fix(strategy): exclude momentum and fear-greed signals from non-price markets (politics/tech/events)

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>

bot/strategy/bayesian.py

@@ -419,30 +419,36 @@ class BayesianStrategy:
         sources: list[str] = [f"Prior=poly({prior:.3f})"]
         adjustments: list[float] = []
 
-        # Signal 1: price momentum (asset-specific or BTC as sentiment proxy)
+        # Momentum and Fear & Greed only make sense for price markets, where
+        # is_price_above gives the adjustment a meaningful sign.  For
+        # politics/tech/events there is no above/below notion — is_price_above
+        # defaults to False (or flips on accidental wording like "reach"), so
+        # applying these signals just injected sign noise.  Skip them entirely;
+        # their contributions stay 0.0 → feat_mom_lo / feat_fg_lo = 0.0.
+        is_non_price = is_politics or is_tech or is_events
+
+        # Signal 1: price momentum (asset-specific; price markets only)
+        _momentum_contribution = 0.0
+        if not is_non_price:
             if is_btc:
                 momentum = ext.btc_change_24h
                 asset_label = "BTC"
             elif is_eth:
                 momentum = ext.eth_change_24h
                 asset_label = "ETH"
-        elif is_politics or is_tech or is_events:
-            momentum = ext.btc_change_24h
-            asset_label = "BTC(sentiment)"
             else:
                 momentum = ext.total_market_cap_change
                 asset_label = "total mktcap"
 
-        _momentum_contribution = 0.0
             if abs(momentum) > 2:
                 momentum_adj = math.tanh(momentum / 20) * 0.15
-            if is_politics or is_tech or is_events:
-                momentum_adj *= 0.5
                 _momentum_contribution = momentum_adj if is_price_above else -momentum_adj
                 adjustments.append(_momentum_contribution)
                 sources.append(f"{asset_label} 24h: {momentum:+.1f}%")
 
-        # Signal 2: Fear & Greed
+        # Signal 2: Fear & Greed (price markets only)
+        _fg_contribution = 0.0
+        if not is_non_price:
             fg = ext.fear_greed_index
             if fg > 70:
                 fg_adj = 0.06

tests/test_bayesian_macro_signals.py

@@ -0,0 +1,123 @@
+"""
+Tests for FASE 3 — macro signals (momentum, Fear & Greed) must not apply to
+non-price markets (politics / tech / events).
+
+Regression: for "Will X win the election?"-style questions, is_price_above is
+False, so positive BTC momentum and high Fear & Greed were sign-flipped into
+evidence AGAINST the YES outcome. The fix skips both signals entirely for
+politics/tech/events, leaving their contributions (and feat_mom_lo /
+feat_fg_lo) at 0.0.
+
+evaluate_market only returns a TradingSignal on the TRADE path; on skips it
+returns None but always emits a structured log line containing the per-feature
+log-odds (fg_lo=… mom_lo=…). The tests parse that line via caplog.
+"""
+import asyncio
+import logging
+import math
+import re
+
+import pytest
+
+from bot.data.external import ExternalSignals
+from bot.data.polymarket import Market
+from bot.strategy.bayesian import BayesianStrategy
+
+FEAT_RE = re.compile(r"fg_lo=([+-]\d+\.\d+) mom_lo=([+-]\d+\.\d+)")
+
+
+def _make_market(question: str, category: str) -> Market:
+    return Market(
+        id="mkt-test-1",
+        condition_id="cond-test-1",
+        question=question,
+        yes_token_id="yes-tok",
+        no_token_id="no-tok",
+        yes_price=0.50,
+        no_price=0.50,
+        volume_24h=50_000.0,
+        end_date="2026-07-15T00:00:00Z",
+        active=True,
+        category=category,
+    )
+
+
+def _make_signals() -> ExternalSignals:
+    # Strong bullish macro environment: BTC +10%, extreme greed.
+    return ExternalSignals(
+        btc_price=100_000.0,
+        btc_change_24h=10.0,
+        eth_price=4_000.0,
+        eth_change_24h=8.0,
+        btc_dominance=50.0,
+        fear_greed_index=80,
+        fear_greed_label="greed",
+        total_market_cap_change=5.0,
+        valid=True,
+    )
+
+
+def _evaluate_and_parse_feats(question: str, category: str, caplog) -> tuple[float, float]:
+    """Run BayesianStrategy.evaluate and return (feat_fg_lo, feat_mom_lo) from the audit log."""
+    strategy = BayesianStrategy(news=None, manifold=None, db=None)
+    market = _make_market(question, category)
+    with caplog.at_level(logging.INFO, logger="bot.strategy.bayesian"):
+        asyncio.run(
+            strategy.evaluate(market, _make_signals(), occupied_families=set())
+        )
+    for record in caplog.records:
+        m = FEAT_RE.search(record.getMessage())
+        if m:
+            return float(m.group(1)), float(m.group(2))
+    pytest.fail(
+        "No SKIP_EDGE_NET/TRADE log line with feature contributions found; "
+        f"got: {[r.getMessage() for r in caplog.records]}"
+    )
+
+
+def test_politics_market_ignores_momentum_and_fear_greed(caplog):
+    """Political market with BTC +10% and F&G=80 → both contributions 0.0."""
+    feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
+        "Will John Smith win the election?", "politics", caplog
+    )
+    assert feat_mom_lo == 0.0
+    assert feat_fg_lo == 0.0
+    # The signal sources must not mention momentum or Fear & Greed either.
+    full_log = "\n".join(r.getMessage() for r in caplog.records)
+    assert "Fear&Greed" not in full_log
+    assert "24h" not in full_log
+
+
+def test_tech_and_events_markets_ignore_macro_signals(caplog):
+    for category in ("tech", "events"):
+        caplog.clear()
+        feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
+            "Will the product launch happen this quarter?", category, caplog
+        )
+        assert feat_mom_lo == 0.0, f"momentum applied to {category} market"
+        assert feat_fg_lo == 0.0, f"Fear&Greed applied to {category} market"
+
+
+def test_btc_market_keeps_momentum_and_fear_greed(caplog):
+    """BTC price market with BTC +10% and F&G=80 → current behavior preserved."""
+    feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
+        "Will Bitcoin be above $150,000 on July 1?", "crypto/finance", caplog
+    )
+    assert feat_mom_lo > 0
+    assert feat_fg_lo > 0
+    # Exact values: is_price_above=True ("above"), so contributions are positive.
+    # momentum: tanh(10/20) * 0.15, ×2 → log-odds.  F&G>70: +0.06, ×2 → log-odds.
+    assert feat_mom_lo == pytest.approx(math.tanh(10 / 20) * 0.15 * 2, abs=1e-4)
+    assert feat_fg_lo == pytest.approx(0.06 * 2, abs=1e-4)
+    full_log = "\n".join(r.getMessage() for r in caplog.records)
+    assert "Fear&Greed: 80 (greed)" in full_log
+    assert "BTC 24h: +10.0%" in full_log
+
+
+def test_btc_below_market_sign_flip_preserved(caplog):
+    """'below' market: bullish macro lowers YES probability (sign flip intact)."""
+    feat_fg_lo, feat_mom_lo = _evaluate_and_parse_feats(
+        "Will Bitcoin drop below $50,000 by August?", "crypto/finance", caplog
+    )
+    assert feat_mom_lo < 0
+    assert feat_fg_lo < 0