chemavxandClaude Fable 5
4002f03d0c
CI/CD / build-and-push (push) Successful in 7s
For politics/tech/events markets there is no above/below price notion, so is_price_above defaulted to False (or flipped on accidental wording like "reach") and sign-inverted the macro adjustments: BTC +5% or high Fear&Greed subtracted probability from YES on "Will X win the election?" markets. Skip both signals entirely for non-price markets: contributions stay 0.0, feat_mom_lo / feat_fg_lo persist as 0.0. Price markets (BTC/ETH/crypto) keep the exact current behavior, including the below-market sign flip. Removes the now-dead BTC(sentiment) momentum branch and its 0.5 attenuator. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
"""
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Tests for FASE 3 — macro signals (momentum, Fear & Greed) must not apply to
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non-price markets (politics / tech / events).
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Regression: for "Will X win the election?"-style questions, is_price_above is
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False, so positive BTC momentum and high Fear & Greed were sign-flipped into
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evidence AGAINST the YES outcome. The fix skips both signals entirely for
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politics/tech/events, leaving their contributions (and feat_mom_lo /
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feat_fg_lo) at 0.0.
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evaluate_market only returns a TradingSignal on the TRADE path; on skips it
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returns None but always emits a structured log line containing the per-feature
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log-odds (fg_lo=… mom_lo=…). The tests parse that line via caplog.
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"""
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import asyncio
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import logging
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import math
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import re
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import pytest
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from bot.data.external import ExternalSignals
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from bot.data.polymarket import Market
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from bot.strategy.bayesian import BayesianStrategy
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FEAT_RE = re.compile(r"fg_lo=([+-]\d+\.\d+) mom_lo=([+-]\d+\.\d+)")
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def _make_market(question: str, category: str) -> Market:
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return Market(
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id="mkt-test-1",
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condition_id="cond-test-1",
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question=question,
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yes_token_id="yes-tok",
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no_token_id="no-tok",
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yes_price=0.50,
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no_price=0.50,
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volume_24h=50_000.0,
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end_date="2026-07-15T00:00:00Z",
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active=True,
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category=category,
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)
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def _make_signals() -> ExternalSignals:
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# Strong bullish macro environment: BTC +10%, extreme greed.
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return ExternalSignals(
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btc_price=100_000.0,
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btc_change_24h=10.0,
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eth_price=4_000.0,
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eth_change_24h=8.0,
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btc_dominance=50.0,
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fear_greed_index=80,
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fear_greed_label="greed",
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total_market_cap_change=5.0,
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valid=True,
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)
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def _evaluate_and_parse_feats(question: str, category: str, caplog) -> tuple[float, float]:
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"""Run BayesianStrategy.evaluate and return (feat_fg_lo, feat_mom_lo) from the audit log."""
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strategy = BayesianStrategy(news=None, manifold=None, db=None)
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market = _make_market(question, category)
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with caplog.at_level(logging.INFO, logger="bot.strategy.bayesian"):
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asyncio.run(
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strategy.evaluate(market, _make_signals(), occupied_families=set())
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)
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for record in caplog.records:
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m = FEAT_RE.search(record.getMessage())
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if m:
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return float(m.group(1)), float(m.group(2))
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pytest.fail(
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"No SKIP_EDGE_NET/TRADE log line with feature contributions found; "
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f"got: {[r.getMessage() for r in caplog.records]}"
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)
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def test_politics_market_ignores_momentum_and_fear_greed(caplog):
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"""Political market with BTC +10% and F&G=80 → both contributions 0.0."""
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feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
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"Will John Smith win the election?", "politics", caplog
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)
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assert feat_mom_lo == 0.0
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assert feat_fg_lo == 0.0
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# The signal sources must not mention momentum or Fear & Greed either.
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full_log = "\n".join(r.getMessage() for r in caplog.records)
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assert "Fear&Greed" not in full_log
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assert "24h" not in full_log
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def test_tech_and_events_markets_ignore_macro_signals(caplog):
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for category in ("tech", "events"):
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caplog.clear()
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feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
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"Will the product launch happen this quarter?", category, caplog
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)
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assert feat_mom_lo == 0.0, f"momentum applied to {category} market"
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assert feat_fg_lo == 0.0, f"Fear&Greed applied to {category} market"
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def test_btc_market_keeps_momentum_and_fear_greed(caplog):
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"""BTC price market with BTC +10% and F&G=80 → current behavior preserved."""
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feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
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"Will Bitcoin be above $150,000 on July 1?", "crypto/finance", caplog
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)
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assert feat_mom_lo > 0
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assert feat_fg_lo > 0
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# Exact values: is_price_above=True ("above"), so contributions are positive.
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# momentum: tanh(10/20) * 0.15, ×2 → log-odds. F&G>70: +0.06, ×2 → log-odds.
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assert feat_mom_lo == pytest.approx(math.tanh(10 / 20) * 0.15 * 2, abs=1e-4)
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assert feat_fg_lo == pytest.approx(0.06 * 2, abs=1e-4)
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full_log = "\n".join(r.getMessage() for r in caplog.records)
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assert "Fear&Greed: 80 (greed)" in full_log
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assert "BTC 24h: +10.0%" in full_log
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def test_btc_below_market_sign_flip_preserved(caplog):
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"""'below' market: bullish macro lowers YES probability (sign flip intact)."""
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feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
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"Will Bitcoin drop below $50,000 by August?", "crypto/finance", caplog
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)
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assert feat_mom_lo < 0
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assert feat_fg_lo < 0
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