Adds feat_fg_lo / feat_mom_lo / feat_news_lo / feat_mfld_lo / feat_btc_dom_lo
to every trade, all normalized to log-odds contribution for direct comparability.
- fg / mom / btc_dom: raw probability-delta × 2 → log-odds
- news / mfld: already log-odds (LOGODDS_WEIGHT already applied), no scaling
- btc_dom tracked separately in bayesian.py instead of bundled in total_adj
- reasoning string updated to fg_lo= / mom_lo= notation for self-documentation
Schema: 5 new DOUBLE PRECISION columns + 2 partial indexes
Stack: TradingSignal → Order → Trade → save_trade all carry feat fields
Startup: backfill_feature_columns() recovers fg/mom/news/mfld from old
reasoning strings (×2 applied to fg/mom); btc_dom_lo stays NULL for legacy
API: /api/metrics/features — triggered/material split per feature with
two-level thresholds (0.05 for fg/mom/btc_dom, 0.10 for news/mfld)
API: /api/trades/legacy — exposes pre-Phase-1 trades (edge_net IS NULL)
API: _enrich_trade backward-compat: reads DB columns first, falls back to
reasoning regex with unit conversion for pre-Phase-6 trades
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- db: update_family_key() persists corrected family slugs for open trades
- db: get_recently_closed_inverted() returns markets closed for inversion
within N hours; used as reentry guard in the trading loop
- db: get_recent_trades() accepts status=open|closed|None and adds a
computed "status" field to every row
- bot/main.py: legacy scan now computes family_key from stored question
alone (dummy Market) when a position's market is no longer active —
fixes NULL family_key on legacy trades like Ken Paxton (562186)
- bot/main.py: legacy scan (Step 2.5) persists corrected family_keys in
DB so family conflict guards work correctly on next restart
- bot/main.py: positions with NULL edge_net and no live market are tagged
legacy_incomplete instead of OK; counted separately in scan summary
- bot/main.py: reentry_guard blocks re-entering any market closed for
inversion bug within 24h; logs reentry_guard_triggered per skip
- api/main.py: /api/trades now accepts ?status=open|closed|all (default
open) and includes status_filter in response
DB fix (applied directly): 629558 family_key politics-2026 →
ohio-gubernatorial-2026; 562186 family_key NULL → texas-republican-2026
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds run_legacy_scan() that executes once at startup before the trading loop:
1. Re-keys every open DB position using the current market_family_key()
2. Groups by new family key; KEEP = highest edge_net, CLOSE_RECOMMENDED = sibling
3. Manifold re-query for positions whose family key changed; if corrected
probability contradicts the trade direction → CLOSE_RECOMMENDED
4. Logs full report (KEEP / REVIEW / CLOSE_RECOMMENDED) before any closures
5. In paper mode: auto-closes all CLOSE_RECOMMENDED positions
For the existing Ohio bug:
- Democrats win Ohio governor (629557): CLOSE_RECOMMENDED
family changed ohio-democrat-2026 → ohio-gubernatorial-2026
Manifold re-query confirms prob=0.05 contradicts BUY_YES (inversion bug)
$X returned to cash at break-even
- Republicans win Ohio governor (629558): KEEP
higher edge_net (0.349 > 0.247)
Infrastructure:
- schema.sql: closed_at TIMESTAMPTZ, close_reason TEXT on trades
- db.py: all open-position queries filter WHERE closed_at IS NULL
+ close_paper_position(market_id, reason)
- paper.py: close_legacy_position(market_id, reason) → float
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
FASE 1 — market_family_key() general election fix
General elections now group by office, not by party, so complementary
markets ("Republicans win Ohio governor" / "Democrats win Ohio governor")
share the same family key (ohio-gubernatorial-2026). The second market
is blocked by the occupied_families check rather than traded as independent.
Primaries still keep the party (texas-republican-2026) because each party
runs its own separate primary race.
FASE 2 — Manifold party inversion guard
_detect_party() identifies the winning side in both the Polymarket question
and the matched Manifold title. If they are confirmed opposites (republican
vs democrat), the probability is inverted (1 - prob) before use.
Full audit log per query:
poly_question / manifold_title / manifold_url / match_score /
prob_raw / inverted / prob_final
Root cause of Ohio Manifold:0.95 on both sides: both queries matched the
same Manifold market ("Republicans win Ohio governor" prob=0.95). For the
"Democrats win" query the inversion now produces prob_final=0.05 instead of
blindly applying 0.95 to the wrong direction.
FASE 4 — startup contradiction scan
get_open_position_details() added to db.py. main.py checks all open
positions at startup, warns on any family with >1 position, and recommends
keeping the one with the highest edge_net. No auto-close.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Signal 5: ManifoldClient queries Manifold Markets API for a matching binary
market by keyword overlap (threshold 0.25) and applies a log-odds adjustment
proportional to the divergence from the Polymarket prior.
manifold_log_adj = (log_odds(manifold_prob) - log_odds(prior)) × 0.6
A 30pp divergence (Manifold 0.75 vs Poly 0.45) produces edge_gross ≈ 0.19,
clearing the politics far-horizon regime_min=0.12 after costs. Confidence
boosted +0.08 when Manifold match found.
Per-feature observability: every SKIP_EDGE_NET and TRADE log line now includes
fg=±X.XXX mom=±X.XXX mfld=±X.XXXX news=±X.XXXX
so the contribution of each signal to edge is auditable per market.
Files: bot/data/manifold.py (new), bot/strategy/bayesian.py, bot/main.py
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Phase 1 — Edge neto real (paper.py, bayesian.py, risk/manager.py, db.py):
- Trade records now store edge_gross, edge_net, prior_prob, final_prob,
mid_price, spread_estimate, commission, family_key
- edge_net = edge_gross - SPREAD_ESTIMATE(0.02) - COMMISSION_RATE(0.02)
NOTE: both constants are heuristics, not exact Polymarket exchange costs
- Execution gate changed from edge_gross > MIN_EDGE to edge_net > regime_min_edge
Phase 2 — Market families (polymarket.py):
- market_family_key(market) groups related markets:
texas-republican-2026, fed-april-2026, openai-2026, etc.
- At most 1 trade per family per cycle; occupied_families propagated via main.py
- Family key logged on every TRADE and SKIP line
Phase 3 — GNews priority (news.py, bayesian.py, main.py):
- NewsClient.get_freshness() returns 1.0/0.75/0.40/0.10 by cache age
- gnews_priority(market, news) = uncertainty × volume_score × freshness
- Politics markets sorted by priority DESC before eval so best markets get
the 5-query/cycle GNews budget first
Phase 4 — Regime min-edge by category/horizon (bayesian.py):
- politics >60d → 0.12, 30-60d → 0.10, <30d → 0.08
- tech / crypto/finance → 0.10
- All thresholds applied to edge_net (not edge_gross)
Phase 5 — Observability (bayesian.py, main.py):
- Structured skip labels: SKIP_UNSUPPORTED, SKIP_NO_SIGNALS,
SKIP_PRIOR_EXTREME, SKIP_FAMILY, SKIP_GNEWS_PRIORITY, SKIP_EDGE_NET
- TRADE lines now include family_key, edge_gross, edge_net, regime_min, days
- schema.sql: 8 new cols on trades, 7 new cols on signals (via ALTER TABLE IF NOT EXISTS)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- CACHE_TTL: 4h → 6h (≤36 req/day with ≤9 politics markets)
- GNews only called for is_politics markets (BTC/F&G cover crypto/macro)
- MAX_NEWS_QUERIES_PER_CYCLE=5: BayesianStrategy.reset_cycle() called each
iteration; counter increments only on actual API call (cache hits free)
- 2s asyncio.sleep in news.py finally block after each real HTTP request
- main.py sorts markets: politics first by end_date ascending, so soonest-
resolving markets consume the 5-query budget before others
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
bot/data/news.py (new):
- NewsClient with in-memory cache (TTL=4h) to stay within 100 req/day limit
- _build_query(): strips dates, punctuation and stopwords from market question
- _score_headlines(): keyword-based pos/neg vote per article, averaged ∈ [-1, +1]
- Degrades to 0.0 on missing key, 403 quota, or network error
bot/strategy/bayesian.py:
- BayesianStrategy(news=NewsClient) — optional, backwards compatible
- Signal 4: GNews sentiment applied as direct log-odds shift (weight=1.5)
so a ±1.0 sentiment score moves a 50% prior to 82%/18%
- +0.10 confidence boost when news signal is present
- NEWS_LOGODDS_WEIGHT constant documented at module level
bot/main.py:
- Instantiate NewsClient, pass to BayesianStrategy, close in finally block
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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