feat(replay): R0 snapshot recorder — archive per-cycle decisions into signals

The signals and markets tables existed since Phase 2/5 but never had a
writer; the replay engine (phase plan line 2.1) needs a per-(market, cycle)
archive of what the strategy saw and decided. This wires them up:

- signals: one row per evaluated market per cycle, now carrying INPUTS
  (news_sentiment, feat_*_lo, volume_24h, days_to_resolution) plus the
  existing outputs (probs, edges, gates, skip_reason). skip_reason is
  granular: unsupported/no_signals/prior_extreme/family/edge_net/
  confidence/reentry_guard. news_budget_skipped distinguishes "GNews not
  asked" (5-query budget) from "no news".
- ext_snapshots: one row per cycle with the ExternalSignals snapshot;
  signals rows join on cycle_ts.
- markets: metadata upserted each cycle (replay rebuilds Market from it).
- Retention: prune > SIGNALS_RETENTION_DAYS (default 90) once a day.
- SIGNAL_RECORDER_ENABLED (default true) gates all DB writes; every write
  is try/except — the recorder can never break trading.

Strategy changes are purely additive (record accumulation at each exit
path of evaluate()); no weights, thresholds, gates or sizing touched,
per the freeze in the current phase plan.

Tests: 10 new deterministic tests (85 total passing). Schema migration
dry-run validated against prod postgres inside a rolled-back transaction.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

tests/test_signal_recorder.py

@@ -0,0 +1,224 @@
+"""
+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",
+    ]