chemavx
4dadd3c2c4
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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>
265 lines
11 KiB
Python
265 lines
11 KiB
Python
"""
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Bayesian Market Making Strategy.
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Core idea:
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1. Compute a prior probability for a market outcome using external data
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2. Compare with Polymarket's current price
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3. If divergence > threshold + confidence is high enough → generate signal
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For crypto markets: if BTC is up 5% and fear/greed is 75 (greed),
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a market asking "Will BTC be above $X?" should be priced higher than
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Polymarket might reflect in a slow-moving order book.
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"""
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import logging
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import math
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from dataclasses import dataclass
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from typing import Optional
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from bot.data.polymarket import Market
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from bot.data.external import ExternalSignals
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from bot.data.news import NewsClient
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log = logging.getLogger(__name__)
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# Minimum edge required to place a trade.
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# With an informed prior (poly price), 10% means our signals strongly disagree
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# with the market — much higher bar than before, but necessary to avoid noise.
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MIN_EDGE = 0.10 # 10% edge minimum
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MIN_CONFIDENCE = 0.55 # Minimum confidence in our estimate
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# Log-odds weight applied to the GNews sentiment score (range ±1.0).
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# A weight of 1.5 means a fully negative/positive signal shifts log-odds by ±1.5,
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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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@dataclass
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class TradingSignal:
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market_id: str
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question: str
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polymarket_price: float # Current market price for YES (0-1)
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estimated_prob: float # Our Bayesian estimate (0-1)
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edge: float # estimated_prob - polymarket_price
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confidence: float # How confident we are (0-1)
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direction: str # "BUY_YES" | "BUY_NO"
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reasoning: str # Human-readable explanation for logging
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sources: list[str] # Data sources used
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class BayesianStrategy:
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"""
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Estimates true probability using external signals and Bayesian updating.
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Prior: Polymarket's current YES price (market consensus — not 0.5)
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Likelihood updates from:
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- BTC/ETH price momentum
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- Fear & Greed index
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- Market cap trend / BTC dominance
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We only bet when our signals move the estimate far enough from the prior
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to justify the fee + slippage cost (MIN_EDGE).
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"""
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def __init__(self, news: Optional[NewsClient] = None) -> None:
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self._signal_count = 0
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self._news = news # Optional; degrades gracefully when None or key missing
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async def evaluate(
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self,
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market: Market,
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ext: ExternalSignals,
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) -> Optional[TradingSignal]:
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"""
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Evaluate a market and return a signal if edge exists.
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Returns None if no actionable opportunity.
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"""
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question_lower = market.question.lower()
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category = market.category # set by PolymarketClient
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# Classify what kind of market this is
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is_price_above = any(w in question_lower for w in ["above", "over", "exceed", "higher", "atleast", "reach"])
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is_price_below = any(w in question_lower for w in ["below", "under", "less than", "lower", "drop"])
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is_btc = "btc" in question_lower or "bitcoin" in question_lower
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is_eth = "eth" in question_lower or "ethereum" in question_lower
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is_sol = "sol" in question_lower or "solana" in question_lower
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is_xrp = "xrp" in question_lower or "ripple" in question_lower
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is_doge = "doge" in question_lower or "dogecoin" in question_lower
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is_altcoin = is_sol or is_xrp or is_doge or any(
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w in question_lower for w in ["ltc", "litecoin", "bnb", "ada", "cardano", "avax", "avalanche"]
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)
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is_general_crypto = any(
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w in question_lower for w in ["crypto", "market cap", "total market", "altcoin", "defi"]
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)
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is_macro = any(
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w in question_lower for w in ["nasdaq", "s&p", "sp500", "inflation", "fed rate", "interest rate", "tariff"]
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)
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is_politics = category == "politics"
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is_tech = category == "tech"
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is_events = category == "events"
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is_any_supported = (
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is_btc or is_eth or is_altcoin or is_general_crypto or is_macro
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or is_politics or is_tech or is_events
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)
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if not is_any_supported:
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log.info(
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"SKIP %-50s | reason=unsupported category=%r",
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market.question[:50], category,
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)
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return None
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if not ext.valid:
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log.info(
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"SKIP %-50s | reason=no external signals",
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market.question[:50],
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)
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return None # Can't reason without external data
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# --- Bayesian probability estimation ---
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# Prior = Polymarket consensus price, clamped away from extremes.
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# The market already aggregates information from many traders;
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# our signals update from that informed baseline, not from 0.5.
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prior = max(0.05, min(0.95, market.yes_price))
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sources: list[str] = [f"Prior=poly({prior:.3f})"]
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adjustments: list[float] = []
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# Signal 1: Price momentum (asset-specific or total market cap as proxy)
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# For politics/tech/events use BTC as a broad sentiment proxy.
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if is_btc:
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momentum = ext.btc_change_24h
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asset_label = "BTC"
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elif is_eth:
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momentum = ext.eth_change_24h
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asset_label = "ETH"
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elif is_politics or is_tech or is_events:
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# BTC as risk-sentiment proxy for non-crypto categories
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momentum = ext.btc_change_24h
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asset_label = "BTC(sentiment)"
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else:
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# Altcoins and general crypto: use total market cap change as proxy
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momentum = ext.total_market_cap_change
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asset_label = "total mktcap"
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if abs(momentum) > 2:
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momentum_adj = math.tanh(momentum / 20) * 0.15 # Max ±15%
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# For non-directional markets (politics/events/tech), momentum is weaker signal
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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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sources.append(f"{asset_label} 24h: {momentum:+.1f}%")
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# Signal 2: Fear & Greed
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fg = ext.fear_greed_index
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if fg > 70:
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fg_adj = 0.06
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sources.append(f"Fear&Greed: {fg} (greed)")
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elif fg < 30:
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fg_adj = -0.06
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sources.append(f"Fear&Greed: {fg} (fear)")
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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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# 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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dom_adj = -0.03 if is_price_above else 0.03
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adjustments.append(dom_adj)
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sources.append(f"BTC dom: {ext.btc_dominance:.1f}% (high → alt pressure)")
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elif (is_eth or is_altcoin or is_general_crypto) and ext.btc_dominance < 45:
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dom_adj = 0.03 if is_price_above else -0.03
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adjustments.append(dom_adj)
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sources.append(f"BTC dom: {ext.btc_dominance:.1f}% (low → alt season)")
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# Signal 4: GNews sentiment (politics / tech / events only)
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# Applied as a direct log-odds shift — stronger signal than macro proxies.
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# Weight NEWS_LOGODDS_WEIGHT=1.5 means a ±1.0 sentiment score shifts
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# log-odds by ±1.5 (e.g. 50% prior → ~82% / ~18%).
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news_log_adj = 0.0
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if (is_politics or is_tech or is_events) and self._news is not None:
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sentiment = await self._news.get_sentiment(market.question)
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if abs(sentiment) > 0.05:
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news_log_adj = sentiment * NEWS_LOGODDS_WEIGHT
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sources.append(f"GNews: {sentiment:+.2f}")
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# Macro/politics/tech/events: cap confidence lower to reflect weaker signal quality
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if is_macro or is_politics or is_tech or is_events:
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confidence_cap = 0.65
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else:
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confidence_cap = 0.90
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# Compute posterior using log-odds updating.
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# total_adj (BTC/F&G/dominance) is amplified ×2 because those are weak proxies.
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# news_log_adj is applied at face value — it IS a direct log-odds signal.
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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 = max(0.05, min(0.95, estimated_prob))
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# Compute edge
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edge = estimated_prob - market.yes_price
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direction = "BUY_YES" if edge > 0 else "BUY_NO"
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abs_edge = abs(edge)
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# Confidence based on signal agreement
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agreement = sum(1 for a in adjustments if (a > 0) == (total_adj > 0))
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confidence = min(confidence_cap, 0.4 + (agreement / max(len(adjustments), 1)) * 0.5)
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# News signal available → boost confidence by 0.10 (news corroborates macro signals)
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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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# Log evaluation result for every market
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action = "TRADE" if (abs_edge >= MIN_EDGE and confidence >= MIN_CONFIDENCE) else "SKIP"
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skip_reason = ""
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if action == "SKIP":
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reasons = []
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if abs_edge < MIN_EDGE:
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reasons.append(f"edge={abs_edge:.3f}<{MIN_EDGE}")
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if confidence < MIN_CONFIDENCE:
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reasons.append(f"conf={confidence:.2f}<{MIN_CONFIDENCE}")
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skip_reason = " | reason=" + ",".join(reasons)
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log.info(
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"%-5s %-50s | cat=%-12s | prior=%.3f | est=%.3f | edge=%+.3f | conf=%.2f | dir=%-8s | signals=%s%s",
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action,
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market.question[:50],
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category,
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prior,
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estimated_prob,
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edge,
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confidence,
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direction,
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", ".join(sources[1:]) or "none",
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skip_reason,
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)
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# Filter: only trade if edge and confidence thresholds met
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if abs_edge < MIN_EDGE or confidence < MIN_CONFIDENCE:
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return None
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reasoning = (
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f"Prior=poly({prior:.3f}) → estimate={estimated_prob:.3f} | "
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f"Poly price={market.yes_price:.3f} | "
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f"Edge={edge:+.3f} | "
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f"Direction={direction} | "
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f"Signals: {', '.join(sources[1:])}" # skip the prior label already shown
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)
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self._signal_count += 1
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return TradingSignal(
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market_id=market.id,
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question=market.question,
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polymarket_price=market.yes_price,
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estimated_prob=estimated_prob,
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edge=abs_edge,
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confidence=confidence,
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direction=direction,
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reasoning=reasoning,
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sources=sources,
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)
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def _sigmoid(x: float) -> float:
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return 1 / (1 + math.exp(-x))
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