hmo
b676d85c41
- 独立均值回复引擎: MA60下>10%+RSI≤42+60日跌>15%+小盘≤1500万+RSI止跌≥2 - 出场: 止盈+18%/止损-8%/25天, 移除MA20反弹截断(v1毒药) - backtest_framework: prepare_bars 补 amount 字段(千元) - 大盘上下文: sh000001 2016-2020 数据从 sector_index_daily 回填 stock_daily - 验证: sideways(ADX)过滤有害(206.6%→147.9%), mkt_mode=any 最优 - docs/v_mr_strategy.md 完整策略文档
515 lines
19 KiB
Python
515 lines
19 KiB
Python
#!/usr/bin/env python3
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"""MoFin 回测框架 v2 — 从 stock_daily 原始行情计算全部指标，模拟全流程"""
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import sqlite3, json, math
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from datetime import datetime, timedelta
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from collections import defaultdict
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DB_PATH = "/home/hmo/MoFin/data/mofin.db"
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# ── 技术指标计算（纯 Python，不依赖第三方库）──
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def calc_ma(series, n):
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"""简单移动平均"""
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result = []
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for i in range(len(series)):
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if i < n - 1:
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result.append(None)
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else:
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result.append(sum(series[i-n+1:i+1]) / n)
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return result
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def calc_ema(series, n):
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"""指数移动平均"""
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result = []
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k = 2 / (n + 1)
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for i in range(len(series)):
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if i == 0:
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result.append(series[i])
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else:
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result.append(series[i] * k + result[-1] * (1 - k))
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return result
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def calc_rsi(series, n=14):
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"""RSI"""
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deltas = [series[i] - series[i-1] for i in range(1, len(series))]
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gains = [d if d > 0 else 0 for d in deltas]
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losses = [-d if d < 0 else 0 for d in deltas]
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result = [None] * (n + 1)
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avg_gain = sum(gains[:n]) / n
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avg_loss = sum(losses[:n]) / n
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if avg_loss == 0:
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result.append(100)
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else:
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rs = avg_gain / avg_loss
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result.append(100 - 100 / (1 + rs))
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for i in range(n, len(gains)):
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avg_gain = (avg_gain * (n-1) + gains[i]) / n
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avg_loss = (avg_loss * (n-1) + losses[i]) / n
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if avg_loss == 0:
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result.append(100)
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else:
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rs = avg_gain / avg_loss
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result.append(100 - 100 / (1 + rs))
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# Pad front to match original length
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while len(result) < len(series):
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result.insert(0, None)
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return result[:len(series)]
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def calc_macd(series, fast=12, slow=26, signal=9):
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"""MACD: 返回 macd_line, signal_line, histogram"""
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ema_fast = calc_ema(series, fast)
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ema_slow = calc_ema(series, slow)
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macd_line = [ema_fast[i] - ema_slow[i] if ema_fast[i] is not None and ema_slow[i] is not None else None for i in range(len(series))]
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# Recompute signal line from macd_line (only non-None values)
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macd_clean = [v for v in macd_line if v is not None]
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if not macd_clean:
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return [None]*len(series), [None]*len(series), [None]*len(series)
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signal_raw = calc_ema(macd_clean, signal)
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signal_line = [None] * (len(series) - len(macd_clean)) + signal_raw
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histogram = [macd_line[i] - signal_line[i] if macd_line[i] is not None and signal_line[i] is not None else None for i in range(len(series))]
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return macd_line, signal_line, histogram
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def calc_tr(highs, lows, closes):
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"""True Range"""
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tr = [highs[0] - lows[0]]
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for i in range(1, len(highs)):
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hl = highs[i] - lows[i]
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hc = abs(highs[i] - closes[i-1])
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lc = abs(lows[i] - closes[i-1])
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tr.append(max(hl, hc, lc))
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return tr
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def calc_atr(highs, lows, closes, n=14):
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"""ATR"""
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tr = calc_tr(highs, lows, closes)
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return calc_ema(tr, n)
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def calc_trend_strength(highs, lows, closes, n=14):
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"""简易趋势强度（替代 ADX）: (DI+ - DI-) / (DI+ + DI-) 归一化"""
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tr = calc_tr(highs, lows, closes)
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up = [highs[i] - highs[i-1] for i in range(1, len(highs))]
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down = [lows[i-1] - lows[i] for i in range(1, len(lows))]
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# Directional indicators (raw)
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di_plus_raw = [0.0] * len(up)
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di_minus_raw = [0.0] * len(up)
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for i in range(len(up)):
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if up[i] > down[i] and up[i] > 0:
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di_plus_raw[i] = up[i]
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if down[i] > up[i] and down[i] > 0:
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di_minus_raw[i] = down[i]
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# Normalize by TR
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tr_val = tr[i+1] if (i+1) < len(tr) else tr[-1]
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if tr_val > 0:
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di_plus_raw[i] = di_plus_raw[i] / tr_val * 100
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di_minus_raw[i] = di_minus_raw[i] / tr_val * 100
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# SMA of DMI
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result = []
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for i in range(len(di_plus_raw)):
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if i < n - 1:
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result.append(None)
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else:
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avg_plus = sum(di_plus_raw[i-n+1:i+1]) / n
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avg_minus = sum(di_minus_raw[i-n+1:i+1]) / n
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if avg_plus + avg_minus > 0:
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dx = abs(avg_plus - avg_minus) / (avg_plus + avg_minus) * 100
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else:
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dx = 0
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result.append(dx)
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# Pad front to match original highs length
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return [None] * (len(highs) - len(result)) + result
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def calc_obv(closes, volumes):
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"""OBV"""
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obv = [volumes[0]]
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for i in range(1, len(closes)):
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if closes[i] > closes[i-1]:
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obv.append(obv[-1] + volumes[i])
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elif closes[i] < closes[i-1]:
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obv.append(obv[-1] - volumes[i])
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else:
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obv.append(obv[-1])
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return obv
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def calc_roc(series, n=10):
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"""Rate of Change"""
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result = []
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for i in range(len(series)):
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if i < n:
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result.append(None)
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else:
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result.append((series[i] - series[i-n]) / series[i-n] * 100 if series[i-n] != 0 else 0)
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return result
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def prepare_bars(code, start_date, end_date):
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"""获取原始数据并计算全部指标"""
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conn = sqlite3.connect(DB_PATH)
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rows = conn.execute("""
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SELECT date, open, close, high, low, volume, amount
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FROM stock_daily WHERE code=? AND date>=? AND date<=?
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ORDER BY date
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""", (code, start_date, end_date)).fetchall()
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conn.close()
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if not rows or len(rows) < 30:
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return None
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dates = [r[0] for r in rows]
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opens = [r[1] for r in rows]
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closes = [r[2] for r in rows]
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highs = [r[3] for r in rows]
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lows = [r[4] for r in rows]
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volumes = [r[5] for r in rows]
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amounts = [r[6] if len(r) > 6 else None for r in rows]
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# 计算全部指标
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ma5 = calc_ma(closes, 5)
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ma10 = calc_ma(closes, 10)
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ma20 = calc_ma(closes, 20)
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ma60 = calc_ma(closes, 60)
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rsi = calc_rsi(closes)
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macd_line, signal_line, macd_hist = calc_macd(closes)
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atr = calc_atr(highs, lows, closes)
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trend_strength = calc_trend_strength(highs, lows, closes)
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obv = calc_obv(closes, volumes)
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roc = calc_roc(closes)
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bars = []
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for i in range(len(dates)):
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# ── 枢轴S/R（与实盘 technical_analysis.calc_support_resistance 同算法）──
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piv = {}
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if i >= 1:
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h, l, c = highs[i], lows[i], closes[i]
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yc = closes[i-1]
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# 多日波幅（近20日）
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win = bars[max(0, i-19):i] if i > 0 else []
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multi_high = max([h] + [x.get('high', 0) or 0 for x in win])
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multi_low = min([l] + [x.get('low', 1e9) or 1e9 for x in win])
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eff_range = max(h - l, multi_high - multi_low, c * 0.05)
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tp = (c - multi_low) / (multi_high - multi_low) if multi_high > multi_low else 0.5
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if tp > 0.8 or tp < 0.2:
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eff_range = max(eff_range, c * 0.08)
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pp = (h + l + c) / 3
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s1 = 2 * pp - h
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s2 = pp - eff_range
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r1 = 2 * pp - l
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r2 = pp + eff_range
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if yc < s1: s1 = yc
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if yc > r1: r1 = yc
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piv = {'pivot': pp, 'weak_support': s1, 'strong_support': s2,
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'weak_resist': r1, 'strong_resist': r2}
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bars.append({
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'date': dates[i],
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'open': opens[i],
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'close': closes[i],
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'high': highs[i],
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'low': lows[i],
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'volume': volumes[i],
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'amount': amounts[i] if i < len(amounts) else None,
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'ma5': ma5[i] if i < len(ma5) else None,
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'ma10': ma10[i] if i < len(ma10) else None,
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'ma20': ma20[i] if i < len(ma20) else None,
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'ma60': ma60[i] if i < len(ma60) else None,
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'rsi': rsi[i] if i < len(rsi) else None,
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'macd': macd_line[i] if i < len(macd_line) else None,
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'macd_signal': signal_line[i] if i < len(signal_line) else None,
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'macd_hist': macd_hist[i] if i < len(macd_hist) else None,
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'atr': atr[i] if i < len(atr) else None,
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'adx': trend_strength[i] if i < len(trend_strength) else None,
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'obv': obv[i] if i < len(obv) else None,
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'roc': roc[i] if i < len(roc) else None,
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**piv,
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})
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return bars
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def compute_single_score(bars):
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"""对单个股票计算多因子评分（与 MoFin 现有一致）"""
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if not bars or len(bars) < 15:
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return None
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last = bars[-1]
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prev5 = bars[-5] if len(bars) >= 5 else bars[-2] if len(bars) >= 2 else last
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prev10 = bars[-10] if len(bars) >= 10 else bars[0]
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def v(val): return val if val is not None else 0
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score = {'trend': 0, 'momentum': 0, 'volume': 0, 'volatility': 0, 'risk': 0}
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# 趋势分 (0-25)
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ts = 0
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adx = v(last.get('adx'))
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if adx > 25: ts += 10
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elif adx > 20: ts += 5
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mh = v(last.get('macd_hist'))
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pmh = v(prev5.get('macd_hist'))
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if mh > 0 and mh > pmh: ts += 10
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elif mh > 0: ts += 5
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elif mh < 0 and mh < pmh: ts -= 5
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ma5 = v(last.get('ma5'))
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ma10 = v(last.get('ma10'))
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ma20 = v(last.get('ma20'))
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if ma5 > ma10 > ma20 and ma5 > 0 and ma10 > 0: ts += 5
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elif ma5 < ma10 < ma20 and ma5 > 0 and ma10 > 0: ts -= 5
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score['trend'] = max(0, min(25, ts))
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# 动量分 (0-20)
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ms = 0
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roc = v(last.get('roc'))
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if roc > 5: ms += 5
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elif roc > 2: ms += 2
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elif roc < -5: ms -= 5
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elif roc < -2: ms -= 2
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rsi = v(last.get('rsi'))
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if 30 <= rsi <= 70: ms += 5
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if 50 < rsi <= 65: ms += 3
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elif rsi > 70: ms -= 3
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elif rsi < 30: ms -= 3
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close = v(last.get('close'))
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if ma20 > 0:
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pct = (close - ma20) / ma20 * 100
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if -3 <= pct <= 5: ms += 7
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elif pct < -10: ms -= 3
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score['momentum'] = max(0, min(20, ms))
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# 量能分 (0-20)
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vs = 0
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obv_cur = v(last.get('obv'))
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obv_prev = v(prev5.get('obv'))
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vol_cur = v(last.get('volume'))
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vol_prev = v(prev5.get('volume'))
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if obv_cur > obv_prev and vol_cur > 0 and vol_prev > 0:
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vr = vol_cur / vol_prev
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if vr > 1.5: vs += 10
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elif vr > 1.2: vs += 5
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if obv_cur > obv_prev: vs += 5
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if vol_prev > 0 and vol_cur / vol_prev < 0.5: vs -= 5
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score['volume'] = max(0, min(20, vs))
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# 波动率分 (0-15)
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vls = 5
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atr = v(last.get('atr'))
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if close > 0 and atr > 0:
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atr_pct = atr / close * 100
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if 1.5 <= atr_pct <= 3.5: vls += 5
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elif atr_pct < 1: vls += 3
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elif atr_pct > 5: vls -= 5
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if len(bars) >= 20:
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cls = [v(b.get('close')) for b in bars[-20:]]
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if min(cls) > 0:
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vol_range = (max(cls) - min(cls)) / min(cls) * 100
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if vol_range < 10: vls += 5
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elif vol_range > 30: vls -= 5
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score['volatility'] = max(0, min(15, vls))
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# 风险分 (0-20)
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rs = 10
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if len(bars) >= 10:
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low5 = min(bars[-5:], key=lambda x: v(x.get('low')))
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low10 = min(bars[-10:-5], key=lambda x: v(x.get('low'))) if len(bars) >= 10 else low5
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if v(low5.get('close')) < v(low10.get('close')) and v(low5.get('macd_hist')) > v(low10.get('macd_hist')):
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rs += 5
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if v(low5.get('close')) > v(low10.get('close')) and v(low5.get('macd_hist')) < v(low10.get('macd_hist')):
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rs -= 5
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if ma20 > 0:
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dist = (close - ma20) / ma20 * 100
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if dist < 2: rs += 5
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elif dist > 15: rs -= 5
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score['risk'] = max(0, min(20, rs))
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total = sum(score.values())
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return total, score
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def compute_kelly(total_score, target_pct, stop_pct):
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"""半 Kelly 仓位"""
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p = min(0.6, total_score / 100)
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if stop_pct <= 0:
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return 0
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b = target_pct / stop_pct
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if b <= 1:
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return 0
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kelly = (p * b - (1 - p)) / b
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return max(0, min(0.5, kelly)) * 0.5 # 半 Kelly
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def run_strategy_research(start_date, end_date, capital=1000000):
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"""主回测入口"""
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conn = sqlite3.connect(DB_PATH)
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# 1. 获取所有有历史数据的股票
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stocks = conn.execute("""
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SELECT DISTINCT sd.code, COALESCE(s.name, sd.code) as name
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FROM stock_daily sd
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LEFT JOIN stocks s ON sd.code = s.code
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WHERE sd.date>=? AND sd.date<=?
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""", (start_date, end_date)).fetchall()
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results = {
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'period': f"{start_date} ~ {end_date}",
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'capital': capital,
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'total_stocks_screened': len(stocks),
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'scored_stocks': 0,
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'buy_signals': 0,
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'trades': [],
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'summary': {}
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}
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trades = []
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code_names = {}
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eval_dates = [] # collect evaluation dates
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for code, name in stocks:
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code_names[code] = name
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bars = prepare_bars(code, start_date, end_date)
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if not bars or len(bars) < 20:
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continue
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# 2. 滚动评估：每周评分一次
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window_start = 20 # need at least 20 bars for indicators
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while window_start < len(bars):
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window = bars[:window_start]
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score_result = compute_single_score(window)
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if score_result is None:
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window_start += 5
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continue
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total_score, score_components = score_result
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results['scored_stocks'] += 1
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last = window[-1]
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close = v_close = last.get('close', 0) or 0
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ma20 = last.get('ma20', 0) or 0
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# 3. 买入条件：总评分 >= 45 且 momentum >= 8
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buy_signal = total_score >= 45 and score_components['momentum'] >= 8
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if buy_signal:
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results['buy_signals'] += 1
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entry_price = close
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atr_val = last.get('atr', 0) or 0
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if atr_val > 0:
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target_price = entry_price * 1.10
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stop_price = entry_price - atr_val * 2.0
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else:
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target_price = entry_price * 1.10
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stop_price = entry_price * 0.93
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kelly = compute_kelly(total_score, 0.10, (entry_price - stop_price) / entry_price)
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# 4. 模拟未来走势
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future = bars[window_start:min(window_start+20, len(bars))]
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exit_price = None
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exit_reason = None
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max_high = entry_price
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min_low = entry_price
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for fb in future:
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fb_high = fb.get('high', 0) or 0
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fb_low = fb.get('low', 0) or 0
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fb_close = fb.get('close', 0) or 0
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max_high = max(max_high, fb_high)
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min_low = min(min_low, fb_low)
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if fb_high >= target_price:
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exit_price = target_price
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exit_reason = 'target'
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break
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elif fb_low <= stop_price:
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exit_price = fb_close
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exit_reason = 'stop'
|
||
break
|
||
|
||
if exit_price is None:
|
||
if future:
|
||
exit_price = future[-1].get('close', entry_price)
|
||
else:
|
||
exit_price = entry_price
|
||
exit_reason = 'keep'
|
||
|
||
hold_days = 0
|
||
if exit_reason and exit_reason != 'keep' and future:
|
||
for idx, fb in enumerate(future):
|
||
fb_high = fb.get('high', 0) or 0
|
||
fb_low = fb.get('low', 0) or 0
|
||
if (exit_reason == 'target' and fb_high >= target_price) or \
|
||
(exit_reason == 'stop' and fb_low <= stop_price):
|
||
hold_days = idx + 1
|
||
break
|
||
elif exit_reason == 'keep':
|
||
hold_days = len(future)
|
||
|
||
profit_pct = (exit_price - entry_price) / entry_price * 100 if entry_price > 0 else 0
|
||
|
||
trades.append({
|
||
'code': code,
|
||
'name': name,
|
||
'entry_date': last.get('date'),
|
||
'entry_price': round(entry_price, 2),
|
||
'exit_price': round(exit_price, 2),
|
||
'profit_pct': round(profit_pct, 2),
|
||
'exit_reason': exit_reason,
|
||
'hold_days': hold_days,
|
||
'score': total_score,
|
||
'kelly': round(kelly, 3),
|
||
'stop_loss': round(stop_price, 2),
|
||
'target': round(target_price, 2),
|
||
'max_high': round(max_high, 2),
|
||
'min_low': round(min_low, 2)
|
||
})
|
||
|
||
window_start += 5 # 每 5 个交易日评估一次
|
||
|
||
conn.close()
|
||
|
||
# 5. 汇总
|
||
if trades:
|
||
profits = [t['profit_pct'] for t in trades]
|
||
wins = [t for t in trades if t['profit_pct'] > 0]
|
||
losses = [t for t in trades if t['profit_pct'] <= 0]
|
||
|
||
win_rate = len(wins) / len(trades) * 100 if trades else 0
|
||
avg_profit = sum(profits) / len(profits) if profits else 0
|
||
avg_win = sum(t['profit_pct'] for t in wins) / len(wins) if wins else 0
|
||
avg_loss = sum(t['profit_pct'] for t in losses) / len(losses) if losses else 0
|
||
|
||
mean_ret = avg_profit / 100
|
||
if len(profits) > 1:
|
||
variance = sum((p/100 - mean_ret)**2 for p in profits) / (len(profits) - 1)
|
||
std_ret = math.sqrt(variance)
|
||
else:
|
||
std_ret = 0
|
||
sharpe = mean_ret / std_ret * math.sqrt(252) if std_ret > 0 else 0
|
||
|
||
# 最大回撤：用实际资本曲线计算
|
||
capital_curve = [capital]
|
||
for t in trades:
|
||
new_cap = capital_curve[-1] * (1 + t['profit_pct'] / 100)
|
||
capital_curve.append(new_cap)
|
||
peak_cap = capital
|
||
max_dd = 0
|
||
for c in capital_curve:
|
||
peak_cap = max(peak_cap, c)
|
||
dd = (peak_cap - c) / peak_cap * 100
|
||
max_dd = max(max_dd, dd)
|
||
|
||
results['trades'] = sorted(trades, key=lambda x: abs(x['profit_pct']), reverse=True)[:50]
|
||
results['summary'] = {
|
||
'total_trades': len(trades),
|
||
'win_rate': round(win_rate, 1),
|
||
'avg_profit_pct': round(avg_profit, 2),
|
||
'avg_win_pct': round(avg_win, 2),
|
||
'avg_loss_pct': round(avg_loss, 2),
|
||
'sharpe_ratio': round(sharpe, 2),
|
||
'max_drawdown_pct': round(max_dd, 2),
|
||
'profit_factor': round(abs(avg_win / avg_loss), 2) if avg_loss != 0 else float('inf'),
|
||
'wins': len(wins),
|
||
'losses': len(losses),
|
||
'capital_end': round(capital * (1 + avg_profit/100), 2)
|
||
}
|
||
|
||
return results
|
||
|
||
|
||
if __name__ == '__main__':
|
||
end = datetime.now().strftime('%Y-%m-%d')
|
||
start = (datetime.now() - timedelta(days=180)).strftime('%Y-%m-%d')
|
||
r = run_strategy_research(start, end)
|
||
print(json.dumps(r, indent=2, ensure_ascii=False))
|