feat(strategy): Manifold cross-market signal + per-feature contribution logging
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CI/CD / build-and-push (push) Successful in 2m21s
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>
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chemavx
@@ -21,6 +21,7 @@ from bot.data.external import ExternalSignals
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if TYPE_CHECKING:
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from bot.data.news import NewsClient
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from bot.data.manifold import ManifoldClient
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log = logging.getLogger(__name__)
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@@ -51,6 +52,12 @@ MIN_CONFIDENCE = 0.55 # Minimum confidence to generate a signal
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# which moves a 50% prior to ~18%/82% — strong but not overwhelming.
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NEWS_LOGODDS_WEIGHT = 1.5
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# Log-odds weight applied to Manifold cross-market probability signal.
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# Weight 0.6: a 30 pp divergence (Manifold 0.75 vs Poly 0.45) produces
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# edge_gross ≈ 0.19, clearing politics far-horizon regime_min=0.12 after costs.
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# Weaker than NEWS_LOGODDS_WEIGHT because Manifold can have illiquid/stale markets.
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MANIFOLD_LOGODDS_WEIGHT = 0.6
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# GNews free tier: 100 req/day. We limit to 5 queries per trading cycle
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# (politics markets only) and rely on 6 h cache to stay within budget.
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MAX_NEWS_QUERIES_PER_CYCLE = 5
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@@ -180,9 +187,14 @@ class BayesianStrategy:
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- Within evaluate(), the per-cycle cap is enforced.
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"""
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def __init__(self, news: Optional["NewsClient"] = None) -> None:
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def __init__(
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self,
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news: Optional["NewsClient"] = None,
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manifold: Optional["ManifoldClient"] = None,
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) -> None:
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self._signal_count = 0
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self._news = news
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self._manifold = manifold
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self._news_queries_this_cycle = 0
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# Per-cycle counters — reset by reset_cycle(), read by get_cycle_stats()
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self._skip_family: int = 0
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@@ -337,11 +349,13 @@ class BayesianStrategy:
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momentum = ext.total_market_cap_change
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asset_label = "total mktcap"
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_momentum_contribution = 0.0
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if abs(momentum) > 2:
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momentum_adj = math.tanh(momentum / 20) * 0.15
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if is_politics or is_tech or is_events:
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momentum_adj *= 0.5
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adjustments.append(momentum_adj if is_price_above else -momentum_adj)
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_momentum_contribution = momentum_adj if is_price_above else -momentum_adj
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adjustments.append(_momentum_contribution)
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sources.append(f"{asset_label} 24h: {momentum:+.1f}%")
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# Signal 2: Fear & Greed
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@@ -355,7 +369,8 @@ class BayesianStrategy:
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else:
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fg_adj = (fg - 50) / 50 * 0.04
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sources.append(f"Fear&Greed: {fg} (neutral)")
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adjustments.append(fg_adj if is_price_above else -fg_adj)
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_fg_contribution = fg_adj if is_price_above else -fg_adj
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adjustments.append(_fg_contribution)
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# Signal 3: BTC dominance — hurts altcoins when high
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if (is_eth or is_altcoin or is_general_crypto) and ext.btc_dominance > 55:
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@@ -382,13 +397,26 @@ class BayesianStrategy:
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market.question[:50], MAX_NEWS_QUERIES_PER_CYCLE,
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)
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# Signal 5: Manifold cross-market probability (politics + tech)
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# Applies a log-odds adjustment proportional to divergence from prior.
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# No query budget — 30 min cache means network cost is paid once per cycle.
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manifold_log_adj = 0.0
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if (is_politics or is_tech) and self._manifold is not None:
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manifold_prob = await self._manifold.get_probability(market.question)
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if manifold_prob is not None:
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m_clamped = max(0.05, min(0.95, manifold_prob))
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m_log = math.log(m_clamped / (1 - m_clamped))
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p_log = math.log(prior / (1 - prior))
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manifold_log_adj = (m_log - p_log) * MANIFOLD_LOGODDS_WEIGHT
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sources.append(f"Manifold:{manifold_prob:.2f}")
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# Confidence cap: macro/politics/tech signals are weaker proxies
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confidence_cap = 0.65 if (is_macro or is_politics or is_tech or is_events) else 0.90
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# Posterior via log-odds updating
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log_odds_prior = math.log(prior / (1 - prior))
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total_adj = sum(adjustments)
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estimated_prob = _sigmoid(log_odds_prior + total_adj * 2 + news_log_adj)
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estimated_prob = _sigmoid(log_odds_prior + total_adj * 2 + news_log_adj + manifold_log_adj)
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estimated_prob = max(0.05, min(0.95, estimated_prob))
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# ── Phase 1: edge_gross and edge_net ─────────────────────────────────
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@@ -408,6 +436,16 @@ class BayesianStrategy:
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confidence = min(confidence_cap, 0.4 + (agreement / max(len(adjustments), 1)) * 0.5)
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if news_log_adj != 0.0:
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confidence = min(confidence_cap, confidence + 0.10)
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if manifold_log_adj != 0.0:
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confidence = min(confidence_cap, confidence + 0.08)
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# Per-feature contribution string for audit logging
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feat_str = (
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f"fg={_fg_contribution:+.3f} "
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f"mom={_momentum_contribution:+.3f} "
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f"mfld={manifold_log_adj:+.4f} "
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f"news={news_log_adj:+.4f}"
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)
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# ── Phase 5: structured audit log ────────────────────────────────────
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passed_gross = edge_gross >= regime_min
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@@ -433,10 +471,10 @@ class BayesianStrategy:
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log.info(
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"SKIP_EDGE_NET %-50s | cat=%-12s | family=%-28s | "
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"prior=%.3f | est=%.3f | gross=%+.3f | net=%+.3f | "
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"regime=%.2f | days=%d | conf=%.2f | signals=%s | %s",
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"regime=%.2f | days=%d | conf=%.2f | %s | signals=%s | %s",
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market.question[:50], category, family,
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prior, estimated_prob, edge_gross, edge_net,
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regime_min, days, confidence,
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regime_min, days, confidence, feat_str,
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", ".join(sources[1:]) or "none",
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" | ".join(skip_parts),
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)
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@@ -455,10 +493,10 @@ class BayesianStrategy:
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log.info(
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"TRADE %-50s | cat=%-12s | family=%-28s | "
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"prior=%.3f | est=%.3f | gross=%+.3f | net=%+.3f | "
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"regime=%.2f | days=%d | conf=%.2f | dir=%-8s | signals=%s",
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"regime=%.2f | days=%d | conf=%.2f | dir=%-8s | %s | signals=%s",
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market.question[:50], category, family,
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prior, estimated_prob, edge_gross, edge_net,
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regime_min, days, confidence, direction,
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regime_min, days, confidence, direction, feat_str,
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", ".join(sources[1:]) or "none",
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)
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