            "total_trades": self._total,
-            "open_count": self._open,
-            "closed_count": self._total - self._open,
-            "resolved_count": 0,
-        }
-
-    async def get_recently_closed_inverted(self, hours=24):
-        return set()
-
-    async def get_legacy_incomplete_count(self):
-        return 0
-
-    async def get_daily_pnl_closes(self):
-        return []
-
-
-def _run(db: FakeDB, monkeypatch) -> tuple[dict, PaperExecutor]:
-    monkeypatch.setattr(api_main, "db", db)
-    monkeypatch.delenv("PAPER_BANKROLL", raising=False)
-
-    async def run():
-        summary = await api_main.get_summary()
-        ex = PaperExecutor(db=db, bankroll=BANKROLL)
-        await ex.initialize()
-        return summary, ex
-
-    return asyncio.run(run())
-
-
-def test_api_and_executor_report_same_cash(monkeypatch):
-    db = FakeDB(
-        positions={"m1": 100.0, "m2": 80.0},
-        total_net_cost=183.60,   # 180 + fees
-        total_trades=12,
-        open_count=2,
-    )
-    summary, ex = _run(db, monkeypatch)
-    assert summary["cash_available"] == pytest.approx(ex.get_portfolio().cash)
-    assert summary["cash_available"] == pytest.approx(
-        cash_available(BANKROLL, 183.60)
-    )
-    assert summary["total_deployed"] == pytest.approx(183.60)
-
-
-def test_total_trades_not_capped_by_query_limit(monkeypatch):
-    """700 trades in DB: the old len(LIMIT 500) reported 500."""
-    db = FakeDB(
-        positions={"m1": 100.0},
-        total_net_cost=102.0,
-        total_trades=700,
-        open_count=1,
-    )
-    summary, _ = _run(db, monkeypatch)
-    assert summary["total_trades"] == 700
-    assert summary["open_trades_count"] == 1
-    assert summary["closed_trades_count"] == 699
-
-
-def test_cash_consistency_with_no_open_positions(monkeypatch):
-    db = FakeDB(positions={}, total_net_cost=0.0, total_trades=0, open_count=0)
-    summary, ex = _run(db, monkeypatch)
-    assert summary["cash_available"] == pytest.approx(BANKROLL)
-    assert ex.get_portfolio().cash == pytest.approx(BANKROLL)

tests/test_bayesian_asset_detection.py

@@ -1,159 +0,0 @@
-"""
-Tests for FASE 4 — crypto ticker detection must use word boundaries.
-
-Regression: short tickers were detected with substring matching over
-question_lower, so non-crypto markets triggered crypto flags:
-
-  "Israeli parliament dissolved"  contains "sol" → is_sol / is_altcoin
-  "Will Canada win Group B"       contains "ada" → is_altcoin
-  "Will Seth Moulton be the nominee" contains "eth" → is_eth
-
-Those flags armed the BTC-dominance signal (btc_dom_lo=+0.06 observed in
-production on politics markets).  The fix routes short tickers (btc, eth,
-sol, xrp, doge, ltc, bnb, ada, avax) through has_token(), which requires
-non-alphanumeric boundaries; long unambiguous names (bitcoin, ethereum,
-solana, cardano, …) remain substrings.
-
-The is_* flags are internal to evaluate(), so the integration tests assert
-on btc_dom_lo parsed from the structured audit log (same technique as
-test_bayesian_macro_signals.py), with btc_dominance=60 so the signal fires
-whenever an ETH/altcoin flag is set.
-"""
-import asyncio
-import logging
-import re
-
-import pytest
-
-from bot.data.external import ExternalSignals
-from bot.data.polymarket import Market
-from bot.strategy.bayesian import BayesianStrategy, has_token
-
-BTC_DOM_RE = re.compile(r"btc_dom_lo=([+-]\d+\.\d+)")
-
-
-def _make_market(question: str, category: str) -> Market:
-    return Market(
-        id="mkt-test-1",
-        condition_id="cond-test-1",
-        question=question,
-        yes_token_id="yes-tok",
-        no_token_id="no-tok",
-        yes_price=0.50,
-        no_price=0.50,
-        volume_24h=50_000.0,
-        end_date="2026-07-15T00:00:00Z",
-        active=True,
-        category=category,
-    )
-
-
-def _make_signals() -> ExternalSignals:
-    # btc_dominance=60 (>55) arms the BTC-dominance signal for any market
-    # flagged as ETH / altcoin / general-crypto.
-    return ExternalSignals(
-        btc_price=100_000.0,
-        btc_change_24h=10.0,
-        eth_price=4_000.0,
-        eth_change_24h=8.0,
-        btc_dominance=60.0,
-        fear_greed_index=80,
-        fear_greed_label="greed",
-        total_market_cap_change=5.0,
-        valid=True,
-    )
-
-
-def _evaluate_and_parse_btc_dom(question: str, category: str, caplog) -> float:
-    """Run BayesianStrategy.evaluate and return btc_dom_lo from the audit log."""
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    market = _make_market(question, category)
-    with caplog.at_level(logging.INFO, logger="bot.strategy.bayesian"):
-        asyncio.run(
-            strategy.evaluate(market, _make_signals(), occupied_families=set())
-        )
-    for record in caplog.records:
-        m = BTC_DOM_RE.search(record.getMessage())
-        if m:
-            return float(m.group(1))
-    pytest.fail(
-        "No SKIP_EDGE_NET/TRADE log line with btc_dom_lo found; "
-        f"got: {[r.getMessage() for r in caplog.records]}"
-    )
-
-
-# ── has_token unit tests ─────────────────────────────────────────────────────
-
-def test_has_token_rejects_substrings_inside_words():
-    assert has_token("israeli parliament dissolved by june 30?", "sol") is False
-    assert has_token("will canada win group b?", "ada") is False
-    assert has_token("will seth moulton be the nominee?", "eth") is False
-
-def test_has_token_matches_common_market_formats():
-    assert has_token("will eth hit $5000?", "eth") is True
-    assert has_token("$eth above $5000?", "eth") is True
-    assert has_token("eth/usd above 5000?", "eth") is True
-    assert has_token("will sol reach $200?", "sol") is True
-    assert has_token("will ada reach $1?", "ada") is True
-    assert has_token("BTC to $150k?", "btc") is True  # case-insensitive
-
-
-# ── Regression: false positives must not arm the BTC-dominance signal ───────
-
-def test_israeli_parliament_market_is_not_sol(caplog):
-    """'dissolved' contains 'sol' — must NOT flag is_sol/is_altcoin."""
-    btc_dom_lo = _evaluate_and_parse_btc_dom(
-        "Israeli parliament dissolved by June 30?", "politics", caplog
-    )
-    assert btc_dom_lo == 0.0
-
-def test_canada_market_is_not_ada(caplog):
-    """'Canada' contains 'ada' — must NOT flag is_altcoin."""
-    btc_dom_lo = _evaluate_and_parse_btc_dom(
-        "Will Canada win Group B?", "events", caplog
-    )
-    assert btc_dom_lo == 0.0
-
-def test_seth_moulton_market_is_not_eth(caplog):
-    """'Seth' contains 'eth' — must NOT flag is_eth."""
-    btc_dom_lo = _evaluate_and_parse_btc_dom(
-        "Will Seth Moulton be the nominee?", "politics", caplog
-    )
-    assert btc_dom_lo == 0.0
-
-
-# ── Real ticker mentions must keep working ───────────────────────────────────
-
-def test_eth_market_detected(caplog):
-    """Standalone 'ETH' still flags is_eth: BTC-dom fires and momentum uses ETH."""
-    btc_dom_lo = _evaluate_and_parse_btc_dom(
-        "Will ETH hit $5000?", "crypto/finance", caplog
-    )
-    assert btc_dom_lo != 0.0
-    # Momentum picks the ETH branch only when is_eth is True.
-    full_log = "\n".join(r.getMessage() for r in caplog.records)
-    assert "ETH 24h: +8.0%" in full_log
-
-def test_dollar_eth_market_detected(caplog):
-    """'$ETH' format still flags is_eth."""
-    btc_dom_lo = _evaluate_and_parse_btc_dom(
-        "$ETH above $5000?", "crypto/finance", caplog
-    )
-    assert btc_dom_lo != 0.0
-    full_log = "\n".join(r.getMessage() for r in caplog.records)
-    assert "ETH 24h: +8.0%" in full_log
-
-def test_sol_market_detected(caplog):
-    """'SOL reach $200' still flags is_sol → is_altcoin → BTC-dom signal."""
-    btc_dom_lo = _evaluate_and_parse_btc_dom(
-        "Will SOL reach $200?", "crypto/finance", caplog
-    )
-    # 'reach' → is_price_above, dominance 60 → -0.03 contribution → -0.06 log-odds
-    assert btc_dom_lo == pytest.approx(-0.06, abs=1e-4)
-
-def test_ada_market_detected(caplog):
-    """'ADA reach $1' still flags is_altcoin."""
-    btc_dom_lo = _evaluate_and_parse_btc_dom(
-        "Will ADA reach $1?", "crypto/finance", caplog
-    )
-    assert btc_dom_lo == pytest.approx(-0.06, abs=1e-4)

tests/test_bayesian_btc_dom_gate.py

@@ -1,130 +0,0 @@
-"""
-Tests for FASE 5 — BTC-dominance signal must not apply to non-price markets.
-
-FASE 3 gated momentum and Fear & Greed behind is_non_price (politics / tech /
-events); FASE 4 fixed ticker detection so non-crypto questions no longer flag
-crypto assets by accident.  But a non-price market that LEGITIMATELY mentions
-a ticker ("Will the ETH ETF be approved?") still armed the BTC-dominance
-signal, which has no demonstrated causality for non-price outcomes.  FASE 5
-applies the same is_non_price gate to that signal.
-
-Note: the dominance signal only fires for is_eth / is_altcoin /
-is_general_crypto markets — a pure-BTC question never receives it, so the
-pro-Bitcoin test below is a regression guard rather than a gate exercise;
-the ETH-ETF test is the one that fails without the gate.
-
-Same caplog technique as test_bayesian_asset_detection.py: btc_dom_lo is
-parsed from the structured audit log, with btc_dominance=65 (>55) so the
-signal fires whenever it is allowed to.
-"""
-import asyncio
-import logging
-import re
-
-import pytest
-
-from bot.data.external import ExternalSignals
-from bot.data.polymarket import Market
-from bot.strategy.bayesian import BayesianStrategy
-
-BTC_DOM_RE = re.compile(r"btc_dom_lo=([+-]\d+\.\d+)")
-
-
-def _make_market(question: str, category: str) -> Market:
-    return Market(
-        id="mkt-test-1",
-        condition_id="cond-test-1",
-        question=question,
-        yes_token_id="yes-tok",
-        no_token_id="no-tok",
-        yes_price=0.50,
-        no_price=0.50,
-        volume_24h=50_000.0,
-        end_date="2026-07-15T00:00:00Z",
-        active=True,
-        category=category,
-    )
-
-
-def _make_signals() -> ExternalSignals:
-    # btc_dominance=65 (>55) arms the dominance signal wherever it is allowed.
-    # Momentum kept below the 2% threshold so price-market tests isolate the
-    # dominance contribution.
-    return ExternalSignals(
-        btc_price=100_000.0,
-        btc_change_24h=1.0,
-        eth_price=4_000.0,
-        eth_change_24h=1.0,
-        btc_dominance=65.0,
-        fear_greed_index=50,
-        fear_greed_label="neutral",
-        total_market_cap_change=1.0,
-        valid=True,
-    )
-
-
-def _evaluate(question: str, category: str, caplog) -> tuple[float, str]:
-    """Run evaluate() and return (btc_dom_lo, full_log) from the audit log."""
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    market = _make_market(question, category)
-    with caplog.at_level(logging.INFO, logger="bot.strategy.bayesian"):
-        asyncio.run(
-            strategy.evaluate(market, _make_signals(), occupied_families=set())
-        )
-    full_log = "\n".join(r.getMessage() for r in caplog.records)
-    for record in caplog.records:
-        m = BTC_DOM_RE.search(record.getMessage())
-        if m:
-            return float(m.group(1)), full_log
-    pytest.fail(
-        "No SKIP_EDGE_NET/TRADE log line with btc_dom_lo found; "
-        f"got: {[r.getMessage() for r in caplog.records]}"
-    )
-
-
-# ── Non-price markets: gate must zero the signal ─────────────────────────────
-
-def test_politics_market_mentioning_eth_gets_no_btc_dom(caplog):
-    """Legitimate ETH mention in a politics market → btc_dom_lo == 0.0."""
-    btc_dom_lo, full_log = _evaluate(
-        "Will the ETH ETF be approved?", "politics", caplog
-    )
-    assert btc_dom_lo == 0.0
-    assert "BTC dom" not in full_log
-
-def test_politics_market_mentioning_bitcoin_gets_no_btc_dom(caplog):
-    """Legitimate Bitcoin mention in a politics market → btc_dom_lo == 0.0."""
-    btc_dom_lo, full_log = _evaluate(
-        "Will a pro-Bitcoin candidate win the election?", "politics", caplog
-    )
-    assert btc_dom_lo == 0.0
-    assert "BTC dom" not in full_log
-
-def test_tech_and_events_markets_get_no_btc_dom(caplog):
-    for category in ("tech", "events"):
-        caplog.clear()
-        btc_dom_lo, full_log = _evaluate(
-            "Will the ETH foundation launch the product?", category, caplog
-        )
-        assert btc_dom_lo == 0.0, f"BTC dominance applied to {category} market"
-        assert "BTC dom" not in full_log
-
-
-# ── Price markets: current behavior preserved ───────────────────────────────
-
-def test_eth_price_market_keeps_btc_dom(caplog):
-    """ETH price market with dominance 65 → signal fires as before."""
-    btc_dom_lo, full_log = _evaluate(
-        "Will ETH be above $5000?", "crypto/finance", caplog
-    )
-    # 'above' → is_price_above, dominance 65 > 55 → -0.03 → -0.06 log-odds
-    assert btc_dom_lo == pytest.approx(-0.06, abs=1e-4)
-    assert "BTC dom: 65.0% (high → alt pressure)" in full_log
-
-def test_altcoin_price_market_keeps_btc_dom(caplog):
-    """SOL price market with dominance 65 → signal fires as before."""
-    btc_dom_lo, full_log = _evaluate(
-        "Will SOL reach $200?", "crypto/finance", caplog
-    )
-    assert btc_dom_lo == pytest.approx(-0.06, abs=1e-4)
-    assert "BTC dom: 65.0% (high → alt pressure)" in full_log

tests/test_bayesian_macro_signals.py

@@ -1,123 +0,0 @@
-"""
-Tests for FASE 3 — macro signals (momentum, Fear & Greed) must not apply to
-non-price markets (politics / tech / events).
-
-Regression: for "Will X win the election?"-style questions, is_price_above is
-False, so positive BTC momentum and high Fear & Greed were sign-flipped into
-evidence AGAINST the YES outcome. The fix skips both signals entirely for
-politics/tech/events, leaving their contributions (and feat_mom_lo /
-feat_fg_lo) at 0.0.
-
-evaluate_market only returns a TradingSignal on the TRADE path; on skips it
-returns None but always emits a structured log line containing the per-feature
-log-odds (fg_lo=… mom_lo=…). The tests parse that line via caplog.
-"""
-import asyncio
-import logging
-import math
-import re
-
-import pytest
-
-from bot.data.external import ExternalSignals
-from bot.data.polymarket import Market
-from bot.strategy.bayesian import BayesianStrategy
-
-FEAT_RE = re.compile(r"fg_lo=([+-]\d+\.\d+) mom_lo=([+-]\d+\.\d+)")
-
-
-def _make_market(question: str, category: str) -> Market:
-    return Market(
-        id="mkt-test-1",
-        condition_id="cond-test-1",
-        question=question,
-        yes_token_id="yes-tok",
-        no_token_id="no-tok",
-        yes_price=0.50,
-        no_price=0.50,
-        volume_24h=50_000.0,
-        end_date="2026-07-15T00:00:00Z",
-        active=True,
-        category=category,
-    )
-
-
-def _make_signals() -> ExternalSignals:
-    # Strong bullish macro environment: BTC +10%, extreme greed.
-    return ExternalSignals(
-        btc_price=100_000.0,
-        btc_change_24h=10.0,
-        eth_price=4_000.0,
-        eth_change_24h=8.0,
-        btc_dominance=50.0,
-        fear_greed_index=80,
-        fear_greed_label="greed",
-        total_market_cap_change=5.0,
-        valid=True,
-    )
-
-
-def _evaluate_and_parse_feats(question: str, category: str, caplog) -> tuple[float, float]:
-    """Run BayesianStrategy.evaluate and return (feat_fg_lo, feat_mom_lo) from the audit log."""
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    market = _make_market(question, category)
-    with caplog.at_level(logging.INFO, logger="bot.strategy.bayesian"):
-        asyncio.run(
-            strategy.evaluate(market, _make_signals(), occupied_families=set())
-        )
-    for record in caplog.records:
-        m = FEAT_RE.search(record.getMessage())
-        if m:
-            return float(m.group(1)), float(m.group(2))
-    pytest.fail(
-        "No SKIP_EDGE_NET/TRADE log line with feature contributions found; "
-        f"got: {[r.getMessage() for r in caplog.records]}"
-    )
-
-
-def test_politics_market_ignores_momentum_and_fear_greed(caplog):
-    """Political market with BTC +10% and F&G=80 → both contributions 0.0."""
-    feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
-        "Will John Smith win the election?", "politics", caplog
-    )
-    assert feat_mom_lo == 0.0
-    assert feat_fg_lo == 0.0
-    # The signal sources must not mention momentum or Fear & Greed either.
-    full_log = "\n".join(r.getMessage() for r in caplog.records)
-    assert "Fear&Greed" not in full_log
-    assert "24h" not in full_log
-
-
-def test_tech_and_events_markets_ignore_macro_signals(caplog):
-    for category in ("tech", "events"):
-        caplog.clear()
-        feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
-            "Will the product launch happen this quarter?", category, caplog
-        )
-        assert feat_mom_lo == 0.0, f"momentum applied to {category} market"
-        assert feat_fg_lo == 0.0, f"Fear&Greed applied to {category} market"
-
-
-def test_btc_market_keeps_momentum_and_fear_greed(caplog):
-    """BTC price market with BTC +10% and F&G=80 → current behavior preserved."""
-    feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
-        "Will Bitcoin be above $150,000 on July 1?", "crypto/finance", caplog
-    )
-    assert feat_mom_lo > 0
-    assert feat_fg_lo > 0
-    # Exact values: is_price_above=True ("above"), so contributions are positive.
-    # momentum: tanh(10/20) * 0.15, ×2 → log-odds.  F&G>70: +0.06, ×2 → log-odds.
-    assert feat_mom_lo == pytest.approx(math.tanh(10 / 20) * 0.15 * 2, abs=1e-4)
-    assert feat_fg_lo == pytest.approx(0.06 * 2, abs=1e-4)
-    full_log = "\n".join(r.getMessage() for r in caplog.records)
-    assert "Fear&Greed: 80 (greed)" in full_log
-    assert "BTC 24h: +10.0%" in full_log
-
-
-def test_btc_below_market_sign_flip_preserved(caplog):
-    """'below' market: bullish macro lowers YES probability (sign flip intact)."""
-    feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
-        "Will Bitcoin drop below $50,000 by August?", "crypto/finance", caplog
-    )
-    assert feat_mom_lo < 0
-    assert feat_fg_lo < 0

tests/test_manifold_outcome.py

@@ -1,152 +0,0 @@
-"""
-Tests for the Manifold outcome-compatibility guard.
-
-Regression: a Polymarket *nomination* question must not match a Manifold
-*conditional* question ("If X is the nominee, will he win?") even at Jaccard=1.0.
-"""
-import asyncio
-
-import pytest
-
-from bot.data.manifold import (
-    ManifoldClient,
-    _classify_outcome,
-    _is_conditional,
-)
-
-
-# ── _is_conditional ────────────────────────────────────────────────────────────
-def test_is_conditional_prefixes():
-    assert _is_conditional("If Graham Platner is the nominee, will he win?")
-    assert _is_conditional("Conditional on a recession, will rates fall?")
-    assert _is_conditional("Assuming Trump runs, will he win?")
-    assert _is_conditional("Given that X happens, will Y?")
-
-
-def test_is_conditional_midsentence_clause():
-    assert _is_conditional("Will Biden, if he is nominated, win the election?")
-
-
-def test_is_not_conditional():
-    assert not _is_conditional("Will Graham Platner be the Democratic nominee?")
-    assert not _is_conditional("Will the GOP win the Senate?")
-    # "if" without a closing comma clause is not flagged
-    assert not _is_conditional("What happens if everything goes right")
-
-
-# ── _classify_outcome ───────────────────────────────────────────────────────────
-def test_classify_nomination():
-    assert _classify_outcome("Will X be the Democratic nominee for Senate?") == "nomination"
-    assert _classify_outcome("Will X be nominated?") == "nomination"
-    # "primary nominee" → nomination (checked before primary)
-    assert _classify_outcome("Will X be the primary nominee?") == "nomination"
-
-
-def test_classify_primary_win():
-    assert _classify_outcome("Will X win the primary?") == "primary_win"
-    assert _classify_outcome("Will X advance in the first round?") == "primary_win"
-
-
-def test_classify_general_win():
-    assert _classify_outcome("Will X win the election?") == "general_win"
-    assert _classify_outcome("Will X win the seat?") == "general_win"
-    assert _classify_outcome("Will X win the general election?") == "general_win"
-
-
-def test_classify_conditional():
-    assert _classify_outcome("If X is the nominee, will he win?") == "conditional"
-    assert _classify_outcome("Assuming a runoff, who wins?") == "conditional"
-
-
-def test_classify_other():
-    assert _classify_outcome("Will it rain tomorrow?") == "other"
-
-
-# ── End-to-end get_match with a stubbed Manifold API ────────────────────────────
-class _StubResponse:
-    def __init__(self, payload):
-        self._payload = payload
-
-    def raise_for_status(self):
-        pass
-
-    def json(self):
-        return self._payload
-
-
-class _StubHTTP:
-    def __init__(self, payload):
-        self._payload = payload
-
-    async def get(self, *args, **kwargs):
-        return _StubResponse(self._payload)
-
-    async def aclose(self):
-        pass
-
-
-async def _match(poly, mfld_market):
-    client = ManifoldClient()
-    client._client = _StubHTTP([mfld_market])
-    try:
-        return await client.get_match(poly)
-    finally:
-        await client.close()
-
-
-def test_graham_platner_conditional_rejected():
-    """Poly nomination vs Manifold conditional → rejected (Task 4.1)."""
-    poly = ("Will Graham Platner be the Democratic nominee for Senate "
-            "in Maine in 2026?")
-    mfld_market = {
-        "outcomeType": "BINARY",
-        "probability": 0.55,
-        "question": ("If Graham Platner is the Democratic nominee for Senate "
-                     "in Maine, will he win the general election?"),
-        "id": "abc123",
-        "slug": "graham-platner-win",
-        "creatorUsername": "someone",
-    }
-    result = asyncio.run(_match(poly, mfld_market))
-
-    assert result.status == "rejected"
-    assert result.match_reason is not None
-    assert ("conditional" in result.match_reason
-            or "outcome_mismatch" in result.match_reason)
-    # outcome types are classified and available for persistence
-    assert result.poly_outcome_type == "nomination"
-    assert result.mfld_outcome_type == "conditional"
-
-
-def test_outcome_mismatch_nomination_vs_general_rejected():
-    """Poly nomination vs Manifold general_win (non-conditional) → rejected."""
-    poly = "Will Jane Doe be the Republican nominee for Governor?"
-    mfld_market = {
-        "outcomeType": "BINARY",
-        "probability": 0.4,
-        "question": "Will Jane Doe win the election for Governor?",
-        "id": "x", "slug": "jane-doe", "creatorUsername": "u",
-    }
-    result = asyncio.run(_match(poly, mfld_market))
-
-    assert result.status == "rejected"
-    assert "outcome_mismatch" in result.match_reason
-    assert result.poly_outcome_type == "nomination"
-    assert result.mfld_outcome_type == "general_win"
-
-
-def test_matching_nomination_accepted():
-    """Poly nomination vs Manifold nomination (same outcome) → accepted."""
-    poly = "Will Graham Platner be the Democratic nominee for Senate in Maine?"
-    mfld_market = {
-        "outcomeType": "BINARY",
-        "probability": 0.62,
-        "question": "Will Graham Platner be the Democratic Senate nominee in Maine?",
-        "id": "ok", "slug": "platner-nominee", "creatorUsername": "u",
-    }
-    result = asyncio.run(_match(poly, mfld_market))
-
-    assert result.status == "accepted"
-    assert result.poly_outcome_type == "nomination"
-    assert result.mfld_outcome_type == "nomination"
-    assert result.prob_final == pytest.approx(0.62)

tests/test_news_guardrail.py

@@ -1,247 +0,0 @@
-"""
-Tests for the GNews guardrail (catastrophic fuse).
-
-Post-mortem NVIDIA 631181: one uncorroborated signal at high weight flipped a
-0.845 market to 0.431.  With Manifold observational-only and macro signals
-gated behind is_non_price, GNews is the only live signal able to move politics
-markets 20-30 pp against the order-book consensus.  The fuse clamps the
-posterior to prior ± MAX_NEWS_ONLY_PROB_SHIFT when GNews is the ONLY material
-signal (|log-odds| >= NEWS_MATERIAL_LOGODDS_THRESHOLD); any other material
-signal counts as corroboration and disables the clamp.
-
-Politics markets have no macro adjustments, so full-path tests exercise the
-"GNews only" branch naturally; the corroboration branch is tested through the
-pure helper apply_news_guardrail().
-
-evaluate() emits a NEWS_MATERIAL log line for every market whose news
-contribution is material (trade or skip); tests parse it via caplog.
-"""
-import asyncio
-import logging
-import math
-import re
-
-import pytest
-
-import bot.strategy.bayesian as bayesian
-from bot.data.external import ExternalSignals
-from bot.data.polymarket import Market
-from bot.strategy.bayesian import (
-    NEWS_LOGODDS_WEIGHT,
-    BayesianStrategy,
-    apply_news_guardrail,
-)
-
-NEWS_MATERIAL_RE = re.compile(
-    r"NEWS_MATERIAL.*raw=(\d+\.\d+) \| final=(\d+\.\d+).*"
-    r"guardrail=(applied|none) \| changed_decision=(true|false)"
-)
-
-
-def _logodds(p: float) -> float:
-    return math.log(p / (1 - p))
-
-
-def _sentiment_for(prior: float, target_raw: float) -> float:
-    """Sentiment that moves `prior` to exactly `target_raw` via GNews alone."""
-    return (_logodds(target_raw) - _logodds(prior)) / NEWS_LOGODDS_WEIGHT
-
-
-class FakeNews:
-    """Deterministic NewsClient stub returning a fixed sentiment."""
-
-    enabled = True
-
-    def __init__(self, sentiment: float) -> None:
-        self._sentiment = sentiment
-
-    async def get_sentiment(self, question: str) -> float:
-        return self._sentiment
-
-    def get_freshness(self, question: str) -> float:
-        return 1.0
-
-
-def _make_market(yes_price: float) -> Market:
-    return Market(
-        id="mkt-guardrail-1",
-        condition_id="cond-guardrail-1",
-        question="Will John Smith win the election?",
-        yes_token_id="yes-tok",
-        no_token_id="no-tok",
-        yes_price=yes_price,
-        no_price=1.0 - yes_price,
-        volume_24h=50_000.0,
-        end_date="2026-07-15T00:00:00Z",  # politics <30 d → regime_min 0.08
-        active=True,
-        category="politics",
-    )
-
-
-def _make_signals() -> ExternalSignals:
-    # Neutral macro environment; irrelevant for politics (gated) but explicit.
-    return ExternalSignals(
-        btc_price=100_000.0,
-        btc_change_24h=0.0,
-        eth_price=4_000.0,
-        eth_change_24h=0.0,
-        btc_dominance=50.0,
-        fear_greed_index=50,
-        fear_greed_label="neutral",
-        total_market_cap_change=0.0,
-        valid=True,
-    )
-
-
-def _evaluate(yes_price: float, sentiment: float, caplog) -> tuple[
-    BayesianStrategy, tuple[float, float, str, str]
-]:
-    """Run evaluate() on a politics market and parse the NEWS_MATERIAL line."""
-    strategy = BayesianStrategy(news=FakeNews(sentiment), manifold=None, db=None)
-    market = _make_market(yes_price)
-    with caplog.at_level(logging.INFO, logger="bot.strategy.bayesian"):
-        asyncio.run(strategy.evaluate(market, _make_signals(), occupied_families=set()))
-    for record in caplog.records:
-        m = NEWS_MATERIAL_RE.search(record.getMessage())
-        if m:
-            return strategy, (
-                float(m.group(1)), float(m.group(2)), m.group(3), m.group(4)
-            )
-    pytest.fail(
-        "No NEWS_MATERIAL log line found; got: "
-        f"{[r.getMessage() for r in caplog.records]}"
-    )
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Test 1 — extreme uncorroborated shift: clamp to prior - MAX_NEWS_ONLY_PROB_SHIFT
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_extreme_news_only_shift_is_clamped(caplog):
-    """prior=0.845, raw 0.431 (NVIDIA signature) → final clamped to 0.595."""
-    strategy, (raw, final, guardrail, _) = _evaluate(
-        yes_price=0.845, sentiment=_sentiment_for(0.845, 0.431), caplog=caplog
-    )
-    assert raw == pytest.approx(0.431, abs=1e-3)
-    assert guardrail == "applied"
-    assert final >= 0.595
-    assert final == pytest.approx(0.845 - bayesian.MAX_NEWS_ONLY_PROB_SHIFT, abs=1e-3)
-    assert strategy.get_cycle_stats()["news_guardrail_applied"] == 1
-    assert strategy.get_cycle_stats()["news_with_material"] == 1
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Test 2 — moderate shift inside the band: passes through untouched
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_moderate_news_shift_inside_band_not_clamped(caplog):
-    """prior=0.50, raw 0.62 → within ±0.25 band → final=0.62, no clamp."""
-    strategy, (raw, final, guardrail, _) = _evaluate(
-        yes_price=0.50, sentiment=_sentiment_for(0.50, 0.62), caplog=caplog
-    )
-    assert raw == pytest.approx(0.62, abs=1e-3)
-    assert final == pytest.approx(0.62, abs=1e-3)
-    assert guardrail == "none"
-    assert strategy.get_cycle_stats()["news_guardrail_applied"] == 0
-    # Still counted as a material-news market for the NEWS SUMMARY.
-    assert strategy.get_cycle_stats()["news_with_material"] == 1
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Test 3 — corroboration: any other material signal disables the fuse
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_corroborated_news_not_clamped():
-    """GNews material + another signal >= threshold → raw passes without clamp."""
-    news_lo = _logodds(0.20) - _logodds(0.50)  # ≈ -1.386, clearly material
-    final, applied = apply_news_guardrail(
-        prior=0.50,
-        raw_final_prob=0.20,
-        feat_news_lo=news_lo,
-        other_feats_lo=(0.0, 0.15, 0.0, 0.0),  # one corroborating signal
-    )
-    assert final == 0.20
-    assert applied is False
-
-
-def test_corroboration_threshold_is_inclusive():
-    """|other| == threshold exactly counts as corroboration (>=, not >)."""
-    final, applied = apply_news_guardrail(
-        prior=0.50,
-        raw_final_prob=0.20,
-        feat_news_lo=-1.386,
-        other_feats_lo=(bayesian.NEWS_MATERIAL_LOGODDS_THRESHOLD, 0.0, 0.0, 0.0),
-    )
-    assert final == 0.20
-    assert applied is False
-
-
-def test_uncorroborated_helper_clamps():
-    """Same shift with only noise elsewhere → clamped to prior - 0.25."""
-    final, applied = apply_news_guardrail(
-        prior=0.50,
-        raw_final_prob=0.20,
-        feat_news_lo=-1.386,
-        other_feats_lo=(0.05, -0.09, 0.0, 0.0),  # all below threshold → noise
-    )
-    assert final == pytest.approx(0.25)
-    assert applied is True
-
-
-def test_sub_material_news_never_clamped():
-    """|news_lo| below threshold → fuse not armed, whatever the shift."""
-    final, applied = apply_news_guardrail(
-        prior=0.50,
-        raw_final_prob=0.10,
-        feat_news_lo=0.09,
-        other_feats_lo=(0.0, 0.0, 0.0, 0.0),
-    )
-    assert final == 0.10
-    assert applied is False
-
-
-def test_guardrail_disabled_passthrough(monkeypatch):
-    monkeypatch.setattr(bayesian, "NEWS_GUARDRAIL_ENABLED", False)
-    final, applied = apply_news_guardrail(
-        prior=0.845,
-        raw_final_prob=0.431,
-        feat_news_lo=-1.974,
-        other_feats_lo=(0.0, 0.0, 0.0, 0.0),
-    )
-    assert final == 0.431
-    assert applied is False
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Test 4 — changed_decision: the clamp moves the edge from tradeable to not
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_guardrail_changed_trade_decision(monkeypatch, caplog):
-    """
-    With max_shift=0.10 the clamped edge (0.10 gross, 0.06 net) falls below the
-    politics <30 d regime gate (0.08) while the raw edge (0.414 gross, 0.374
-    net) crossed it → the fuse prevented the trade → changed_decision=true.
-
-    (With the default 0.25 the clamped edge_net is 0.21, above every regime
-    minimum, so the flag can only fire with a tighter configured band.)
-    """
-    monkeypatch.setattr(bayesian, "MAX_NEWS_ONLY_PROB_SHIFT", 0.10)
-    strategy, (raw, final, guardrail, changed) = _evaluate(
-        yes_price=0.845, sentiment=_sentiment_for(0.845, 0.431), caplog=caplog
-    )
-    assert raw == pytest.approx(0.431, abs=1e-3)
-    assert final == pytest.approx(0.745, abs=1e-3)
-    assert guardrail == "applied"
-    assert changed == "true"
-    stats = strategy.get_cycle_stats()
-    assert stats["news_changed_decisions"] == 1
-    assert stats["news_guardrail_applied"] == 1
-
-
-def test_default_band_does_not_change_decision(caplog):
-    """Default 0.25 band: clamp binds but edge_net 0.21 still crosses the gate."""
-    _, (_, _, guardrail, changed) = _evaluate(
-        yes_price=0.845, sentiment=_sentiment_for(0.845, 0.431), caplog=caplog
-    )
-    assert guardrail == "applied"
-    assert changed == "false"

tests/test_news_query.py

@@ -1,77 +0,0 @@
-"""Tests for the GNews layer minor fixes.
-
-Two faults found during the GNews capture/prioritisation diagnostic:
-
-1. Hyphens/dashes in a market question reached the GNews query verbatim and,
-   because '-' is GNews's exclusion operator, produced HTTP 400
-   (e.g. "Abdul El-Sayed Michigan Democratic Primary").
-
-2. The per-cycle GNews budget counter incremented in evaluate() *before*
-   get_sentiment() checked the API key, so with no key configured the
-   [CYCLE SUMMARY] reported a phantom "gnews_queries_used: 5/5" even though
-   zero real requests left the process.
-"""
-import asyncio
-
-from bot.data.news import NewsClient
-from bot.data.external import ExternalSignals
-from bot.data.polymarket import Market
-from bot.strategy.bayesian import BayesianStrategy
-
-
-# ── Fix 1: query sanitisation ────────────────────────────────────────────────
-
-def test_build_query_strips_hyphen_that_breaks_gnews():
-    q = NewsClient._build_query(
-        "Will Abdul El-Sayed win the 2026 Michigan Democratic Primary?"
-    )
-    assert "-" not in q              # the exclusion operator must be gone
-    assert "El-Sayed" not in q
-    assert "Sayed" in q             # the meaningful token survives as its own word
-
-
-def test_build_query_strips_unicode_dashes():
-    q = NewsClient._build_query("Trump–Putin summit — final outcome")
-    assert "–" not in q and "—" not in q
-    assert "Trump" in q and "Putin" in q
-
-
-# ── Fix 2: enabled property + budget accounting ──────────────────────────────
-
-def test_enabled_reflects_api_key(monkeypatch):
-    monkeypatch.delenv("GNEWS_API_KEY", raising=False)
-    assert NewsClient().enabled is False
-    monkeypatch.setenv("GNEWS_API_KEY", "deadbeefdeadbeefdeadbeefdeadbeef")
-    assert NewsClient().enabled is True
-
-
-def _politics_market() -> Market:
-    return Market(
-        id="m1", condition_id="c1",
-        question="Will candidate X win the 2026 governor election?",
-        yes_token_id="y", no_token_id="n",
-        yes_price=0.50, no_price=0.50, volume_24h=10_000.0,
-        end_date="2026-07-15T00:00:00Z", active=True, category="politics",
-    )
-
-
-def _signals() -> ExternalSignals:
-    return ExternalSignals(
-        btc_price=1.0, btc_change_24h=0.0, eth_price=1.0, eth_change_24h=0.0,
-        btc_dominance=50.0, fear_greed_index=50, fear_greed_label="neutral",
-        total_market_cap_change=0.0, valid=True,
-    )
-
-
-def test_disabled_news_consumes_no_gnews_budget(monkeypatch):
-    """Regression: no API key → gnews_queries_used stays 0 (was a phantom 1+)."""
-    monkeypatch.delenv("GNEWS_API_KEY", raising=False)
-    news = NewsClient()
-    assert news.enabled is False
-
-    strategy = BayesianStrategy(news=news, manifold=None, db=None)
-    strategy.reset_cycle()
-    asyncio.run(
-        strategy.evaluate(_politics_market(), _signals(), occupied_families=set())
-    )
-    assert strategy.get_cycle_stats()["gnews_queries_used"] == 0

tests/test_outcomes.py

@@ -1,174 +0,0 @@
-"""Replay R2 tests — outcome fetching and calibration scoring."""
-import asyncio
-import math
-
-import pytest
-
-from bot.data.polymarket import MarketResolution
-from bot.outcomes import (
-    LOGLOSS_EPS,
-    compute_calibration,
-    fetch_outcomes,
-    print_report,
-)
-from datetime import datetime, timezone
-
-
-class FakePoly:
-    """get_market_resolution stand-in driven by a dict of canned responses."""
-
-    def __init__(self, responses: dict):
-        self.responses = responses
-        self.calls: list[str] = []
-
-    async def get_market_resolution(self, market_id: str):
-        self.calls.append(market_id)
-        return self.responses.get(market_id)
-
-
-RESOLVED_AT = datetime(2026, 7, 1, 12, 0, tzinfo=timezone.utc)
-
-
-def _row(market_id="m1", category="politics", est=0.6, prior=0.5, outcome=1.0):
-    return {
-        "market_id": market_id,
-        "category": category,
-        "estimated_prob": est,
-        "prior_prob": prior,
-        "outcome": outcome,
-    }
-
-
-# ── fetch_outcomes ───────────────────────────────────────────────────────────
-
-def test_fetch_keeps_only_definitive_resolutions():
-    poly = FakePoly({
-        "yes":      MarketResolution(resolved=True, resolution=1.0,
-                                     resolved_at=RESOLVED_AT),
-        "no":       MarketResolution(resolved=True, resolution=0.0,
-                                     resolved_at=None),
-        "open":     MarketResolution(resolved=False),
-        "disputed": MarketResolution(resolved=False),
-        "apierror": None,  # get_market_resolution returns None on HTTP errors
-    })
-    out = asyncio.run(
-        fetch_outcomes(poly, ["yes", "no", "open", "disputed", "apierror"])
-    )
-    assert poly.calls == ["yes", "no", "open", "disputed", "apierror"]
-    assert out == [
-        {"market_id": "yes", "outcome": 1.0, "resolved_at": RESOLVED_AT},
-        {"market_id": "no", "outcome": 0.0, "resolved_at": None},
-    ]
-
-
-def test_fetch_empty_list_is_noop():
-    poly = FakePoly({})
-    assert asyncio.run(fetch_outcomes(poly, [])) == []
-    assert poly.calls == []
-
-
-# ── compute_calibration ──────────────────────────────────────────────────────
-
-def test_no_rows_returns_none():
-    assert compute_calibration([]) is None
-
-
-def test_single_row_known_values():
-    m = compute_calibration([_row(est=0.8, prior=0.6, outcome=1.0)])
-    assert m["n_evaluations"] == 1
-    assert m["n_markets"] == 1
-    assert m["brier_model"] == pytest.approx((0.8 - 1.0) ** 2)
-    assert m["brier_prior"] == pytest.approx((0.6 - 1.0) ** 2)
-    assert m["logloss_model"] == pytest.approx(-math.log(0.8))
-    assert m["logloss_prior"] == pytest.approx(-math.log(0.6))
-    # one market: macro == micro
-    assert m["brier_model_macro"] == pytest.approx(m["brier_model"])
-    assert m["brier_prior_macro"] == pytest.approx(m["brier_prior"])
-
-
-def test_logloss_no_outcome_branch():
-    m = compute_calibration([_row(est=0.2, prior=0.7, outcome=0.0)])
-    assert m["logloss_model"] == pytest.approx(-math.log(0.8))
-    assert m["logloss_prior"] == pytest.approx(-math.log(0.3))
-
-
-def test_logloss_clipping_keeps_hard_miss_finite():
-    # A hard 1.0 estimate on a NO outcome must not produce inf.
-    m = compute_calibration([_row(est=1.0, prior=0.5, outcome=0.0)])
-    assert math.isfinite(m["logloss_model"])
-    assert m["logloss_model"] == pytest.approx(-math.log(LOGLOSS_EPS))
-
-
-def test_micro_weights_evaluations_macro_weights_markets():
-    # Market a: 3 evaluations, model error 0.1; market b: 1 evaluation, error 0.5.
-    rows = [
-        _row(market_id="a", est=0.9, prior=0.8, outcome=1.0),
-        _row(market_id="a", est=0.9, prior=0.8, outcome=1.0),
-        _row(market_id="a", est=0.9, prior=0.8, outcome=1.0),
-        _row(market_id="b", est=0.5, prior=0.6, outcome=1.0),
-    ]
-    m = compute_calibration(rows)
-    assert m["n_evaluations"] == 4
-    assert m["n_markets"] == 2
-    # micro: (3*0.01 + 0.25) / 4 ; macro: (0.01 + 0.25) / 2
-    assert m["brier_model"] == pytest.approx((3 * 0.01 + 0.25) / 4)
-    assert m["brier_model_macro"] == pytest.approx((0.01 + 0.25) / 2)
-    assert m["brier_prior"] == pytest.approx((3 * 0.04 + 0.16) / 4)
-    assert m["brier_prior_macro"] == pytest.approx((0.04 + 0.16) / 2)
-
-
-def test_model_beating_market_gives_negative_delta():
-    # est closer to the outcome than the price on every row
-    rows = [
-        _row(market_id="a", est=0.8, prior=0.6, outcome=1.0),
-        _row(market_id="b", est=0.3, prior=0.45, outcome=0.0),
-    ]
-    m = compute_calibration(rows)
-    assert m["brier_model"] < m["brier_prior"]
-    assert m["logloss_model"] < m["logloss_prior"]
-
-
-def test_per_category_grouping_and_unknown():
-    rows = [
-        _row(market_id="a", category="politics", est=0.8, prior=0.6, outcome=1.0),
-        _row(market_id="b", category="politics", est=0.7, prior=0.6, outcome=1.0),
-        _row(market_id="c", category=None, est=0.4, prior=0.5, outcome=0.0),
-    ]
-    m = compute_calibration(rows)
-    assert set(m["per_category"]) == {"politics", "unknown"}
-    pol = m["per_category"]["politics"]
-    assert pol["n"] == 2 and pol["markets"] == 2
-    assert pol["brier_model"] == pytest.approx((0.04 + 0.09) / 2)
-    unk = m["per_category"]["unknown"]
-    assert unk["n"] == 1 and unk["markets"] == 1
-    assert unk["brier_model"] == pytest.approx(0.16)
-
-
-def test_repeated_market_counts_once_in_markets():
-    rows = [
-        _row(market_id="a", est=0.8, prior=0.6, outcome=1.0),
-        _row(market_id="a", est=0.7, prior=0.55, outcome=1.0),
-    ]
-    m = compute_calibration(rows)
-    assert m["n_markets"] == 1
-    assert m["per_category"]["politics"]["markets"] == 1
-
-
-# ── print_report ─────────────────────────────────────────────────────────────
-
-def test_report_handles_no_metrics(capsys):
-    print_report(None, "R0 archive")
-    assert "no scorable rows yet" in capsys.readouterr().out
-
-
-def test_report_prints_all_metric_lines(capsys):
-    m = compute_calibration([
-        _row(market_id="a", est=0.8, prior=0.6, outcome=1.0),
-        _row(market_id="b", category=None, est=0.4, prior=0.5, outcome=0.0),
-    ])
-    print_report(m, "R0 archive")
-    out = capsys.readouterr().out
-    assert "2 evaluations, 2 markets" in out
-    for label in ("Brier micro", "Brier macro", "logloss micro",
-                  "politics", "unknown"):
-        assert label in out

tests/test_paper_close.py

@@ -1,130 +0,0 @@
-"""
-Tests for PaperExecutor.close_position() settlement payout.
-
-Regression: the old code computed cash += position_cost * resolution, which
-ignores direction — a winning BUY_NO (resolution = 0.0) paid out $0.
-
-Correct settlement:
-  BUY_YES: payout = shares * resolution
-  BUY_NO:  payout = shares * (1 - resolution)
-  pnl = payout - net_cost
-"""
-import asyncio
-
-import pytest
-
-from bot.executor import paper
-from bot.executor.paper import PaperExecutor
-
-
-class FakeDB:
-    """Minimal Database stub for close_position()."""
-
-    def __init__(self, trades_by_market: dict[str, list[dict]]):
-        self._trades = trades_by_market
-        self.closed: list[tuple] = []
-
-    async def get_open_trades_for_market(self, market_id: str) -> list[dict]:
-        return self._trades.get(market_id, [])
-
-    async def close_paper_position(self, market_id, reason="", resolution=None):
-        self.closed.append((market_id, reason, resolution))
-
-
-def _close(direction: str, resolution: float):
-    """Open one paper trade (size $100 @ 0.5 → 200 shares, net_cost $102)
-    and settle it at `resolution`. Returns (pnl, executor, notifications)."""
-    notifications: list[tuple] = []
-
-    async def fake_trade_closed(question, pnl):
-        notifications.append((question, pnl))
-
-    async def run():
-        db = FakeDB({
-            "mkt1": [{"direction": direction, "shares": 200.0, "net_cost": 102.0}],
-        })
-        ex = PaperExecutor(db=db, bankroll=1000.0)
-        ex._portfolio.cash = 898.0           # 1000 - net_cost spent at entry
-        ex._portfolio.positions["mkt1"] = 100.0  # size_usdc, as execute() stores it
-
-        original = paper.telegram.trade_closed
-        paper.telegram.trade_closed = fake_trade_closed
-        try:
-            pnl = await ex.close_position("mkt1", resolution, question="Test market?")
-            await asyncio.sleep(0)  # let the notification task run
-        finally:
-            paper.telegram.trade_closed = original
-        return pnl, ex, db
-
-    pnl, ex, db = asyncio.run(run())
-    return pnl, ex, db, notifications
-
-
-def test_buy_yes_wins():
-    pnl, ex, db, notif = _close("BUY_YES", resolution=1.0)
-    assert pnl == pytest.approx(200.0 - 102.0)   # payout = 200 * 1.0
-    assert pnl > 0
-    assert ex._portfolio.cash == pytest.approx(898.0 + 200.0)
-    assert notif[0][1] > 0                        # Telegram reports a win
-
-
-def test_buy_yes_loses():
-    pnl, ex, db, notif = _close("BUY_YES", resolution=0.0)
-    assert pnl == pytest.approx(-102.0)           # payout = 0
-    assert pnl < 0
-    assert ex._portfolio.cash == pytest.approx(898.0)
-    assert notif[0][1] < 0                        # Telegram reports a loss
-
-
-def test_buy_no_wins():
-    pnl, ex, db, notif = _close("BUY_NO", resolution=0.0)
-    assert pnl == pytest.approx(200.0 - 102.0)   # payout = 200 * (1 - 0.0)
-    assert pnl > 0
-    assert ex._portfolio.cash == pytest.approx(898.0 + 200.0)
-    assert notif[0][1] > 0                        # win despite resolution = 0.0
-
-
-def test_buy_no_loses():
-    pnl, ex, db, notif = _close("BUY_NO", resolution=1.0)
-    assert pnl == pytest.approx(-102.0)           # payout = 200 * (1 - 1.0) = 0
-    assert pnl < 0
-    assert ex._portfolio.cash == pytest.approx(898.0)
-    assert notif[0][1] < 0                        # loss despite resolution = 1.0
-
-
-def test_position_is_removed_and_persisted():
-    pnl, ex, db, notif = _close("BUY_YES", resolution=1.0)
-    assert "mkt1" not in ex._portfolio.positions
-    assert db.closed == [("mkt1", "resolved", 1.0)]
-
-
-def test_unknown_market_returns_none():
-    async def run():
-        ex = PaperExecutor(db=FakeDB({}), bankroll=1000.0)
-        return await ex.close_position("nope", 1.0)
-    assert asyncio.run(run()) is None
-
-
-def test_db_failure_keeps_position_for_retry():
-    """Regression: a DB error during close must not mutate the in-memory
-    portfolio — otherwise the next resolution check skips the market
-    (not in positions) and the DB row stays open forever."""
-
-    class FailingDB(FakeDB):
-        async def close_paper_position(self, market_id, reason="", resolution=None):
-            raise RuntimeError("db down")
-
-    async def run():
-        db = FailingDB({
-            "mkt1": [{"direction": "BUY_YES", "shares": 200.0, "net_cost": 102.0}],
-        })
-        ex = PaperExecutor(db=db, bankroll=1000.0)
-        ex._portfolio.cash = 898.0
-        ex._portfolio.positions["mkt1"] = 100.0
-        with pytest.raises(RuntimeError):
-            await ex.close_position("mkt1", 1.0)
-        return ex
-
-    ex = asyncio.run(run())
-    assert ex._portfolio.positions == {"mkt1": 100.0}   # still open in memory
-    assert ex._portfolio.cash == pytest.approx(898.0)   # payout not credited

tests/test_replay_engine.py

@@ -1,367 +0,0 @@
-"""
-Tests for the Replay R1 replay core (bot/replay.py) and the as_of clock
-injection in BayesianStrategy.evaluate().
-
-The central contract is round-trip fidelity: a decision recorded by R0 and
-replayed through replay_cycle() with the same strategy constants must match
-field-for-field (matched=True, mismatch_field=None).  Each round-trip test
-produces the "archive" by running the real evaluate() with FakeNews, then
-replays the drained record as if it had been read back from the signals table.
-"""
-import asyncio
-from datetime import datetime, timedelta, timezone
-
-import pytest
-
-import bot.strategy.bayesian as bayesian
-from bot.data.polymarket import Market, market_family_key
-from bot.strategy.bayesian import BayesianStrategy, _days_to_resolution
-from bot.replay import (
-    ReplayNews,
-    build_ext,
-    build_market,
-    replay_cycle,
-    strategy_config_hash,
-)
-
-from tests.test_news_guardrail import FakeNews, _sentiment_for
-
-
-def _end_date(days_ahead: int = 20) -> str:
-    dt = datetime.now(timezone.utc) + timedelta(days=days_ahead)
-    return dt.strftime("%Y-%m-%dT00:00:00Z")
-
-
-def _make_market(
-    yes_price: float,
-    question: str = "Will John Smith win the election?",
-    category: str = "politics",
-    market_id: str = "mkt-replay-1",
-    end_date: str = None,
-) -> Market:
-    return Market(
-        id=market_id,
-        condition_id="cond-replay-1",
-        question=question,
-        yes_token_id="yes-tok",
-        no_token_id="no-tok",
-        yes_price=yes_price,
-        no_price=1.0 - yes_price,
-        volume_24h=50_000.0,
-        end_date=end_date if end_date is not None else _end_date(),
-        active=True,
-        category=category,
-    )
-
-
-def _snapshot(valid: bool = True) -> dict:
-    """An ext_snapshots row as read back from the DB."""
-    return {
-        "btc_price": 100_000.0,
-        "btc_change_24h": 0.0,
-        "eth_price": 4_000.0,
-        "eth_change_24h": 0.0,
-        "btc_dominance": 50.0,
-        "fear_greed_index": 50,
-        "fear_greed_label": "neutral",
-        "total_market_cap_change": 0.0,
-        "valid": valid,
-    }
-
-
-def _market_row(market: Market) -> dict:
-    """A markets-table row for the given Market."""
-    return {
-        "id": market.id,
-        "condition_id": market.condition_id,
-        "question": market.question,
-        "category": market.category,
-        "end_date": market.end_date,
-    }
-
-
-def _record_with_live_evaluate(
-    market: Market,
-    news=None,
-    families: set = frozenset(),
-) -> dict:
-    """Run the real evaluate() and return the R0 record it produced —
-    the same dict save_signal_records() would have archived."""
-    strategy = BayesianStrategy(news=news, manifold=None, db=None)
-    asyncio.run(strategy.evaluate(market, build_ext(_snapshot()), set(families)))
-    return strategy.drain_cycle_records()[0]
-
-
-def _replay_one(record: dict, market: Market, snapshot: dict = None) -> dict:
-    cycle_ts = datetime.now(timezone.utc)
-    decisions = asyncio.run(replay_cycle(
-        cycle_ts,
-        snapshot or _snapshot(),
-        [record],
-        {market.id: _market_row(market)},
-    ))
-    assert len(decisions) == 1
-    return decisions[0]
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Clock injection
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_days_to_resolution_uses_injected_clock():
-    end = "2026-08-01T00:00:00Z"
-    as_of = datetime(2026, 7, 2, 12, 0, tzinfo=timezone.utc)
-    assert _days_to_resolution(end, as_of) == 29
-    assert _days_to_resolution(end, as_of - timedelta(days=60)) == 89
-
-
-def test_default_clock_is_wall_clock():
-    end = _end_date(days_ahead=40)
-    assert _days_to_resolution(end) == _days_to_resolution(
-        end, datetime.now(timezone.utc)
-    )
-
-
-def test_as_of_changes_regime_threshold():
-    """Same politics market: <30 d out → regime 0.08; replayed from 60 d
-    earlier → regime 0.12.  The clock, not the wall time, must decide."""
-    market = _make_market(0.470)
-    sentiment = _sentiment_for(0.470, 0.601)
-
-    def _regime(as_of):
-        strategy = BayesianStrategy(news=FakeNews(sentiment), manifold=None, db=None)
-        asyncio.run(strategy.evaluate(
-            market, build_ext(_snapshot()), set(), as_of=as_of,
-        ))
-        return strategy.drain_cycle_records()[0]["regime_min_edge"]
-
-    now = datetime.now(timezone.utc)
-    assert _regime(now) == pytest.approx(0.08)
-    assert _regime(now - timedelta(days=60)) == pytest.approx(0.12)
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Round-trip fidelity: record with live evaluate(), replay, expect match
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_roundtrip_confidence_skip():
-    """Georgia signature: edge passes, confidence blocks — full-field match."""
-    sentiment = _sentiment_for(0.470, 0.601)
-    market = _make_market(0.470)
-    record = _record_with_live_evaluate(market, news=FakeNews(sentiment))
-    assert record["skip_reason"] == "confidence"
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is True
-    assert decision["mismatch_field"] is None
-    assert decision["skip_reason"] == "confidence"
-    assert decision["estimated_prob"] == pytest.approx(record["estimated_prob"])
-    assert decision["edge_net"] == pytest.approx(record["edge_net"])
-    assert decision["confidence"] == pytest.approx(record["confidence"])
-    assert decision["direction"] == record["direction"]
-    assert decision["would_trade"] is False
-
-
-def test_roundtrip_edge_net_skip():
-    market = _make_market(0.50)
-    record = _record_with_live_evaluate(market)
-    assert record["skip_reason"] == "edge_net"
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is True
-    assert decision["would_trade"] is False
-
-
-def test_roundtrip_guardrail_clamp():
-    """Clamped posterior must reproduce exactly (raw != final in archive)."""
-    market = _make_market(0.845)
-    record = _record_with_live_evaluate(
-        market, news=FakeNews(_sentiment_for(0.845, 0.431))
-    )
-    assert record["guardrail_applied"] is True
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is True
-    assert decision["raw_final_prob"] == pytest.approx(record["raw_final_prob"])
-    assert decision["estimated_prob"] == pytest.approx(record["estimated_prob"])
-
-
-def test_roundtrip_prior_extreme():
-    market = _make_market(0.03)
-    record = _record_with_live_evaluate(market)
-    assert record["skip_reason"] == "prior_extreme"
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is True
-    assert decision["skip_reason"] == "prior_extreme"
-
-
-def test_roundtrip_family_skip():
-    """Family-skipped rows replay with their own family injected as occupied."""
-    market = _make_market(0.50)
-    record = _record_with_live_evaluate(
-        market, families={market_family_key(market)}
-    )
-    assert record["skip_reason"] == "family"
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is True
-    assert decision["skip_reason"] == "family"
-
-
-def test_roundtrip_unsupported():
-    market = _make_market(0.50, question="Will it rain tomorrow?", category="")
-    record = _record_with_live_evaluate(market)
-    assert record["skip_reason"] == "unsupported"
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is True
-
-
-def test_roundtrip_no_signals():
-    """ext.valid=False archived → replay rebuilds the invalid snapshot."""
-    market = _make_market(0.50)
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    asyncio.run(strategy.evaluate(market, build_ext(_snapshot(valid=False)), set()))
-    record = strategy.drain_cycle_records()[0]
-    assert record["skip_reason"] == "no_signals"
-
-    decision = _replay_one(record, market, snapshot=_snapshot(valid=False))
-    assert decision["matched"] is True
-
-
-def test_roundtrip_trade_path(monkeypatch):
-    """skip_reason=None (tradeable) round-trips with would_trade=True.
-    Politics can't clear MIN_CONFIDENCE=0.55 (the known ceiling), so the
-    gate is lowered for this test only — both record and replay see the
-    same constant, which is exactly the config_hash contract."""
-    monkeypatch.setattr(bayesian, "MIN_CONFIDENCE", 0.45)
-    sentiment = _sentiment_for(0.470, 0.601)
-    market = _make_market(0.470)
-    record = _record_with_live_evaluate(market, news=FakeNews(sentiment))
-    assert record["skip_reason"] is None
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is True
-    assert decision["skip_reason"] is None
-    assert decision["would_trade"] is True
-    assert decision["direction"] == "BUY_YES"
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Replay-specific semantics
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_budget_skipped_row_replays_without_news():
-    """A budget-skipped archive row (sentiment 0.0) must replay to the same
-    no-news decision — and never consume a replay-side budget."""
-    market = _make_market(0.50)
-    strategy = BayesianStrategy(news=FakeNews(0.9), manifold=None, db=None)
-    strategy._news_queries_this_cycle = bayesian.MAX_NEWS_QUERIES_PER_CYCLE
-    asyncio.run(strategy.evaluate(market, build_ext(_snapshot()), set()))
-    record = strategy.drain_cycle_records()[0]
-    assert record["news_budget_skipped"] is True
-    assert record["news_sentiment"] == 0.0
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is True
-    assert decision["estimated_prob"] == pytest.approx(record["estimated_prob"])
-
-
-def test_reentry_guard_row_is_recalibrated_not_compared():
-    """record_skip() rows carry no decision fields; the replay re-evaluates
-    them (calibration data) but marks them non-comparable."""
-    market = _make_market(0.50)
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    strategy.record_skip(market, "reentry_guard")
-    record = strategy.drain_cycle_records()[0]
-
-    decision = _replay_one(record, market)
-    assert decision["matched"] is None
-    assert decision["recorded_skip_reason"] == "reentry_guard"
-    # Re-evaluated on its merits: a full decision despite the recorded skip
-    assert decision["estimated_prob"] is not None
-    assert decision["skip_reason"] == "edge_net"
-
-
-def test_missing_market_row_flagged_not_crashed():
-    market = _make_market(0.50)
-    record = _record_with_live_evaluate(market)
-
-    decisions = asyncio.run(replay_cycle(
-        datetime.now(timezone.utc), _snapshot(), [record], {},
-    ))
-    assert decisions[0]["matched"] is False
-    assert decisions[0]["mismatch_field"] == "market_missing"
-
-
-def test_mismatch_detected_when_config_differs(monkeypatch):
-    """Counterfactual sanity: replaying under a different guardrail band
-    must produce matched=False with the diverging field named."""
-    market = _make_market(0.845)
-    record = _record_with_live_evaluate(
-        market, news=FakeNews(_sentiment_for(0.845, 0.431))
-    )
-    assert record["guardrail_applied"] is True
-
-    monkeypatch.setattr(bayesian, "MAX_NEWS_ONLY_PROB_SHIFT", 0.10)
-    decision = _replay_one(record, market)
-    assert decision["matched"] is False
-    # Tighter clamp (prior 0.845 ± 0.10 → est 0.745): edge_net drops from
-    # 0.21 to 0.06 < regime 0.08, so the skip flips confidence → edge_net
-    # and skip_reason is the first field _compare() sees diverge.
-    assert decision["mismatch_field"] == "skip_reason"
-    assert decision["skip_reason"] == "edge_net"
-
-
-def test_multi_row_cycle_preserves_order_and_isolation():
-    """Rows replay independently within a cycle: a family skip and a full
-    evaluation with different sentiments don't bleed into each other."""
-    m1 = _make_market(0.470, market_id="m1")
-    m2 = _make_market(
-        0.50, market_id="m2",
-        question="Will Jane Doe win the Georgia Senate race?",
-    )
-    r1 = _record_with_live_evaluate(m1, news=FakeNews(_sentiment_for(0.470, 0.601)))
-    r2 = _record_with_live_evaluate(m2)  # no news → edge_net skip
-
-    decisions = asyncio.run(replay_cycle(
-        datetime.now(timezone.utc),
-        _snapshot(),
-        [r1, r2],
-        {"m1": _market_row(m1), "m2": _market_row(m2)},
-    ))
-    assert [d["market_id"] for d in decisions] == ["m1", "m2"]
-    assert all(d["matched"] is True for d in decisions)
-    assert decisions[0]["skip_reason"] == "confidence"
-    assert decisions[1]["skip_reason"] == "edge_net"
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Run tagging
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_config_hash_stable_and_sensitive(monkeypatch):
-    h1 = strategy_config_hash()
-    assert strategy_config_hash() == h1
-    monkeypatch.setattr(bayesian, "MAX_NEWS_ONLY_PROB_SHIFT", 0.10)
-    assert strategy_config_hash() != h1
-
-
-def test_replay_news_returns_current_sentiment():
-    news = ReplayNews()
-    assert asyncio.run(news.get_sentiment("q")) == 0.0
-    news.sentiment = -0.42
-    assert asyncio.run(news.get_sentiment("q")) == -0.42
-
-
-def test_build_market_reconstruction():
-    market = _make_market(0.37)
-    record = _record_with_live_evaluate(market)
-    rebuilt = build_market(_market_row(market), record)
-    assert rebuilt.id == market.id
-    assert rebuilt.yes_price == pytest.approx(0.37)
-    assert rebuilt.volume_24h == pytest.approx(market.volume_24h)
-    assert rebuilt.end_date == market.end_date
-    assert rebuilt.category == "politics"
-    assert market_family_key(rebuilt) == market_family_key(market)

tests/test_resolution_detector.py

@@ -1,219 +0,0 @@
-"""
-Tests for the automatic market-resolution detector (Phase 2).
-
-Covers:
-  - PolymarketClient.get_market_resolution() parsing of real Gamma API shapes
-    (resolved YES/NO, still open, UMA-disputed, ambiguous prices, 404, errors).
-  - check_resolutions() in bot/main.py: a resolved market settles the open
-    paper position via PaperExecutor.close_position() and persists
-    close_reason='resolved' with the resolution value.
-"""
-import asyncio
-import json
-
-import httpx
-import pytest
-
-from bot.data.polymarket import PolymarketClient, MarketResolution
-from bot.executor import paper
-from bot.executor.paper import PaperExecutor
-from bot.main import check_resolutions
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# get_market_resolution() — Gamma API response parsing
-# ─────────────────────────────────────────────────────────────────────────────
-
-class FakeResponse:
-    def __init__(self, status_code: int, payload: dict | None = None):
-        self.status_code = status_code
-        self._payload = payload or {}
-
-    def json(self):
-        return self._payload
-
-    def raise_for_status(self):
-        if self.status_code >= 400:
-            raise httpx.HTTPStatusError(
-                f"HTTP {self.status_code}", request=None, response=None
-            )
-
-
-class FakeHTTPClient:
-    def __init__(self, response):
-        self._response = response
-        self.requested_urls: list[str] = []
-
-    async def get(self, url, **kwargs):
-        self.requested_urls.append(url)
-        if isinstance(self._response, Exception):
-            raise self._response
-        return self._response
-
-
-def _resolution_for(response) -> MarketResolution | None:
-    client = PolymarketClient()
-    client._client = FakeHTTPClient(response)
-    return asyncio.run(client.get_market_resolution("12345"))
-
-
-def _gamma_market(closed: bool, yes_price: str, no_price: str,
-                  uma_status: str | None = "resolved") -> dict:
-    """Mirror the real Gamma /markets/{id} payload shape (observed 2026-06-11)."""
-    m = {
-        "id": "12345",
-        "question": "Test market?",
-        "closed": closed,
-        "active": True,
-        "outcomePrices": json.dumps([yes_price, no_price]),
-        "closedTime": "2026-06-11 13:15:01+00" if closed else None,
-        "umaEndDate": "2026-06-11T13:15:01Z" if closed else None,
-        "endDate": "2026-06-11T13:00:00Z",
-    }
-    if uma_status is not None:
-        m["umaResolutionStatus"] = uma_status
-    return m
-
-
-def test_resolution_no_won():
-    res = _resolution_for(FakeResponse(200, _gamma_market(True, "0", "1")))
-    assert res.resolved is True
-    assert res.resolution == 0.0
-    assert res.resolved_at is not None
-
-
-def test_resolution_yes_won():
-    res = _resolution_for(FakeResponse(200, _gamma_market(True, "1", "0")))
-    assert res.resolved is True
-    assert res.resolution == 1.0
-
-
-def test_open_market_not_resolved():
-    res = _resolution_for(FakeResponse(
-        200, _gamma_market(False, "0.51", "0.49", uma_status=None)
-    ))
-    assert res.resolved is False
-    assert res.resolution is None
-
-
-def test_closed_but_uma_disputed_not_settled():
-    res = _resolution_for(FakeResponse(
-        200, _gamma_market(True, "0", "1", uma_status="disputed")
-    ))
-    assert res.resolved is False
-
-
-def test_closed_with_ambiguous_prices_not_settled():
-    res = _resolution_for(FakeResponse(200, _gamma_market(True, "0.6", "0.4")))
-    assert res.resolved is False
-
-
-def test_market_not_found_returns_none():
-    assert _resolution_for(FakeResponse(404)) is None
-
-
-def test_api_error_returns_none():
-    assert _resolution_for(httpx.ConnectError("boom")) is None
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# check_resolutions() — detector loop settles paper positions
-# ─────────────────────────────────────────────────────────────────────────────
-
-class FakeDB:
-    """Database stub: one open BUY_NO paper position."""
-
-    def __init__(self, trades_by_market: dict[str, list[dict]]):
-        self._trades = trades_by_market
-        self.closed: list[tuple] = []
-
-    async def get_open_position_details(self) -> list[dict]:
-        return [
-            {"market_id": mid, "question": t[0].get("question", ""),
-             "direction": t[0]["direction"]}
-            for mid, t in self._trades.items()
-        ]
-
-    async def get_open_trades_for_market(self, market_id: str) -> list[dict]:
-        return self._trades.get(market_id, [])
-
-    async def close_paper_position(self, market_id, reason="", resolution=None):
-        self.closed.append((market_id, reason, resolution))
-
-
-class FakePoly:
-    def __init__(self, resolutions: dict[str, MarketResolution | None]):
-        self._resolutions = resolutions
-        self.checked: list[str] = []
-
-    async def get_market_resolution(self, market_id: str):
-        self.checked.append(market_id)
-        return self._resolutions.get(market_id)
-
-
-def _run_check(resolutions: dict, trades: dict):
-    notifications: list[tuple] = []
-
-    async def fake_trade_closed(question, pnl):
-        notifications.append((question, pnl))
-
-    async def run():
-        db = FakeDB(trades)
-        ex = PaperExecutor(db=db, bankroll=1000.0)
-        for mid, t in trades.items():
-            ex._portfolio.positions[mid] = sum(x["net_cost"] for x in t) - 2.0
-        ex._portfolio.cash = 898.0
-        poly = FakePoly(resolutions)
-
-        original = paper.telegram.trade_closed
-        paper.telegram.trade_closed = fake_trade_closed
-        try:
-            await check_resolutions(poly, ex, db)
-            await asyncio.sleep(0)  # let notification task run
-        finally:
-            paper.telegram.trade_closed = original
-        return db, ex, poly
-
-    db, ex, poly = asyncio.run(run())
-    return db, ex, poly, notifications
-
-
-BUY_NO_TRADE = {
-    "mkt1": [{
-        "direction": "BUY_NO", "shares": 200.0, "net_cost": 102.0,
-        "question": "Will X happen?",
-    }],
-}
-
-
-def test_resolved_buy_no_position_is_closed():
-    """BUY_NO position + market resolved NO (resolution=0.0) → winning close."""
-    db, ex, poly, notif = _run_check(
-        {"mkt1": MarketResolution(resolved=True, resolution=0.0)},
-        BUY_NO_TRADE,
-    )
-    assert poly.checked == ["mkt1"]
-    # close_paper_position called with close_reason='resolved' and the resolution
-    assert db.closed == [("mkt1", "resolved", 0.0)]
-    # Position removed and payout credited: 200 shares * (1 - 0.0) = $200
-    assert "mkt1" not in ex._portfolio.positions
-    assert ex._portfolio.cash == pytest.approx(898.0 + 200.0)
-    # Telegram notified with positive pnl (200 - 102)
-    assert notif == [("Will X happen?", pytest.approx(98.0))]
-
-
-def test_unresolved_position_stays_open():
-    db, ex, poly, notif = _run_check(
-        {"mkt1": MarketResolution(resolved=False)},
-        BUY_NO_TRADE,
-    )
-    assert poly.checked == ["mkt1"]
-    assert db.closed == []
-    assert "mkt1" in ex._portfolio.positions
-    assert notif == []
-
-
-def test_api_failure_leaves_position_open():
-    db, ex, poly, notif = _run_check({"mkt1": None}, BUY_NO_TRADE)
-    assert db.closed == []
-    assert "mkt1" in ex._portfolio.positions

tests/test_sharpe_gate.py

@@ -1,242 +0,0 @@
-"""
-Tests for the real Sharpe ratio with minimum-sample gate.
-
-Regression: sharpe_ratio was hardcoded to 0.0 in MetricsTracker and exposed
-as `latest.get("sharpe_ratio") or 0` in /api/summary, and promotion_ready
-could in principle flip on a statistically meaningless sample (e.g. 1
-resolved trade over ~40 days of flat PnL plus a single +299 jump).
-
-Fix: bot/metrics/sharpe.py computes an annualized Sharpe from the daily
-total_pnl close series, gated to None ("insufficient_sample") below 30 days
-observed / 10 resolved trades. /api/summary exposes the value plus an
-explanation (sharpe_status, days_observed, min_* fields), and
-promotion_ready additionally requires the sample minimums and non-null
-metrics.
-"""
-import asyncio
-from statistics import mean, stdev
-
-import pytest
-
-import api.main as api_main
-from bot.metrics.sharpe import (
-    MIN_DAYS_OBSERVED,
-    MIN_RESOLVED_TRADES,
-    SHARPE_INSUFFICIENT,
-    SHARPE_OK,
-    SHARPE_ZERO_VARIANCE,
-    compute_sharpe,
-    daily_returns,
-    sharpe_with_gate,
-)
-from bot.metrics.tracker import MetricsTracker
-
-
-BANKROLL = 10_000.0
-
-
-def _closes_from_deltas(deltas: list[float], start: float = 0.0) -> list[float]:
-    closes = [start]
-    for d in deltas:
-        closes.append(closes[-1] + d)
-    return closes
-
-
-# ── Pure computation ─────────────────────────────────────────────────────────
-
-def test_daily_returns_are_bankroll_normalized_deltas():
-    closes = [0.0, 100.0, 50.0, 50.0]
-    assert daily_returns(closes, BANKROLL) == pytest.approx([0.01, -0.005, 0.0])
-
-
-def test_compute_sharpe_matches_manual_formula():
-    deltas = [10.0, 14.0, 8.0, 12.0, 6.0, 13.0, 9.0]
-    closes = _closes_from_deltas(deltas)
-    rets = [d / BANKROLL for d in deltas]
-    expected = mean(rets) / stdev(rets) * 365 ** 0.5
-    assert compute_sharpe(closes, BANKROLL) == pytest.approx(expected)
-    assert compute_sharpe(closes, BANKROLL) > 0
-
-
-def test_compute_sharpe_undefined_cases_return_none():
-    assert compute_sharpe([], BANKROLL) is None
-    assert compute_sharpe([0.0], BANKROLL) is None
-    assert compute_sharpe([0.0, 50.0], BANKROLL) is None      # only 1 return
-    assert compute_sharpe([0.0] * 40, BANKROLL) is None        # zero variance
-
-
-# ── Minimum-sample gate ───────────────────────────────────────────────────────
-
-def test_gate_blocks_current_situation_one_resolved_trade():
-    """~40 flat days plus a single +299 jump, 1 resolved trade → no Sharpe."""
-    closes = [0.0] * 35 + [299.06] * 5
-    sharpe, status = sharpe_with_gate(closes, BANKROLL, resolved_count=1)
-    assert sharpe is None
-    assert status == SHARPE_INSUFFICIENT
-    # The raw (ungated) value would exist and be wildly misleading:
-    assert compute_sharpe(closes, BANKROLL) is not None
-
-
-def test_gate_blocks_too_few_days_even_with_enough_resolved():
-    closes = _closes_from_deltas([10.0, -5.0] * 10)  # 21 days < 30
-    sharpe, status = sharpe_with_gate(closes, BANKROLL, resolved_count=15)
-    assert sharpe is None
-    assert status == SHARPE_INSUFFICIENT
-
-
-def test_gate_passes_with_sufficient_sample():
-    deltas = [10.0, 14.0, 8.0, 12.0, 6.0] * 8  # 40 returns → 41 days
-    closes = _closes_from_deltas(deltas)
-    sharpe, status = sharpe_with_gate(closes, BANKROLL, resolved_count=MIN_RESOLVED_TRADES)
-    assert status == SHARPE_OK
-    assert sharpe == pytest.approx(compute_sharpe(closes, BANKROLL))
-
-
-def test_gate_flat_curve_with_sufficient_sample_is_zero_variance():
-    sharpe, status = sharpe_with_gate([0.0] * 40, BANKROLL, resolved_count=12)
-    assert sharpe is None
-    assert status == SHARPE_ZERO_VARIANCE
-
-
-# ── /api/summary ─────────────────────────────────────────────────────────────
-
-class FakeDB:
-    def __init__(self, daily_closes, resolved_count, total_trades=60,
-                 win_rate=0.6, calibration=0.8):
-        self._closes = daily_closes
-        self._resolved = resolved_count
-        self._total = total_trades
-        self._win_rate = win_rate
-        self._calibration = calibration
-
-    async def get_metrics_history(self, days=1):
-        return [{
-            "win_rate": self._win_rate,
-            "calibration_score": self._calibration,
-            "unrealized_pnl_est": 0.0,
-            "realized_pnl": 299.06,
-            "total_pnl": 299.06,
-        }]
-
-    async def compute_metrics_from_db(self):
-        return {
-            "total_trades": self._total,
-            "open_count": self._total - self._resolved,
-            "closed_count": self._resolved,
-            "resolved_count": self._resolved,
-        }
-
-    async def get_open_position_data(self):
-        return {}, 0.0
-
-    async def get_recently_closed_inverted(self, hours=24):
-        return set()
-
-    async def get_legacy_incomplete_count(self):
-        return 0
-
-    async def get_daily_pnl_closes(self):
-        return list(self._closes)
-
-
-def _summary(db, monkeypatch) -> dict:
-    monkeypatch.setattr(api_main, "db", db)
-    monkeypatch.delenv("PAPER_BANKROLL", raising=False)
-    return asyncio.run(api_main.get_summary())
-
-
-def test_api_insufficient_sample_returns_null_with_explanation(monkeypatch):
-    """Current prod situation: 1 resolved, ~40 days → null Sharpe, not ready."""
-    db = FakeDB(daily_closes=[0.0] * 35 + [299.06] * 5, resolved_count=1)
-    s = _summary(db, monkeypatch)
-    assert s["sharpe_ratio"] is None
-    assert s["sharpe_status"] == SHARPE_INSUFFICIENT
-    assert s["resolved_count"] == 1
-    assert s["min_resolved_required"] == MIN_RESOLVED_TRADES == 10
-    assert s["days_observed"] == 40
-    assert s["min_days_required"] == MIN_DAYS_OBSERVED == 30
-    # One lucky resolved trade must never promote, even with perfect
-    # win_rate/calibration and 50+ trades.
-    assert s["promotion_ready"] is False
-
-
-def test_api_sharpe_appears_with_sufficient_sample(monkeypatch):
-    deltas = [10.0, 14.0, 8.0, 12.0, 6.0] * 8
-    db = FakeDB(daily_closes=_closes_from_deltas(deltas), resolved_count=12)
-    s = _summary(db, monkeypatch)
-    assert s["sharpe_status"] == SHARPE_OK
-    assert s["sharpe_ratio"] == pytest.approx(
-        compute_sharpe(_closes_from_deltas(deltas), BANKROLL)
-    )
-    assert s["sharpe_ratio"] >= 0.5
-    assert s["promotion_ready"] is True
-
-
-def test_api_not_ready_when_sharpe_below_threshold(monkeypatch):
-    # Zero-drift curve: mean return ~0 → Sharpe ≈ 0 < 0.5
-    deltas = [50.0, -50.0] * 20
-    db = FakeDB(daily_closes=_closes_from_deltas(deltas), resolved_count=12)
-    s = _summary(db, monkeypatch)
-    assert s["sharpe_status"] == SHARPE_OK
-    assert s["sharpe_ratio"] < 0.5
-    assert s["promotion_ready"] is False
-
-
-def test_api_not_ready_when_metrics_null(monkeypatch):
-    db = FakeDB(
-        daily_closes=_closes_from_deltas([10.0, 14.0, 8.0, 12.0, 6.0] * 8),
-        resolved_count=12,
-        win_rate=None,
-        calibration=None,
-    )
-    s = _summary(db, monkeypatch)
-    assert s["sharpe_status"] == SHARPE_OK
-    assert s["promotion_ready"] is False
-
-
-# ── MetricsTracker: no hardcoded 0.0 in the snapshot ─────────────────────────
-
-class FakeTrackerDB:
-    def __init__(self, daily_closes, resolved_count):
-        self._closes = daily_closes
-        self._resolved = resolved_count
-        self.saved = None
-
-    async def compute_metrics_from_db(self):
-        return {
-            "total_trades": 60,
-            "open_count": 40,
-            "closed_count": 20,
-            "resolved_count": self._resolved,
-            "wins_realized": self._resolved,
-            "unrealized_pnl_est": 0.0,
-            "realized_pnl": 100.0,
-            "total_deployed": 1000.0,
-            "total_fees": 20.0,
-            "calibration_score": 0.8,
-        }
-
-    async def get_daily_pnl_closes(self):
-        return list(self._closes)
-
-    async def save_daily_metrics(self, metrics):
-        self.saved = metrics
-
-
-def test_tracker_stores_null_sharpe_below_gate(monkeypatch):
-    monkeypatch.delenv("PAPER_BANKROLL", raising=False)
-    db = FakeTrackerDB(daily_closes=[0.0] * 35 + [299.06] * 5, resolved_count=1)
-    asyncio.run(MetricsTracker(db).update_daily_summary())
-    assert db.saved is not None
-    assert db.saved["sharpe_ratio"] is None
-
-
-def test_tracker_stores_real_sharpe_above_gate(monkeypatch):
-    monkeypatch.delenv("PAPER_BANKROLL", raising=False)
-    closes = _closes_from_deltas([10.0, 14.0, 8.0, 12.0, 6.0] * 8)
-    db = FakeTrackerDB(daily_closes=closes, resolved_count=12)
-    asyncio.run(MetricsTracker(db).update_daily_summary())
-    assert db.saved["sharpe_ratio"] == pytest.approx(
-        compute_sharpe(closes, BANKROLL)
-    )
-    assert db.saved["sharpe_ratio"] != 0.0

tests/test_signal_recorder.py

@@ -1,224 +0,0 @@
-"""
-Tests for the Replay R0 snapshot recorder (strategy-side record accumulation).
-
-Every evaluate() call must leave exactly one record in _cycle_records, whatever
-exit path it takes, so the signals archive is a complete account of each cycle.
-DB persistence itself (save_signal_records) is exercised in prod; these tests
-cover the record-building contract the replay engine will rely on:
-
-  - one record per market per evaluate() call, drained per cycle
-  - skip_reason granularity (prior_extreme / family / edge_net / confidence /
-    unsupported / reentry_guard via record_skip)
-  - full input/output fields on records that reached edge computation
-  - news_budget_skipped distinguishes "not asked" from "no news"
-"""
-import asyncio
-from datetime import datetime, timedelta, timezone
-
-import pytest
-
-import bot.strategy.bayesian as bayesian
-from bot.data.external import ExternalSignals
-from bot.data.polymarket import Market
-from bot.strategy.bayesian import (
-    MAX_NEWS_QUERIES_PER_CYCLE,
-    BayesianStrategy,
-)
-
-from tests.test_news_guardrail import FakeNews, _sentiment_for
-
-
-def _end_date(days_ahead: int = 20) -> str:
-    dt = datetime.now(timezone.utc) + timedelta(days=days_ahead)
-    return dt.strftime("%Y-%m-%dT00:00:00Z")
-
-
-def _make_market(
-    yes_price: float,
-    question: str = "Will John Smith win the election?",
-    category: str = "politics",
-    market_id: str = "mkt-recorder-1",
-) -> Market:
-    return Market(
-        id=market_id,
-        condition_id="cond-recorder-1",
-        question=question,
-        yes_token_id="yes-tok",
-        no_token_id="no-tok",
-        yes_price=yes_price,
-        no_price=1.0 - yes_price,
-        volume_24h=50_000.0,
-        end_date=_end_date(),  # ~20 d → politics regime_min 0.08
-        active=True,
-        category=category,
-    )
-
-
-def _make_signals() -> ExternalSignals:
-    return ExternalSignals(
-        btc_price=100_000.0,
-        btc_change_24h=0.0,
-        eth_price=4_000.0,
-        eth_change_24h=0.0,
-        btc_dominance=50.0,
-        fear_greed_index=50,
-        fear_greed_label="neutral",
-        total_market_cap_change=0.0,
-        valid=True,
-    )
-
-
-def _evaluate(strategy: BayesianStrategy, market: Market, families=None) -> None:
-    asyncio.run(strategy.evaluate(market, _make_signals(), families or set()))
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Full-evaluation records: every input/output field the replay needs
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_confidence_skip_record_has_full_fields():
-    """Politics market whose edge passes but confidence blocks (the known
-    politics ceiling): record must carry the complete decision context."""
-    sentiment = _sentiment_for(0.470, 0.601)  # Georgia signature: edge_net 0.091
-    strategy = BayesianStrategy(news=FakeNews(sentiment), manifold=None, db=None)
-    market = _make_market(0.470)
-    _evaluate(strategy, market)
-
-    records = strategy.drain_cycle_records()
-    assert len(records) == 1
-    rec = records[0]
-    assert rec["market_id"] == "mkt-recorder-1"
-    assert rec["skip_reason"] == "confidence"
-    assert rec["category"] == "politics"
-    assert rec["polymarket_price"] == pytest.approx(0.470)
-    assert rec["prior_prob"] == pytest.approx(0.470)
-    assert rec["estimated_prob"] == pytest.approx(0.601, abs=1e-3)
-    assert rec["raw_final_prob"] == pytest.approx(0.601, abs=1e-3)
-    assert rec["edge_net"] == pytest.approx(0.091, abs=1e-3)
-    assert rec["regime_min_edge"] == pytest.approx(0.08)
-    assert rec["passed_net"] is True
-    assert rec["confidence"] == pytest.approx(0.50)
-    assert rec["direction"] == "BUY_YES"
-    assert rec["news_sentiment"] == pytest.approx(sentiment, abs=1e-6)
-    assert rec["feat_news_lo"] != 0.0
-    assert rec["news_budget_skipped"] is False
-    assert rec["guardrail_applied"] is False
-    assert rec["guardrail_changed_decision"] is False
-    assert rec["days_to_resolution"] is not None
-    assert rec["acted_on"] is False
-
-
-def test_edge_net_skip_record():
-    """No news, no edge → skip_reason=edge_net with passed_net False."""
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    market = _make_market(0.50)
-    _evaluate(strategy, market)
-
-    rec = strategy.drain_cycle_records()[0]
-    assert rec["skip_reason"] == "edge_net"
-    assert rec["passed_net"] is False
-    assert rec["estimated_prob"] == pytest.approx(0.50, abs=1e-3)
-    assert rec["feat_news_lo"] == 0.0
-
-
-def test_guardrail_fields_recorded_when_clamped():
-    """Guardrail clamp shows up in the record (applied=True, raw != final)."""
-    strategy = BayesianStrategy(
-        news=FakeNews(_sentiment_for(0.845, 0.431)), manifold=None, db=None
-    )
-    market = _make_market(0.845)
-    _evaluate(strategy, market)
-
-    rec = strategy.drain_cycle_records()[0]
-    assert rec["guardrail_applied"] is True
-    assert rec["raw_final_prob"] == pytest.approx(0.431, abs=1e-3)
-    assert rec["estimated_prob"] == pytest.approx(
-        0.845 - bayesian.MAX_NEWS_ONLY_PROB_SHIFT, abs=1e-3
-    )
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Early-skip records: minimal but present
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_prior_extreme_record():
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    _evaluate(strategy, _make_market(0.03))
-
-    rec = strategy.drain_cycle_records()[0]
-    assert rec["skip_reason"] == "prior_extreme"
-    assert rec["polymarket_price"] == pytest.approx(0.03)
-    assert rec["prior_prob"] == pytest.approx(0.05)  # clamped prior
-    assert rec["estimated_prob"] is None
-    assert rec["edge_net"] is None
-
-
-def test_family_skip_record():
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    market = _make_market(0.50)
-    from bot.data.polymarket import market_family_key
-    _evaluate(strategy, market, families={market_family_key(market)})
-
-    rec = strategy.drain_cycle_records()[0]
-    assert rec["skip_reason"] == "family"
-    assert rec["family_key"] is not None
-
-
-def test_unsupported_record():
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    market = _make_market(0.50, question="Will it rain tomorrow?", category="")
-    _evaluate(strategy, market)
-
-    rec = strategy.drain_cycle_records()[0]
-    assert rec["skip_reason"] == "unsupported"
-
-
-def test_record_skip_external_reason():
-    """main.py records reentry-guard skips through record_skip()."""
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    strategy.record_skip(_make_market(0.50), "reentry_guard")
-
-    rec = strategy.drain_cycle_records()[0]
-    assert rec["skip_reason"] == "reentry_guard"
-    assert rec["estimated_prob"] is None
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Budget flag + cycle lifecycle
-# ─────────────────────────────────────────────────────────────────────────────
-
-def test_news_budget_skipped_flag():
-    """With the cycle budget exhausted, the record must say GNews was never
-    asked — feat_news_lo=0.0 alone would be indistinguishable from no-news."""
-    strategy = BayesianStrategy(news=FakeNews(0.9), manifold=None, db=None)
-    strategy._news_queries_this_cycle = MAX_NEWS_QUERIES_PER_CYCLE
-    _evaluate(strategy, _make_market(0.50))
-
-    rec = strategy.drain_cycle_records()[0]
-    assert rec["news_budget_skipped"] is True
-    assert rec["news_sentiment"] == 0.0
-    assert rec["feat_news_lo"] == 0.0
-
-
-def test_drain_empties_and_reset_clears():
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    _evaluate(strategy, _make_market(0.50))
-    assert len(strategy.drain_cycle_records()) == 1
-    assert strategy.drain_cycle_records() == []
-
-    _evaluate(strategy, _make_market(0.50))
-    strategy.reset_cycle()
-    assert strategy.drain_cycle_records() == []
-
-
-def test_one_record_per_market_accumulates_in_order():
-    strategy = BayesianStrategy(news=None, manifold=None, db=None)
-    _evaluate(strategy, _make_market(0.03, market_id="m1"))   # prior_extreme
-    _evaluate(strategy, _make_market(0.50, market_id="m2"))   # edge_net
-    _evaluate(strategy, _make_market(0.97, market_id="m3"))   # prior_extreme
-
-    records = strategy.drain_cycle_records()
-    assert [r["market_id"] for r in records] == ["m1", "m2", "m3"]
-    assert [r["skip_reason"] for r in records] == [
-        "prior_extreme", "edge_net", "prior_extreme",
-    ]