425 lines
16 KiB
Python
425 lines
16 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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量化策略引擎：主流策略信号生成 + 回测框架
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- 6 个主流策略：双均线 / MACD / RSI / 布林带 / 动量 / N日突破
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- 回测规则：收盘产生信号，次日开盘成交（全仓多头，避免未来函数）
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- 指标：总收益 / 年化 / 最大回撤 / 夏普 / 胜率 / 盈亏比 / 交易次数
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- 结果持久化到 strategy_backtests 表，支持全市场批量回测
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扩展新策略：在 STRATEGIES 注册 name/desc + 实现信号生成函数即可
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"""
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import json
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import math
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import statistics
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from database import executemany, query_one
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# ===================================================================== 指标序列
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def ma_series(closes, n):
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out = []
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s = 0.0
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for i, c in enumerate(closes):
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s += c
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if i >= n:
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s -= closes[i - n]
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out.append(s / n if i >= n - 1 else None)
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return out
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def ema_series(vals, n):
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out = []
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k = 2 / (n + 1)
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e = vals[0] if vals else 0
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for i, v in enumerate(vals):
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e = v if i == 0 else v * k + e * (1 - k)
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out.append(e)
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return out
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def macd_series(closes):
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ema12 = ema_series(closes, 12)
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ema26 = ema_series(closes, 26)
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dif = [a - b for a, b in zip(ema12, ema26)]
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dea = ema_series(dif, 9)
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return dif, dea
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def rsi_series(closes, n=14):
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out = [None] * len(closes)
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if len(closes) <= n:
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return out
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gains, losses = [], []
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for i in range(1, len(closes)):
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chg = closes[i] - closes[i - 1]
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gains.append(max(chg, 0))
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losses.append(max(-chg, 0))
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avg_g = sum(gains[:n]) / n
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avg_l = sum(losses[:n]) / n
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for i in range(n, len(gains)):
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avg_g = (avg_g * (n - 1) + gains[i]) / n
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avg_l = (avg_l * (n - 1) + losses[i]) / n
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rs = 100 if avg_l == 0 else avg_g / avg_l
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out[i + 1] = 100 - 100 / (1 + rs)
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out[n] = 100 if avg_l == 0 else 100 - 100 / (1 + avg_g / max(avg_l, 1e-9))
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return out
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def boll_series(closes, n=20, k=2.0):
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mid, upper, lower = [], [], []
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for i in range(len(closes)):
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if i >= n - 1:
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seg = closes[i - n + 1:i + 1]
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m = sum(seg) / n
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sd = statistics.pstdev(seg)
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mid.append(m); upper.append(m + k * sd); lower.append(m - k * sd)
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else:
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mid.append(None); upper.append(None); lower.append(None)
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return mid, upper, lower
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# ===================================================================== 策略注册
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STRATEGIES = {
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"ma_cross": {
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"name": "双均线金叉",
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"icon": "📐",
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"params": "MA5 / MA20",
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"desc": "短均线MA5上穿长均线MA20买入（金叉），下穿卖出（死叉）。经典趋势跟踪策略。",
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"tags": ["趋势"],
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},
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"macd": {
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"name": "MACD 金叉",
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"icon": "🟢",
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"params": "12/26/9",
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"desc": "DIF 上穿 DEA 买入，下穿卖出。捕捉中线趋势拐点，过滤震荡噪音。",
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"tags": ["趋势", "动量"],
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},
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"rsi_rev": {
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"name": "RSI 超买超卖",
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"icon": "🔄",
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"params": "RSI(14) 30/70",
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"desc": "RSI 低于 30 超卖买入、高于 70 超买卖出。均值回归型反转策略。",
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"tags": ["反转"],
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},
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"boll": {
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"name": "布林带回归",
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"icon": "📦",
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"params": "20日 / 2σ",
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"desc": "价格跌破下轨买入、突破上轨卖出，赌价格向中轨回归。震荡市表现佳。",
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"tags": ["回归"],
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},
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"momentum": {
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"name": "20日动量",
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"icon": "🚀",
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"params": "20日涨幅 / MA20",
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"desc": "20日涨幅超阈值且站上MA20买入，跌破MA20卖出。顺势强者恒强。",
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"tags": ["动量"],
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},
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"breakout": {
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"name": "N日新高突破",
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"icon": "🧗",
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"params": "20日高低点",
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"desc": "收盘突破20日新高买入，跌破20日新低卖出。海龟式突破策略。",
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"tags": ["突破"],
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},
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}
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def _signals(bars, key):
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"""生成信号列表 [{date, action:'buy'/'sell', close, reason}]"""
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closes = [b["close"] for b in bars]
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n = len(bars)
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sigs = []
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if key == "ma_cross":
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ma5, ma20 = ma_series(closes, 5), ma_series(closes, 20)
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prev_state = None
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for i in range(n):
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if ma5[i] is None or ma20[i] is None:
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continue
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state = ma5[i] > ma20[i]
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if prev_state is not None and state != prev_state:
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if state:
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sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
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"reason": f"MA5({ma5[i]:.2f})上穿MA20({ma20[i]:.2f})金叉"})
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else:
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sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
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"reason": f"MA5({ma5[i]:.2f})下穿MA20({ma20[i]:.2f})死叉"})
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prev_state = state
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elif key == "macd":
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dif, dea = macd_series(closes)
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prev = None
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for i in range(n):
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if dif[i] is None or dea[i] is None:
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continue
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state = dif[i] > dea[i]
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if prev is not None and state != prev:
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if state:
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sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
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"reason": f"MACD金叉 DIF({dif[i]:.3f})上穿DEA({dea[i]:.3f})"})
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else:
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sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
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"reason": f"MACD死叉 DIF({dif[i]:.3f})下穿DEA({dea[i]:.3f})"})
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prev = state
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elif key == "rsi_rev":
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rsi = rsi_series(closes)
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for i in range(1, n):
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if rsi[i] is None or rsi[i - 1] is None:
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continue
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if rsi[i - 1] >= 30 and rsi[i] < 30:
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sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
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"reason": f"RSI({rsi[i]:.1f})下穿30超卖"})
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elif rsi[i - 1] <= 70 and rsi[i] > 70:
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sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
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"reason": f"RSI({rsi[i]:.1f})上穿70超买"})
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elif key == "boll":
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mid, upper, lower = boll_series(closes)
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prev_state = None
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for i in range(n):
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if lower[i] is None:
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continue
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if closes[i] < lower[i]:
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state = "buy"
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elif closes[i] > upper[i]:
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state = "sell"
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else:
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state = prev_state
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if state != prev_state and state is not None:
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if state == "buy":
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sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
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"reason": f"收盘跌破下轨({lower[i]:.2f})"})
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else:
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sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
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"reason": f"收盘突破上轨({upper[i]:.2f})"})
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prev_state = state
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elif key == "momentum":
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ma20 = ma_series(closes, 20)
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prev_state = None
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for i in range(n):
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if i < 20 or ma20[i] is None:
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continue
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mom = closes[i] / closes[i - 20] - 1
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state = "buy" if (mom > 0.03 and closes[i] > ma20[i]) else ("sell" if closes[i] < ma20[i] else prev_state)
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if state != prev_state and state is not None:
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if state == "buy":
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sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
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"reason": f"20日动量{mom*100:+.1f}%且站上MA20"})
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else:
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sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
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"reason": "跌破MA20止盈/止损"})
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prev_state = state
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elif key == "breakout":
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N = 20
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for i in range(N, n):
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window = closes[i - N:i]
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if closes[i] > max(window) and closes[i] > closes[i - 1]:
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sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
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"reason": f"突破{N}日新高({max(window):.2f})"})
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elif closes[i] < min(window):
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sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
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"reason": f"跌破{N}日新低({min(window):.2f})"})
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return sigs
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# ===================================================================== 回测
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def backtest(bars, key):
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"""全仓多头回测。收盘信号 → 次日开盘成交。返回 metrics/equity/trades"""
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signals = _signals(bars, key)
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n = len(bars)
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opens = [b["open"] for b in bars]
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closes = [b["close"] for b in bars]
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dates = [b["date"] for b in bars]
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# 信号按日期归类（同一天可能多个 buy/sell，取最后一个有效方向）
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by_day = {}
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for s in signals:
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by_day[s["date"]] = s
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position = 0.0 # 持股数量（按买入价格折算）
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cash = 1.0 # 初始资金=1
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trades = []
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entry = None
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equity = []
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trade_idx = 0
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for i in range(n):
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d = dates[i]
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# 当日开盘执行前一日收盘信号
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sig = by_day.get(d)
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exec_price = opens[i]
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if sig and sig["action"] == "buy" and position == 0:
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position = cash / exec_price
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cash = 0.0
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entry = {"date": d, "price": exec_price, "reason": sig["reason"]}
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elif sig and sig["action"] == "sell" and position > 0:
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cash = position * exec_price
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position = 0.0
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if entry:
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ret = (exec_price / entry["price"] - 1)
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trades.append({"entry_date": entry["date"], "entry_price": round(entry["price"], 3),
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"exit_date": d, "exit_price": round(exec_price, 3),
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"return": round(ret * 100, 2),
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"days": _day_diff(dates, entry["date"], d),
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"reason": entry["reason"]})
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entry = None
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value = cash + position * closes[i]
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equity.append({"date": d, "value": round(value, 4)})
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# 期末仍持仓则平仓（按最后收盘）
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if position > 0 and entry:
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last = closes[-1]
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cash = position * last
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ret = last / entry["price"] - 1
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trades.append({"entry_date": entry["date"], "entry_price": round(entry["price"], 3),
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"exit_date": dates[-1], "exit_price": round(last, 3),
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"return": round(ret * 100, 2), "days": _day_diff(dates, entry["date"], dates[-1]),
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"reason": entry["reason"] + "（期末平仓）"})
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position = 0.0
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equity[-1]["value"] = round(cash, 4)
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metrics = _calc_metrics(equity, trades, dates, bars)
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# 买入持有基准
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bh = bars[0]["close"]
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for e in equity:
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e["bh"] = round(bars[n - 1]["close"] / bh, 4) if bh else 1.0
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return {"metrics": metrics, "equity": equity, "trades": trades}
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def _day_diff(dates, start, end):
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try:
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from datetime import date
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ds = date.fromisoformat(start)
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de = date.fromisoformat(end)
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return (de - ds).days
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except Exception:
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return 0
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def _calc_metrics(equity, trades, dates, bars):
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n = len(equity)
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final = equity[-1]["value"] if equity else 1.0
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total = final - 1
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years = n / 252.0
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ann = (final ** (1 / years) - 1) if (years > 0 and final > 0) else 0
|
|||
|
|
# 最大回撤
|
|||
|
|
peak, mdd = equity[0]["value"], 0.0
|
|||
|
|
for e in equity:
|
|||
|
|
peak = max(peak, e["value"])
|
|||
|
|
mdd = min(mdd, (e["value"] - peak) / peak)
|
|||
|
|
# 日收益 → 夏普
|
|||
|
|
rets = []
|
|||
|
|
for i in range(1, n):
|
|||
|
|
prev = equity[i - 1]["value"]
|
|||
|
|
if prev > 0:
|
|||
|
|
rets.append(equity[i]["value"] / prev - 1)
|
|||
|
|
sharpe = 0.0
|
|||
|
|
if rets and statistics.stdev(rets) > 0:
|
|||
|
|
sharpe = statistics.mean(rets) / statistics.stdev(rets) * math.sqrt(252)
|
|||
|
|
# 交易统计
|
|||
|
|
n_tr = len(trades)
|
|||
|
|
wins = [t for t in trades if t["return"] > 0]
|
|||
|
|
losses = [t for t in trades if t["return"] <= 0]
|
|||
|
|
win_rate = len(wins) / n_tr if n_tr else 0.0
|
|||
|
|
gp = sum(t["return"] for t in wins)
|
|||
|
|
gl = abs(sum(t["return"] for t in losses))
|
|||
|
|
pf = (gp / gl) if gl > 0 else (gp if gp > 0 else 0)
|
|||
|
|
avg_hold = sum(t["days"] for t in trades) / n_tr if n_tr else 0
|
|||
|
|
# 基准（买入持有）
|
|||
|
|
bh_ret = bars[-1]["close"] / bars[0]["close"] - 1
|
|||
|
|
return {
|
|||
|
|
"total_return": round(total * 100, 2),
|
|||
|
|
"annualized": round(ann * 100, 2),
|
|||
|
|
"max_drawdown": round(mdd * 100, 2),
|
|||
|
|
"sharpe": round(sharpe, 2),
|
|||
|
|
"win_rate": round(win_rate * 100, 1),
|
|||
|
|
"profit_factor": round(pf, 2),
|
|||
|
|
"trades": n_tr,
|
|||
|
|
"avg_hold_days": round(avg_hold, 1),
|
|||
|
|
"benchmark": round(bh_ret * 100, 2),
|
|||
|
|
"excess": round((total - bh_ret) * 100, 2),
|
|||
|
|
"days": n,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ===================================================================== 批量回测
|
|||
|
|
def run_one(code, name, bars, key):
|
|||
|
|
"""单只股票单策略回测，返回入库行"""
|
|||
|
|
res = backtest(bars, key)
|
|||
|
|
return {
|
|||
|
|
"strategy": key, "code": code, "stock_name": name,
|
|||
|
|
"metrics": json.dumps(res["metrics"], ensure_ascii=False),
|
|||
|
|
"equity": json.dumps(res["equity"], ensure_ascii=False),
|
|||
|
|
"trades": json.dumps(res["trades"], ensure_ascii=False),
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
def build_all(progress=None):
|
|||
|
|
"""全市场 × 全策略批量回测（覆盖写入 strategy_backtests）"""
|
|||
|
|
from database import query, executemany
|
|||
|
|
stocks = query("SELECT code, name FROM stocks")
|
|||
|
|
rows = []
|
|||
|
|
for si, s in enumerate(stocks):
|
|||
|
|
bars = query("SELECT date, open, high, low, close, volume FROM stock_daily "
|
|||
|
|
"WHERE code=? ORDER BY date ASC", (s["code"],))
|
|||
|
|
if len(bars) < 30:
|
|||
|
|
continue
|
|||
|
|
for key in STRATEGIES:
|
|||
|
|
rows.append(run_one(s["code"], s["name"], bars, key))
|
|||
|
|
if progress:
|
|||
|
|
progress(si + 1, len(stocks))
|
|||
|
|
executemany(
|
|||
|
|
"INSERT OR REPLACE INTO strategy_backtests(strategy, code, stock_name, metrics, equity, trades, run_at) "
|
|||
|
|
"VALUES(?,?,?,?,?,?,datetime('now','localtime'))",
|
|||
|
|
[(r["strategy"], r["code"], r["stock_name"], r["metrics"], r["equity"], r["trades"]) for r in rows])
|
|||
|
|
return len(rows)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def strategy_summary(key):
|
|||
|
|
"""某策略的全市场统计（用于列表页头部）"""
|
|||
|
|
from database import query
|
|||
|
|
rows = query("SELECT metrics FROM strategy_backtests WHERE strategy=?", (key,))
|
|||
|
|
if not rows:
|
|||
|
|
return {}
|
|||
|
|
best = None
|
|||
|
|
sums = {"total": 0, "sharpe": 0, "win": 0, "n": 0}
|
|||
|
|
for r in rows:
|
|||
|
|
m = json.loads(r["metrics"])
|
|||
|
|
sums["total"] += m["total_return"]
|
|||
|
|
sums["sharpe"] += m["sharpe"]
|
|||
|
|
sums["win"] += m["win_rate"]
|
|||
|
|
sums["n"] += 1
|
|||
|
|
if best is None or m["total_return"] > best["metrics"]["total_return"]:
|
|||
|
|
best = {"code": None, "metrics": m}
|
|||
|
|
# 顺便找最优个股（第二遍，轻量）
|
|||
|
|
bcode, bname, bret = None, None, -1e9
|
|||
|
|
rows2 = query("SELECT code, stock_name, metrics FROM strategy_backtests WHERE strategy=?", (key,))
|
|||
|
|
for r in rows2:
|
|||
|
|
m = json.loads(r["metrics"])
|
|||
|
|
if m["total_return"] > bret:
|
|||
|
|
bret, bcode, bname = m["total_return"], r["code"], r["stock_name"]
|
|||
|
|
nn = sums["n"]
|
|||
|
|
return {
|
|||
|
|
"avg_return": round(sums["total"] / nn, 2) if nn else 0,
|
|||
|
|
"avg_sharpe": round(sums["sharpe"] / nn, 2) if nn else 0,
|
|||
|
|
"avg_win_rate": round(sums["win"] / nn, 1) if nn else 0,
|
|||
|
|
"n": nn,
|
|||
|
|
"best_code": bcode, "best_name": bname, "best_return": round(bret, 2) if bcode else None,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
def market_rank(key, limit=100):
|
|||
|
|
"""某策略全市场收益榜"""
|
|||
|
|
from database import query
|
|||
|
|
rows = query("SELECT code, stock_name, metrics FROM strategy_backtests WHERE strategy=? ORDER BY run_at DESC", (key,))
|
|||
|
|
items = []
|
|||
|
|
for r in rows:
|
|||
|
|
m = json.loads(r["metrics"])
|
|||
|
|
items.append({"code": r["code"], "name": r["stock_name"], **m})
|
|||
|
|
items.sort(key=lambda x: x["total_return"], reverse=True)
|
|||
|
|
return items[:limit]
|