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>
Two minor faults found during the GNews capture/prioritisation diagnostic:
1. Hyphens/dashes reached the GNews query verbatim. '-' is GNews's exclusion
operator, so a token like "El-Sayed" returned HTTP 400 and wasted a query.
_PUNCT_RE now strips '-', en dash and em dash to spaces.
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" with zero real
requests. Added NewsClient.enabled and gated the GNews block on it; with no
key the counter stays 0/5 and no spurious SKIP_GNEWS_PRIORITY is logged.
No behaviour change when a key is present.
Prioritisation itself was confirmed correct and is left untouched: politics
markets are sorted by gnews_priority DESC and prior-extreme markets return
before the budget is consumed, so no query is ever spent on a market that
cannot trade.
Tests: tests/test_news_query.py (4 new); full suite 66 passed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
sharpe_ratio was hardcoded to 0.0 in MetricsTracker and exposed as
'or 0' in /api/summary. With only 1 resolved trade (~40 flat days plus
one +299 jump) any computed Sharpe is statistically meaningless, so:
- bot/metrics/sharpe.py: annualized Sharpe (sqrt(365)) from daily
total_pnl closes, normalized by bankroll; sharpe_with_gate() returns
None + status until >=30 days observed AND >=10 resolved trades.
- Database.get_daily_pnl_closes(): last metrics_daily snapshot per UTC
day, oldest first — the return series input.
- MetricsTracker: stores the real (gated) Sharpe in the snapshot, NULL
below the gate; log line now includes sharpe.
- /api/summary: live Sharpe + sharpe_status/days_observed/min_* fields
explaining why it is null; resolved_count now live from COUNT(*).
- promotion_ready: requires resolved>=10, days>=30, and non-null
win_rate/calibration/sharpe plus existing thresholds — a single lucky
resolved trade can no longer promote.
- Dashboard Sharpe card shows the insufficient-sample explanation when
null instead of a bare em dash.
Tests: 13 new in tests/test_sharpe_gate.py (formula, gate, API contract,
tracker snapshot); verified failing pre-fix. Suite: 62 passed.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
resolved_count shared the final_prob IS NOT NULL filter with
calibration_score, so the resolved legacy Paxton trade (no signal data)
didn't count: realized_pnl=+309.06 and wins_realized=1 but resolved_count=0.
resolved_count now only requires resolution + not excluded; calibration
keeps the final_prob requirement since it scores against the estimate.
Validated against prod DB: new filter returns 1, old returned 0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Cycle-10 resolution check found market 562186 resolved (Paxton YES) but the
close failed with asyncpg AmbiguousParameterError: Postgres cannot infer the
type of a bare '$3 IS NOT NULL' in the close_pnl CASE. Reproduced via PREPARE
in the postgres pod; fixed by casting every $3 use to double precision.
The failed DB write also left memory/DB diverged: close_position() popped the
position and credited cash before persisting, so the retry at cycle 20 skipped
the market (pnl=n/a) while the DB row stayed open. Now the DB write happens
first and memory mutates only on success; check_resolutions() also isolates
per-market close failures so one error doesn't abort the cycle.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- PolymarketClient.get_market_resolution(): query Gamma API by market id;
resolved only when closed AND uma status final AND outcome prices binary
(never settle on disputed/ambiguous outcomes)
- bot/main.py: check_resolutions() runs every 10 cycles (~10 min) in paper
mode, settles open positions via PaperExecutor.close_position()
- close_reason now persisted as 'resolved' (resolution has its own column)
- tests/test_resolution_detector.py: 10 tests covering API parsing shapes
and the BUY_NO settlement flow; 27/27 suite green
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Legacy scan called ManifoldClient.get_probability(), removed in the v3
matcher migration, causing AttributeError when positions had changed
family keys. The block used Manifold to escalate positions to
CLOSE_RECOMMENDED (inversion detection) — a trading decision forbidden
under MANIFOLD_SIGNAL_ENABLED=false — so the dependency is removed
entirely; the scan keeps family re-keying and sibling-conflict logic.
- PaperExecutor.close_position() computed cash += position_cost * resolution,
ignoring direction: a winning BUY_NO (resolution=0.0) paid out $0 and
reported a loss. Now settles per trade:
BUY_YES: payout = shares * resolution
BUY_NO: payout = shares * (1 - resolution)
with pnl = payout - net_cost; Telegram win/loss keys off pnl > 0.
Adds read-only Database.get_open_trades_for_market().
- tests/test_paper_close.py covers the 4 deterministic payout cases;
tests/conftest.py shims datetime.UTC for local Python 3.10 (prod is 3.11).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Measure real Manifold coverage per semantic market category counted by UNIQUE
market (not audit rows, which are inflated by repeated re-evaluation). Base table
is manifold_match_audit filtered to v3_outcome_guard; each poly_market_id is
collapsed to one row, LEFT JOIN trades for family_key, category inferred from
family_key when present else from poly_question (gubernatorial / mayoral / senate
/ primary-republican / primary-democrat / big-tech / geopolitics / other).
Per category: unique_evaluated / unique_accepted / unique_rejected /
unique_no_results / coverage_rate, ordered by unique_evaluated DESC. Summary adds
total_unique_evaluated, total_unique_accepted, overall_coverage_rate,
categories_with_coverage. Read-only: new db method + endpoint only; matcher,
scheduler, cooldowns, thresholds and exposure untouched.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The trading loop re-evaluated the same ~22 politics/tech markets every ~60s,
flooding manifold_match_audit with ~76k rows (~3,500 attempts/market) of which
none carried new information, making the metrics uninterpretable.
Add per-market persistent cooldowns:
- New table manifold_eval_cooldown (poly_market_id PK, last_evaluated_at,
last_status, retry_after, cooldown_reason) created via run_migrations.
- bayesian.evaluate() consults the cooldown BEFORE calling the matcher and skips
the call entirely while now() < retry_after — no matcher call, no audit row,
no signal (equivalent to no_results). After a real evaluation it upserts the
cooldown with a verdict-dependent backoff: no_results/low_score/outcome_mismatch/
ambiguous_inversion -> 24h, conditional_market -> 7d, accepted -> 1h.
- manifold.py stays a pure client/matcher with no DB access.
- New db methods get_manifold_cooldown / upsert_manifold_cooldown.
- /api/metrics/manifold-matches summary gains unique_markets
{evaluated, accepted, coverage_rate} for per-market (not per-attempt) coverage.
Matching logic, MANIFOLD_MATCHER_VERSION, MANIFOLD_LOGODDS_WEIGHT, edge/exposure
thresholds and existing audit rows are unchanged.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The trades_dominated_by_mfld counter omitted the excluded_from_metrics
filter, so the admin-closed Maine governor trade inflated it to 1 while
attribution/features (which exclude such trades) were empty.
Add excluded_from_metrics IS NOT TRUE and mfld_match_status = 'accepted'
to the query so the counter is consistent with the attribution and
feature-metrics endpoints.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add MANIFOLD_MATCHER_VERSION="v3_outcome_guard" tag persisted to
manifold_match_audit.matcher_version so metrics can isolate current-matcher
stats from pre-versioning records, whose accepted matches the outcome
guard would now reject.
- schema: add matcher_version column + index; idempotent startup backfill
tagging NULL rows as legacy_pre_outcome_guard (no outcome types) or
v2_outcome_guard_no_version (has outcome type, version not persisted)
- save_manifold_audit: write matcher_version on every new record
- get_manifold_matches: split summary into current_version / all_time /
legacy; recent_matches now carry matcher_version
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Reject false-positive matches where Jaccard overlap is high but the outcome is
not equivalent (e.g. Poly nomination vs Manifold "If X is nominee, will he win").
- _is_conditional(): detect conditional Manifold markets (If/Conditional on/
Assuming/Given that prefixes + mid-sentence " if ...," clauses) -> reject with
reason "conditional_market".
- _classify_outcome(): classify into nomination|primary_win|general_win|
conditional|other; reject when poly/mfld types differ or either is conditional
-> reason "outcome_mismatch: poly=... manifold=...".
- Persist poly_outcome_type/mfld_outcome_type on ManifoldMatchResult, in
manifold_match_audit (CREATE + idempotent ALTER), save_manifold_audit() and
the bayesian call site.
- Tests covering classification, conditional detection and the Graham Platner
regression (now rejected); valid nomination<->nomination still accepted.
Untouched: _MATCH_THRESHOLD (0.40), MANIFOLD_LOGODDS_WEIGHT, edge thresholds,
exposure, trading logic.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds alpha attribution by dominant signal feature — which feat_*_lo had
the largest absolute log-odds value on each trade.
Changes:
- _dominant_feature() helper in api/main.py: picks the winning feature
from signal_components (threshold 0.0001, same as "triggered" in
/api/metrics/features)
- _enrich_trade() refactored to single exit point; adds dominant_feature
field to every open trade in /api/trades
- compute_attribution_from_db() in db.py: VALUES subquery finds dominant
feature per trade in SQL, then aggregates trade_count/avg_edge_net/
unrealized_pnl_est/realized_pnl/resolved_count/win_rate per group
- /api/metrics/attribution endpoint: returns attribution dict + total_attributed_trades
No schema changes, no strategy changes. Pure observability.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Eliminates the phantom 0.4% exposure overage after pod restarts.
During live trading execute() stores size_usdc in portfolio.positions and
deducts net_cost from cash — so total_value = bankroll − fees and
exposure_pct = sum(size_usdc) / (bankroll − fees).
Old initialize() stored net_cost in positions, making total_value = bankroll
and inflating exposure_pct (observed: 30.085% vs runtime 29.670%).
Fix: new get_open_position_data() returns both {market_id: size_usdc} and
total_net_cost in one query; initialize() uses size_usdc for positions and
total_net_cost for cash — identical model to execute().
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
schema.sql
trades: + close_pnl, resolution (market outcome storage)
metrics_daily: + unrealized_pnl_est, realized_pnl, open/closed/resolved_count
db.py
close_paper_position(): accepts resolution; computes close_pnl in SQL
BUY_YES: (resolution − entry_price) × shares
BUY_NO: ((1 − resolution) − entry_price) × shares
save_daily_metrics(): persists new columns
compute_metrics_from_db(): single DB query for all metrics; no in-memory state
tracker.py — complete rewrite (stateless)
Removed self._trades, self._daily_returns, compute_metrics(), _compute_sharpe(),
check_promotion_thresholds(), _empty_metrics()
update_daily_summary() now reads compute_metrics_from_db() every cycle
Safe across pod restarts: always reflects full DB history
paper.py
close_position(): passes resolution to close_paper_position()
api/main.py /api/summary
Added unrealized_pnl_est (estimated, open trades) and realized_pnl (exact,
closed+resolved) as separate fields alongside total_pnl
win_rate: null if < 5 resolved trades (was proxy on entry_price < 0.5)
calibration_score: Brier-based, null if < 10 resolved trades
resolved_count exposed as field
Each field annotated with: exact/estimated, source, null conditions
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>
Republicans (plural) previously didn't match _PARTY_RE because the pattern
was r"\bRepublican\b" (no optional s). Added Republicans? for symmetry with
Democrats?. The general-election family fix already handles this case via
etype_m, but the plural match is needed for the party-only fallback branch.
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>
- Add European football leagues (La Liga, Premier League, Bundesliga, etc.)
to _SPORTS_EXCLUSIONS so those markets are filtered before category detection
- Reorder _detect_category: check tech before crypto/finance so company-specific
markets (OpenAI IPO, NVIDIA, Apple) resolve to "tech" instead of "crypto/finance"
- Widen resolution horizon default from 60 to 90 days to surface more
markets in the 0.08–0.92 uncertainty zone
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add _SPORTS_EXCLUSIONS list checked first in _detect_category so NBA/NFL/
MLB/NHL/tennis/golf/UFC/boxing/wrestling/tournament markets never bleed into
politics or events categories. Also removes 'super bowl' from _EVENTS_KEYWORDS
since it's now covered by the sports exclusion.
Keywords excluded: nba, nfl, mlb, nhl, basketball, football, baseball, hockey,
soccer, mvp, rookie of the, championship, super bowl, world series, playoffs,
playoff, tournament, tennis, golf, ufc, boxing, wrestler, wrestling,
slam dunk, home run, touchdown.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
vaderSentiment==3.3.2 added to requirements.txt.
_score_headlines now:
- scores each article (title + description) with VADER compound ∈ [-1, +1]
- filters out articles with |compound| ≤ 0.05 (no clear signal)
- weights remaining articles by recency (GNews newest-first, rank 0 → highest weight)
- returns weighted mean clamped to [-1, +1]
Removes the custom keyword sets (_POSITIVE/_NEGATIVE) and the set-based
bag-of-words algorithm that capped scores at ~±0.5 in practice.
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>
- Drop the 'from' date filter — it's a paid GNews feature, causes 403 on free tier
- Add User-Agent header to httpx client; urllib default passes, httpx default blocked
- Log actual HTTP status code for every request (INFO) and response body on non-200
- Cache neutral result on 400/401/403/429 to avoid hammering the quota
- Remove unused _iso_days_ago() helper and 'days' param from get_sentiment()
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>
- polymarket.py: add keyword lists for politics (election, trump, ukraine…),
tech (AI, OpenAI, Apple, nvidia…), and events (super bowl, oscar, spacex…);
introduce _detect_category() so all four categories flow through a single
code path; filter already-expired markets (end_dt < now) in addition to
the existing future-cutoff filter; log per-category counts at startup
- bayesian.py: extend is_any_supported to include is_politics / is_tech /
is_events; use BTC as a risk-sentiment proxy for non-crypto categories
(halved weight to reflect weaker correlation); cap confidence_cap at 0.65
for macro/politics/tech/events; MIN_EDGE stays at 0.10
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
