411 lines
15 KiB
Python
411 lines
15 KiB
Python
#!/usr/bin/env python3
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"""strategy_evaluator.py — 策略双维度评估引擎
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两阶段评估模型：
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阶段一（策略制定→价格达标）：
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理论：策略设定的买入区/止损/止盈 → 股价是否达到过这些价位 → 理论盈亏
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实际：老爸是否按策略执行 → 实际买入/卖出价格 → 实际盈亏
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阶段二（价格回落后→新止损验证）：
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理论：价格未按预期走 → 给出新止损 → 股价是否继续下跌验证止损正确性
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实际：老爸实际卖出价格 → 对比新止损 → 验证止损有效性
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输出：写入 decisions.json 的 evaluation 字段 + accuracy_stats.json
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"""
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import json
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import urllib.request
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import os
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import sys
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from datetime import datetime, timedelta
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from pathlib import Path
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DATA_DIR = Path(__file__).parent / "data"
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DECISIONS_PATH = DATA_DIR / "decisions.json"
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PORTFOLIO_PATH = DATA_DIR / "portfolio.json"
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ACCURACY_PATH = DATA_DIR / "accuracy_stats.json"
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UA = "Mozilla/5.0"
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def load_json(path, default=None):
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try:
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with open(path, encoding="utf-8") as f:
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return json.load(f)
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except (FileNotFoundError, json.JSONDecodeError):
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return {} if default is None else default
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def save_json(path, data):
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Path(path).parent.mkdir(parents=True, exist_ok=True)
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with open(path, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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def fetch_prices(codes):
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"""批量拉腾讯行情"""
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if not codes:
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return {}
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symbols = []
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code_map = {}
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for c in codes:
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sym = f"hk{c}" if len(c) == 5 else f"sh{c}" if c.startswith(("5", "6", "9")) else f"sz{c}"
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symbols.append(sym)
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code_map[sym] = c
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url = f"http://qt.gtimg.cn/q={','.join(symbols)}"
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try:
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req = urllib.request.Request(url, headers={"User-Agent": UA})
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resp = urllib.request.urlopen(req, timeout=10)
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text = resp.read().decode("gbk")
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except Exception as e:
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print(f"行情拉取失败: {e}", file=sys.stderr)
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return {}
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prices = {}
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for line in text.strip().split("\n"):
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line = line.strip()
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if not line or "=" not in line:
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continue
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raw = line.split("=", 1)[1].strip().strip('"').strip(";")
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fields = raw.split("~")
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if len(fields) < 33:
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continue
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sym = line.split("=", 1)[0].strip().lstrip("v_")
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orig = code_map.get(sym)
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if not orig:
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continue
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prices[orig] = {
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"name": fields[1],
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"price": float(fields[3]) if fields[3] else 0,
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"prev_close": float(fields[4]) if fields[4] else 0,
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"change_pct": float(fields[32]) if fields[32] else 0,
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"high": float(fields[33]) if fields[33] else 0,
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"low": float(fields[34]) if fields[34] else 0,
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}
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return prices
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def evaluate_phase1(decision, price_info, holding):
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"""
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阶段一评估：策略制定→价格是否达到过目标价位
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返回 evaluation dict
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"""
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trig = decision.get("trigger", {})
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code = decision["code"]
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name = decision.get("name", code)
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price = price_info.get("price", 0)
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change = price_info.get("change_pct", 0)
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# 策略区间 — 支持两种数据格式：
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# 1) trigger 子对象（含 entry_zone/stop_loss/take_profit）
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# 2) 顶层字段（entry_low+entry_high / stop_loss / take_profit）
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el = trig.get("entry_zone", "")
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sl = trig.get("stop_loss", "")
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tp = trig.get("take_profit", "")
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if not el and decision.get("entry_low") is not None:
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el_low = decision.get("entry_low")
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el_high = decision.get("entry_high")
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el = f"{el_low}~{el_high}" if el_low is not None and el_high is not None else ""
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if not sl:
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sl = decision.get("stop_loss", "")
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if not tp:
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tp = decision.get("take_profit", "")
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el_low = el_high = None
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if el and "~" in str(el):
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try:
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parts = str(el).split("~")
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el_low, el_high = float(parts[0]), float(parts[1])
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except:
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pass
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sl_p = float(sl) if sl else None
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tp_p = float(tp) if tp else None
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# 持仓信息
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cost = holding.get("cost", 0) if holding else 0
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shares = holding.get("shares", 0) if holding else 0
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position_pct = holding.get("position_pct", 0) if holding else 0
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# 理论盈亏计算（基于策略区间中值）
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entry_mid = (el_low + el_high) / 2 if el_low and el_high else price
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theoretical_pnl_pct = (tp_p - entry_mid) / entry_mid * 100 if tp_p and entry_mid else 0
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theoretical_pnl_amount = theoretical_pnl_pct / 100 * entry_mid * (shares or 100) / 100 if shares else 0
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# 实际盈亏
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actual_pnl_pct = (price - cost) / cost * 100 if cost > 0 and price > 0 else 0
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actual_pnl_amount = actual_pnl_pct / 100 * cost * shares if cost > 0 and shares > 0 else 0
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# === R/R 盈亏比计算（2026-06-18 新增）===
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# 以现价为基准计算：向下风险（到止损）vs 向上空间（到止盈）
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# 止损价作为风险基准，止盈价作为收益目标
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rr = None
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rr_risk_pct = None
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rr_reward_pct = None
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rr_interpretation = ""
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rr_level = ""
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if sl_p and tp_p and price > 0 and sl_p > 0 and price > sl_p:
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rr_risk_pct = round((price - sl_p) / price * 100, 2) # 距止损%
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rr_reward_pct = round((tp_p - price) / price * 100, 2) # 距止盈%
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rr = round(rr_reward_pct / rr_risk_pct, 2) if rr_risk_pct > 0 else None
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# 判断场景：深套/已持仓盈利/已持仓亏损/新买入
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is_deep_loss = actual_pnl_pct < -20
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has_profit = actual_pnl_pct >= 0
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held = (cost > 0 and shares > 0)
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if not held:
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# 新买入/自选股场景
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if rr is not None and rr < 1.5:
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rr_level = "⚠️盈亏比不足"
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rr_interpretation = f"新买入要求R/R≥1.5，现{rr}每亏1元仅赚{rr}元，不建议买入"
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elif rr is not None and rr < 2.0:
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rr_level = "⚠️盈亏比偏低"
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rr_interpretation = f"新买入要求R/R≥1.5，现{rr}每亏1元赚{rr}元，谨慎"
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else:
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rr_level = "R/R达标"
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rr_interpretation = f"每亏1元赚{rr}元，盈亏比合理"
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elif is_deep_loss:
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# 深套场景 — 不限R/R
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rr_level = "深套持有"
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rr_interpretation = f"浮亏{actual_pnl_pct:.1f}%>20%深套，R/R={rr}仅参考，不补不割等反弹"
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elif has_profit:
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# 已持仓盈利场景
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if rr is not None and rr < 0.5:
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rr_level = "⚠️R/R极低"
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rr_interpretation = f"盈利持仓R/R={rr}，每亏1元仅赚{rr}元，考虑止盈或上移止损保护利润"
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elif rr is not None and rr < 1.5:
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rr_level = "⚠️R/R偏低"
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rr_interpretation = f"盈利持仓R/R={rr}，不建议加仓，当前仓位持有观察"
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else:
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rr_level = "R/R合理"
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rr_interpretation = f"盈利持仓R/R={rr}，每亏1元赚{rr}元，持有合理"
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else:
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# 已持仓浮亏(但非深套)
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if rr is not None and rr < 0.5:
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rr_level = "⚠️R/R极低"
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rr_interpretation = f"浮亏持仓R/R={rr}，每亏1元仅赚{rr}元，不建议加仓，关注止损"
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elif rr is not None and rr < 1.0:
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rr_level = "⚠️R/R不足"
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rr_interpretation = f"浮亏持仓要求加仓R/R≥1.0，现{rr}，不加仓"
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else:
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rr_level = "R/R可接受"
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rr_interpretation = f"浮亏持仓R/R={rr}，每亏1元赚{rr}元，持有等反弹"
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elif price and sl_p and price <= sl_p:
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rr_level = "已跌破止损"
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rr_interpretation = f"现价{price}已破止损{sl_p}，R/R不适用"
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else:
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rr_interpretation = "止损或止盈缺失，无法计算R/R"
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# 当前状态判断
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status = "safe"
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if sl_p and price > 0 and price <= sl_p:
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status = "stop_loss_hit"
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elif tp_p and price > 0 and price >= tp_p:
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status = "take_profit_hit"
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elif el_low and el_high and price > 0 and el_low <= price <= el_high:
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status = "in_entry_zone"
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elif el_low and price > 0 and price < el_low:
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status = "below_entry"
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elif el_high and price > 0 and price > el_high:
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status = "above_entry"
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# 理论阶段评估
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theoretical = {
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"entry_zone": f"{el_low}~{el_high}" if el_low else "N/A",
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"stop_loss": sl_p,
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"take_profit": tp_p,
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"entry_mid_price": round(entry_mid, 2),
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"target_price": tp_p,
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"theoretical_pnl_pct": round(theoretical_pnl_pct, 2),
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"theoretical_pnl_amount": round(theoretical_pnl_amount, 2),
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"status": status,
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"current_price": price,
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"current_change_pct": change,
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"rr": rr,
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"rr_risk_pct": rr_risk_pct,
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"rr_reward_pct": rr_reward_pct,
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"rr_level": rr_level,
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}
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# 实际阶段评估
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actual = {
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"cost_price": cost,
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"shares": shares,
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"position_pct": position_pct,
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"actual_pnl_pct": round(actual_pnl_pct, 2),
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"actual_pnl_amount": round(actual_pnl_amount, 2),
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"status": status,
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"current_price": price,
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}
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return {
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"code": code,
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"name": name,
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"evaluated_at": datetime.now().isoformat(),
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"phase": 1,
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"theoretical": theoretical,
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"actual": actual,
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"rr_level": rr_level,
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"rr_interpretation": rr_interpretation,
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"summary": f"{name}({code}) | 损{sl_p}/盈{tp_p} | "
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f"现价{price}({change:+.2f}%) | "
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f"距损{rr_risk_pct}%/距盈{rr_reward_pct}% | RR={rr} | "
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f"{rr_level} | 理{theoretical_pnl_pct:+.1f}%实{actual_pnl_pct:+.1f}%",
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}
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def evaluate_phase2(decision, price_info, holding, prev_eval):
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"""
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阶段二评估：价格回落后→新止损验证
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需要 prev_eval 中记录了之前的目标价和新止损价
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"""
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trig = decision.get("trigger", {})
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code = decision["code"]
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name = decision.get("name", code)
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price = price_info.get("price", 0)
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sl = trig.get("stop_loss", "")
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if not sl:
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sl = decision.get("stop_loss", "")
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sl_p = float(sl) if sl else None
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# 从 prev_eval 中获取阶段一的止损
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prev_sl = None
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if prev_eval:
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prev_sl = prev_eval.get("theoretical", {}).get("stop_loss")
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# 检查新止损是否被跌破
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new_sl_hit = False
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days_to_hit = None
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if sl_p and price > 0 and price <= sl_p:
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new_sl_hit = True
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# 无法精确知道多少天跌破，标记为当前
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days_to_hit = 0
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result = {
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"code": code,
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"name": name,
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"evaluated_at": datetime.now().isoformat(),
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"phase": 2,
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"new_stop_loss": sl_p,
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"previous_stop_loss": prev_sl,
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"current_price": price,
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"new_sl_hit": new_sl_hit,
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"days_to_hit": days_to_hit,
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"summary": f"{name}({code}) 新止损{sl_p} {'已跌破' if new_sl_hit else '未触及'} 现价{price}",
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}
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return result
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def run():
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decisions = load_json(DECISIONS_PATH, {"decisions": []})
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portfolio = load_json(PORTFOLIO_PATH, {"holdings": []})
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holdings_map = {h["code"]: h for h in portfolio.get("holdings", [])}
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# 收集所有代码
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all_codes = [d["code"] for d in decisions["decisions"]]
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prices = fetch_prices(all_codes)
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results = []
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stats = {
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"phase1_correct": 0, "phase1_wrong": 0, "phase1_pending": 0,
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"phase2_correct": 0, "phase2_wrong": 0, "phase2_pending": 0,
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}
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for d in decisions["decisions"]:
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code = d["code"]
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pi = prices.get(code, {})
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h = holdings_map.get(code)
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# 获取已有的 evaluation 记录
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existing_eval = d.get("evaluation", [])
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# 阶段一评估
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eval1 = evaluate_phase1(d, pi, h)
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results.append(eval1)
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# 阶段二评估（如果有前次止损记录）
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prev_eval = existing_eval[-1] if existing_eval else None
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if prev_eval and prev_eval.get("phase") == 1:
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eval2 = evaluate_phase2(d, pi, h, prev_eval)
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results.append(eval2)
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# 更新 decisions.json 的 evaluation 字段
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d["evaluation"] = [e for e in [eval1] + ([eval2] if prev_eval and prev_eval.get("phase") == 1 else [])]
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# 保存更新
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save_json(DECISIONS_PATH, decisions)
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# 汇总统计
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for r in results:
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phase = r["phase"]
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if phase == 1:
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status = r["theoretical"]["status"]
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if status in ("take_profit_hit",):
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stats["phase1_correct"] += 1
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elif status in ("stop_loss_hit",):
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stats["phase1_wrong"] += 1
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else:
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stats["phase1_pending"] += 1
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elif phase == 2:
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if r.get("new_sl_hit"):
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stats["phase2_correct"] += 1
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else:
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stats["phase2_pending"] += 1
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# 写入 accuracy_stats
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accuracy = {
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"updated_at": datetime.now().isoformat(),
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"phase1": {
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"correct": stats["phase1_correct"],
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"wrong": stats["phase1_wrong"],
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"pending": stats["phase1_pending"],
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"accuracy_pct": round(stats["phase1_correct"] / max(stats["phase1_correct"] + stats["phase1_wrong"], 1) * 100, 1),
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},
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"phase2": {
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"correct": stats["phase2_correct"],
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"wrong": stats["phase2_wrong"],
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"pending": stats["phase2_pending"],
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"accuracy_pct": round(stats["phase2_correct"] / max(stats["phase2_correct"] + stats["phase2_wrong"], 1) * 100, 1),
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},
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"total_evaluated": len(results),
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"details": [r["summary"] for r in results],
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}
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save_json(ACCURACY_PATH, accuracy)
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# 输出报告
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print("=" * 70)
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print(f"策略双维度评估报告 | {datetime.now().strftime('%Y-%m-%d %H:%M')}")
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print("=" * 70)
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print(f"\n📊 阶段一（策略制定→价格达标）")
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print(f" 正确(达到止盈): {stats['phase1_correct']}")
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print(f" 错误(跌破止损): {stats['phase1_wrong']}")
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print(f" 待验证: {stats['phase1_pending']}")
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print(f" 准确率: {accuracy['phase1']['accuracy_pct']}%")
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print(f"\n📊 阶段二（价格回落→新止损验证）")
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print(f" 正确(新止损验证有效): {stats['phase2_correct']}")
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print(f" 错误: {stats['phase2_wrong']}")
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print(f" 待验证: {stats['phase2_pending']}")
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print(f"\\n📋 逐股评估:")
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for r in results:
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print(f" {r['summary']}")
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if r.get("rr_interpretation"):
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print(f" → {r['rr_interpretation']}")
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# R/R 统计
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rr_count = sum(1 for r in results if r.get("rr_level") and r["phase"] == 1)
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rr_warn = sum(1 for r in results if "⚠️" in r.get("rr_level", "") and r["phase"] == 1)
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print(f"\\n📊 盈亏比R/R统计:")
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print(f" 有R/R评估: {rr_count}只 | ⚠️异常: {rr_warn}只")
|
||
for r in results:
|
||
if r["phase"] == 1 and r.get("rr_level"):
|
||
level = r["rr_level"]
|
||
if "⚠️" in level:
|
||
print(f" ⚠️ {r['summary'].split('|')[0].strip()} → {level} | {r['rr_interpretation']}")
|
||
|
||
print(f"\\n✅ 评估完成，已写入 decisions.json 和 accuracy_stats.json")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
run()
|