313 lines
12 KiB
Python
Executable File
313 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Coin Hunter hourly review engine."""
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import json
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import os
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import sys
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from datetime import datetime, timezone, timedelta
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from pathlib import Path
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import ccxt
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from .logger import get_logs_last_n_hours, log_error
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from .runtime import get_runtime_paths, load_env_file
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PATHS = get_runtime_paths()
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ENV_FILE = PATHS.env_file
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REVIEW_DIR = PATHS.reviews_dir
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CST = timezone(timedelta(hours=8))
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def load_env():
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load_env_file(PATHS)
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def get_exchange():
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load_env()
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ex = ccxt.binance({
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"apiKey": os.getenv("BINANCE_API_KEY"),
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"secret": os.getenv("BINANCE_API_SECRET"),
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"options": {"defaultType": "spot"},
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"enableRateLimit": True,
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})
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ex.load_markets()
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return ex
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def ensure_review_dir():
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REVIEW_DIR.mkdir(parents=True, exist_ok=True)
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def norm_symbol(symbol: str) -> str:
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s = symbol.upper().replace("-", "").replace("_", "")
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if "/" in s:
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return s
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if s.endswith("USDT"):
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return s[:-4] + "/USDT"
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return s
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def fetch_current_price(ex, symbol: str):
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try:
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return float(ex.fetch_ticker(norm_symbol(symbol))["last"])
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except Exception:
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return None
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def analyze_trade(trade: dict, ex) -> dict:
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symbol = trade.get("symbol")
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price = trade.get("price")
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action = trade.get("action", "")
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current_price = fetch_current_price(ex, symbol) if symbol else None
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pnl_estimate = None
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outcome = "neutral"
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if price and current_price and symbol:
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change_pct = (current_price - float(price)) / float(price) * 100
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if action == "BUY":
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pnl_estimate = round(change_pct, 2)
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outcome = "good" if change_pct > 2 else "bad" if change_pct < -2 else "neutral"
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elif action == "SELL_ALL":
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pnl_estimate = round(-change_pct, 2)
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# Lowered missed threshold: >2% is a missed opportunity in short-term trading
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outcome = "good" if change_pct < -2 else "missed" if change_pct > 2 else "neutral"
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return {
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"timestamp": trade.get("timestamp"),
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"symbol": symbol,
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"action": action,
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"decision_id": trade.get("decision_id"),
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"execution_price": price,
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"current_price": current_price,
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"pnl_estimate_pct": pnl_estimate,
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"outcome_assessment": outcome,
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}
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def analyze_hold_passes(decisions: list, ex) -> list:
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"""Check HOLD decisions where an opportunity was explicitly PASSed but later rallied."""
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misses = []
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for d in decisions:
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if d.get("decision") != "HOLD":
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continue
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analysis = d.get("analysis")
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if not isinstance(analysis, dict):
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continue
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opportunities = analysis.get("opportunities_evaluated", [])
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market_snapshot = d.get("market_snapshot", {})
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if not opportunities or not market_snapshot:
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continue
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for op in opportunities:
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verdict = op.get("verdict", "")
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if "PASS" not in verdict and "pass" not in verdict:
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continue
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symbol = op.get("symbol", "")
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# Try to extract decision-time price from market_snapshot
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snap = market_snapshot.get(symbol) or market_snapshot.get(symbol.replace("/", ""))
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if not snap:
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continue
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decision_price = None
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if isinstance(snap, dict):
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decision_price = float(snap.get("lastPrice", 0)) or float(snap.get("last", 0))
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elif isinstance(snap, (int, float, str)):
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decision_price = float(snap)
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if not decision_price:
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continue
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current_price = fetch_current_price(ex, symbol)
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if not current_price:
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continue
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change_pct = (current_price - decision_price) / decision_price * 100
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if change_pct > 3: # >3% rally after being passed = missed watch
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misses.append({
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"timestamp": d.get("timestamp"),
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"symbol": symbol,
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"decision_price": round(decision_price, 8),
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"current_price": round(current_price, 8),
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"change_pct": round(change_pct, 2),
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"verdict_snippet": verdict[:80],
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})
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return misses
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def analyze_cash_misses(decisions: list, ex) -> list:
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"""If portfolio was mostly USDT but a watchlist coin rallied >5%, flag it."""
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misses = []
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watchlist = set()
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for d in decisions:
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snap = d.get("market_snapshot", {})
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if isinstance(snap, dict):
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for k in snap.keys():
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if k.endswith("USDT"):
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watchlist.add(k)
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for d in decisions:
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ts = d.get("timestamp")
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balances = d.get("balances") or d.get("balances_before", {})
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if not balances:
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continue
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total = sum(float(v) if isinstance(v, (int, float, str)) else 0 for v in balances.values())
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usdt = float(balances.get("USDT", 0))
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if total == 0 or (usdt / total) < 0.9:
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continue
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# Portfolio mostly cash — check watchlist performance
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snap = d.get("market_snapshot", {})
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if not isinstance(snap, dict):
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continue
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for symbol, data in snap.items():
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if not symbol.endswith("USDT"):
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continue
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decision_price = None
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if isinstance(data, dict):
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decision_price = float(data.get("lastPrice", 0)) or float(data.get("last", 0))
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elif isinstance(data, (int, float, str)):
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decision_price = float(data)
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if not decision_price:
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continue
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current_price = fetch_current_price(ex, symbol)
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if not current_price:
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continue
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change_pct = (current_price - decision_price) / decision_price * 100
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if change_pct > 5:
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misses.append({
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"timestamp": ts,
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"symbol": symbol,
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"decision_price": round(decision_price, 8),
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"current_price": round(current_price, 8),
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"change_pct": round(change_pct, 2),
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})
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# Deduplicate by symbol keeping the worst miss
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seen = {}
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for m in misses:
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sym = m["symbol"]
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if sym not in seen or m["change_pct"] > seen[sym]["change_pct"]:
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seen[sym] = m
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return list(seen.values())
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def generate_review(hours: int = 1) -> dict:
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decisions = get_logs_last_n_hours("decisions", hours)
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trades = get_logs_last_n_hours("trades", hours)
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errors = get_logs_last_n_hours("errors", hours)
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review = {
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"review_period_hours": hours,
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"review_timestamp": datetime.now(CST).isoformat(),
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"total_decisions": len(decisions),
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"total_trades": len(trades),
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"total_errors": len(errors),
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"decision_quality": [],
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"stats": {},
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"insights": [],
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"recommendations": [],
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}
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if not decisions and not trades:
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review["insights"].append("本周期无决策/交易记录")
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return review
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ex = get_exchange()
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outcomes = {"good": 0, "neutral": 0, "bad": 0, "missed": 0}
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pnl_samples = []
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for trade in trades:
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analysis = analyze_trade(trade, ex)
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review["decision_quality"].append(analysis)
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outcomes[analysis["outcome_assessment"]] += 1
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if analysis["pnl_estimate_pct"] is not None:
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pnl_samples.append(analysis["pnl_estimate_pct"])
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# New: analyze missed opportunities from HOLD / cash decisions
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hold_pass_misses = analyze_hold_passes(decisions, ex)
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cash_misses = analyze_cash_misses(decisions, ex)
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total_missed = outcomes["missed"] + len(hold_pass_misses) + len(cash_misses)
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review["stats"] = {
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"good_decisions": outcomes["good"],
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"neutral_decisions": outcomes["neutral"],
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"bad_decisions": outcomes["bad"],
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"missed_opportunities": total_missed,
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"missed_sell_all": outcomes["missed"],
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"missed_hold_passes": len(hold_pass_misses),
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"missed_cash_sits": len(cash_misses),
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"avg_estimated_edge_pct": round(sum(pnl_samples) / len(pnl_samples), 2) if pnl_samples else None,
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}
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if errors:
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review["insights"].append(f"本周期出现 {len(errors)} 次执行/系统错误，健壮性需优先关注")
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if outcomes["bad"] > outcomes["good"]:
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review["insights"].append("最近交易质量偏弱，建议降低交易频率或提高入场门槛")
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if total_missed > 0:
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parts = []
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if outcomes["missed"]:
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parts.append(f"卖出后继续上涨 {outcomes['missed']} 次")
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if hold_pass_misses:
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parts.append(f"PASS 后错失 {len(hold_pass_misses)} 次")
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if cash_misses:
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parts.append(f"空仓观望错失 {len(cash_misses)} 次")
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review["insights"].append("存在错失机会: " + "，".join(parts) + "，建议放宽趋势跟随或入场条件")
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if outcomes["good"] >= max(1, outcomes["bad"] + total_missed):
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review["insights"].append("近期决策总体可接受")
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if not trades and decisions:
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review["insights"].append("有决策无成交，可能是观望、最小成交额限制或执行被拦截")
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if len(trades) < len(decisions) * 0.1 and decisions:
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review["insights"].append("大量决策未转化为交易，需检查执行门槛（最小成交额/精度/手续费缓冲）是否过高")
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if hold_pass_misses:
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for m in hold_pass_misses[:3]:
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review["insights"].append(f"HOLD 时 PASS 了 {m['symbol']}，之后上涨 {m['change_pct']}%")
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if cash_misses:
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for m in cash_misses[:3]:
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review["insights"].append(f"持仓以 USDT 为主时 {m['symbol']} 上涨 {m['change_pct']}%")
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review["recommendations"] = [
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"优先检查最小成交额/精度拒单是否影响小资金执行",
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"若连续两个复盘周期 edge 为负，下一小时减少换仓频率",
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"若错误日志增加，优先进入防守模式（多持 USDT）",
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]
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return review
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def save_review(review: dict):
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ensure_review_dir()
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ts = datetime.now(CST).strftime("%Y%m%d_%H%M%S")
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path = REVIEW_DIR / f"review_{ts}.json"
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path.write_text(json.dumps(review, indent=2, ensure_ascii=False), encoding="utf-8")
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return str(path)
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def print_review(review: dict):
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print("=" * 50)
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print("📊 Coin Hunter 小时复盘报告")
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print(f"复盘时间: {review['review_timestamp']}")
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print(f"统计周期: 过去 {review['review_period_hours']} 小时")
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print(f"总决策数: {review['total_decisions']} | 总交易数: {review['total_trades']} | 总错误数: {review['total_errors']}")
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stats = review.get("stats", {})
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print("\n决策质量统计:")
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print(f" ✓ 优秀: {stats.get('good_decisions', 0)}")
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print(f" ○ 中性: {stats.get('neutral_decisions', 0)}")
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print(f" ✗ 失误: {stats.get('bad_decisions', 0)}")
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print(f" ↗ 错过机会: {stats.get('missed_opportunities', 0)}")
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if stats.get("avg_estimated_edge_pct") is not None:
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print(f" 平均估计 edge: {stats['avg_estimated_edge_pct']}%")
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if review.get("insights"):
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print("\n💡 见解:")
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for item in review["insights"]:
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print(f" • {item}")
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if review.get("recommendations"):
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print("\n🔧 优化建议:")
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for item in review["recommendations"]:
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print(f" • {item}")
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print("=" * 50)
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def main():
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try:
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hours = int(sys.argv[1]) if len(sys.argv) > 1 else 1
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review = generate_review(hours)
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path = save_review(review)
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print_review(review)
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print(f"复盘已保存至: {path}")
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except Exception as e:
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log_error("review_engine", e)
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raise
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if __name__ == "__main__":
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main()
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