refactor: split precheck_core and migrate commands to commands/

- Split 900-line precheck_core.py into 9 focused modules:
  precheck_constants, time_utils, data_utils, state_manager,
  market_data, candidate_scoring, snapshot_builder,
  adaptive_profile, trigger_analyzer
- Remove dead auto_trader command and module
- Migrate 7 root-level command modules into commands/:
  check_api, doctor, external_gate, init_user_state,
  market_probe, paths, rotate_external_gate_log
- Keep thin backward-compatible facades in root package
- Update cli.py MODULE_MAP to route through commands/
- Verified compileall and smoke tests for all key commands

src/coinhunter/services/market_data.py

@@ -0,0 +1,136 @@
+"""Market data fetching and metric computation for precheck."""
+
+from __future__ import annotations
+
+import os
+
+import ccxt
+
+from .data_utils import norm_symbol, to_float
+from .precheck_constants import BLACKLIST, MAX_PRICE_CAP, MIN_CHANGE_PCT
+from .time_utils import utc_now
+
+
+def get_exchange():
+    from ..runtime import load_env_file
+    from .precheck_constants import ENV_FILE
+
+    load_env_file(ENV_FILE)
+    api_key = os.getenv("BINANCE_API_KEY")
+    secret = os.getenv("BINANCE_API_SECRET")
+    if not api_key or not secret:
+        raise RuntimeError("Missing BINANCE_API_KEY or BINANCE_API_SECRET in ~/.hermes/.env")
+    ex = ccxt.binance({
+        "apiKey": api_key,
+        "secret": secret,
+        "options": {"defaultType": "spot"},
+        "enableRateLimit": True,
+    })
+    ex.load_markets()
+    return ex
+
+
+def fetch_ohlcv_batch(ex, symbols: set, timeframe: str, limit: int):
+    results = {}
+    for sym in sorted(symbols):
+        try:
+            ohlcv = ex.fetch_ohlcv(sym, timeframe=timeframe, limit=limit)
+            if ohlcv and len(ohlcv) >= 2:
+                results[sym] = ohlcv
+        except Exception:
+            pass
+    return results
+
+
+def compute_ohlcv_metrics(ohlcv_1h, ohlcv_4h, current_price, volume_24h=None):
+    metrics = {}
+    if ohlcv_1h and len(ohlcv_1h) >= 2:
+        closes = [c[4] for c in ohlcv_1h]
+        volumes = [c[5] for c in ohlcv_1h]
+        metrics["change_1h_pct"] = round((closes[-1] - closes[-2]) / closes[-2] * 100, 2) if closes[-2] != 0 else None
+        if len(closes) >= 5:
+            metrics["change_4h_pct"] = round((closes[-1] - closes[-5]) / closes[-5] * 100, 2) if closes[-5] != 0 else None
+        recent_vol = sum(volumes[-4:]) / 4 if len(volumes) >= 4 else None
+        metrics["volume_1h_avg"] = round(recent_vol, 2) if recent_vol else None
+        highs = [c[2] for c in ohlcv_1h[-4:]]
+        lows = [c[3] for c in ohlcv_1h[-4:]]
+        metrics["high_4h"] = round(max(highs), 8) if highs else None
+        metrics["low_4h"] = round(min(lows), 8) if lows else None
+
+    if ohlcv_4h and len(ohlcv_4h) >= 2:
+        closes_4h = [c[4] for c in ohlcv_4h]
+        volumes_4h = [c[5] for c in ohlcv_4h]
+        metrics["change_4h_pct_from_4h"] = round((closes_4h[-1] - closes_4h[-2]) / closes_4h[-2] * 100, 2) if closes_4h[-2] != 0 else None
+        recent_vol_4h = sum(volumes_4h[-2:]) / 2 if len(volumes_4h) >= 2 else None
+        metrics["volume_4h_avg"] = round(recent_vol_4h, 2) if recent_vol_4h else None
+        highs_4h = [c[2] for c in ohlcv_4h]
+        lows_4h = [c[3] for c in ohlcv_4h]
+        metrics["high_24h_calc"] = round(max(highs_4h), 8) if highs_4h else None
+        metrics["low_24h_calc"] = round(min(lows_4h), 8) if lows_4h else None
+        if highs_4h and lows_4h:
+            avg_price = sum(closes_4h) / len(closes_4h)
+            metrics["volatility_4h_pct"] = round((max(highs_4h) - min(lows_4h)) / avg_price * 100, 2)
+
+    if current_price:
+        if metrics.get("high_4h"):
+            metrics["distance_from_4h_high_pct"] = round((metrics["high_4h"] - current_price) / metrics["high_4h"] * 100, 2)
+        if metrics.get("low_4h"):
+            metrics["distance_from_4h_low_pct"] = round((current_price - metrics["low_4h"]) / metrics["low_4h"] * 100, 2)
+        if metrics.get("high_24h_calc"):
+            metrics["distance_from_24h_high_pct"] = round((metrics["high_24h_calc"] - current_price) / metrics["high_24h_calc"] * 100, 2)
+        if metrics.get("low_24h_calc"):
+            metrics["distance_from_24h_low_pct"] = round((current_price - metrics["low_24h_calc"]) / metrics["low_24h_calc"] * 100, 2)
+
+    if volume_24h and volume_24h > 0 and metrics.get("volume_1h_avg"):
+        daily_avg_1h = volume_24h / 24
+        metrics["volume_1h_multiple"] = round(metrics["volume_1h_avg"] / daily_avg_1h, 2)
+    if volume_24h and volume_24h > 0 and metrics.get("volume_4h_avg"):
+        daily_avg_4h = volume_24h / 6
+        metrics["volume_4h_multiple"] = round(metrics["volume_4h_avg"] / daily_avg_4h, 2)
+
+    return metrics
+
+
+def enrich_candidates_and_positions(global_candidates, candidate_layers, positions_view, tickers, ex):
+    symbols = set()
+    for c in global_candidates:
+        symbols.add(c["symbol"])
+    for p in positions_view:
+        sym = p.get("symbol")
+        if sym:
+            sym_ccxt = norm_symbol(sym)
+            symbols.add(sym_ccxt)
+
+    ohlcv_1h = fetch_ohlcv_batch(ex, symbols, "1h", 24)
+    ohlcv_4h = fetch_ohlcv_batch(ex, symbols, "4h", 12)
+
+    def _apply(target_list):
+        for item in target_list:
+            sym = item.get("symbol")
+            if not sym:
+                continue
+            sym_ccxt = norm_symbol(sym)
+            v24h = to_float(tickers.get(sym_ccxt, {}).get("quoteVolume"))
+            metrics = compute_ohlcv_metrics(
+                ohlcv_1h.get(sym_ccxt),
+                ohlcv_4h.get(sym_ccxt),
+                item.get("price") or item.get("last_price"),
+                volume_24h=v24h,
+            )
+            item["metrics"] = metrics
+
+    _apply(global_candidates)
+    for band_list in candidate_layers.values():
+        _apply(band_list)
+    _apply(positions_view)
+    return global_candidates, candidate_layers, positions_view
+
+
+def regime_from_pct(pct: float | None) -> str:
+    if pct is None:
+        return "unknown"
+    if pct >= 2.0:
+        return "bullish"
+    if pct <= -2.0:
+        return "bearish"
+    return "neutral"