feat: expand market coverage to politics, tech, and events categories

- 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>

bot/strategy/bayesian.py

@@ -66,6 +66,7 @@ class BayesianStrategy:
         Returns None if no actionable opportunity.
         """
         question_lower = market.question.lower()
+        category = market.category  # set by PolymarketClient
 
         # Classify what kind of market this is
         is_price_above = any(w in question_lower for w in ["above", "over", "exceed", "higher", "atleast", "reach"])
@@ -85,8 +86,14 @@ class BayesianStrategy:
         is_macro = any(
             w in question_lower for w in ["nasdaq", "s&p", "sp500", "inflation", "fed rate", "interest rate", "tariff"]
         )
+        is_politics = category == "politics"
+        is_tech = category == "tech"
+        is_events = category == "events"
 
-        is_any_supported = is_btc or is_eth or is_altcoin or is_general_crypto or is_macro
+        is_any_supported = (
+            is_btc or is_eth or is_altcoin or is_general_crypto or is_macro
+            or is_politics or is_tech or is_events
+        )
         if not is_any_supported:
             log.debug("Skipping unsupported market: %s", market.question[:60])
             return None
@@ -103,12 +110,17 @@ class BayesianStrategy:
         adjustments: list[float] = []
 
         # Signal 1: Price momentum (asset-specific or total market cap as proxy)
+        # For politics/tech/events use BTC as a broad sentiment proxy.
         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:
+            # BTC as risk-sentiment proxy for non-crypto categories
+            momentum = ext.btc_change_24h
+            asset_label = "BTC(sentiment)"
         else:
             # Altcoins and general crypto: use total market cap change as proxy
             momentum = ext.total_market_cap_change
@@ -116,6 +128,9 @@ class BayesianStrategy:
 
         if abs(momentum) > 2:
             momentum_adj = math.tanh(momentum / 20) * 0.15  # Max ±15%
+            # For non-directional markets (politics/events/tech), momentum is weaker signal
+            if is_politics or is_tech or is_events:
+                momentum_adj *= 0.5
             adjustments.append(momentum_adj if is_price_above else -momentum_adj)
             sources.append(f"{asset_label} 24h: {momentum:+.1f}%")
 
@@ -143,9 +158,11 @@ class BayesianStrategy:
             adjustments.append(dom_adj)
             sources.append(f"BTC dom: {ext.btc_dominance:.1f}% (low → alt season)")
 
-        # Macro markets: lower weight, rely only on Fear&Greed signal already added
-        # Cap confidence below for macro to reflect weaker signal quality
-        confidence_cap = 0.70 if is_macro else 0.90
+        # Macro/politics/tech/events: cap confidence lower to reflect weaker signal quality
+        if is_macro or is_politics or is_tech or is_events:
+            confidence_cap = 0.65
+        else:
+            confidence_cap = 0.90
 
         # Compute posterior using log-odds updating
         log_odds_prior = math.log(prior / (1 - prior))