双策略轮动源码|趋势跟踪+动量强弱组合(智能行情分型、实盘可用)

import pandas as pdimport numpy as np# ===================== 统一全局参数（系列通用、无过度拟合） =====================# 趋势参数ema_short = 20ema_long = 60# 动量参数momentum_window = 20min_momentum_ret = 0.01# 波动率ATR参数atr_window = 14# 行情分型阈值trend_strength_threshold = 0.6vol_spike_multiple = 1.5# 读取标准K线数据df = pd.read_csv(”kline_data.csv”)# ===================== 1、通用指标计算（时序安全、滚动窗口） =====================# EMA趋势体系df[”ema_s”] = df[”close”].ewm(span=ema_short, adjust=False).mean()df[”ema_l”] = df[”close”].ewm(span=ema_long, adjust=False).mean()# ATR波动率df[”tr”] = np.maximum(    df[”high”] - df[”low”],    np.maximum(abs(df[”high”] - df[”close”].shift(1)), abs(df[”low”] - df[”close”].shift(1))))df[”atr”] = df[”tr”].rolling(window=atr_window).mean()# 趋势强度：均线发散程度df[”trend_diff”] = abs(df[”ema_s”] - df[”ema_l”])df[”trend_strength”] = df[”trend_diff”] / df[”close”]# 动量收益df[”momentum_ret”] = df[”close”].pct_change(periods=momentum_window)# ===================== 2、智能行情分型（核心轮动开关） =====================# 1.强趋势行情df[”strong_trend”] = (df[”trend_strength”].shift(1) > df[”trend_strength”].rolling(20).mean() * trend_strength_threshold)# 2.结构性分化行情（趋势不强+动量分化明显）df[”structure_market”] = (~df[”strong_trend”].shift(1)) & (abs(df[”momentum_ret”].shift(1)) > min_momentum_ret)# 3.风险行情（波动率飙升，禁止新开仓）df[”vol_spike”] = df[”atr”].shift(1) > df[”atr”].rolling(20).mean() * vol_spike_multiple# ===================== 3、趋势跟踪策略信号（主仓稳健） =====================df[”trend_long”] = (df[”ema_s”].shift(1) > df[”ema_l”].shift(1)) & (df[”close”].shift(1) > df[”ema_s”].shift(1))df[”trend_exit”] = df[”close”].shift(1) < df[”ema_l”].shift(1)# ===================== 4、时间动量策略信号（进攻增厚） =====================df[”momentum_long”] = df[”momentum_ret”].shift(1) > min_momentum_retdf[”momentum_exit”] = df[”momentum_ret”].shift(1) < 0# ===================== 5、双策略智能轮动规则 =====================# 强趋势：只用趋势信号df[”final_trend_signal”] = df[”strong_trend”] & df[”trend_long”] & ~df[”vol_spike”]# 结构行情：只用动量信号df[”final_momentum_signal”] = df[”structure_market”] & df[”momentum_long”] & ~df[”vol_spike”]# 合并最终开仓信号df[”final_entry”] = df[”final_trend_signal”] | df[”final_momentum_signal”]# 合并最终离场信号df[”final_exit”] = df[”trend_exit”] | df[”momentum_exit”]# ===================== 6、动态仓位适配（亏少赚多） =====================def get_rot_weight(row):# 高风险行情：极低仓位    if row[”vol_spike”]:        return 0.1# 强趋势行情：标准重仓吃波段    elif row[”strong_trend”]:        return 0.8# 结构动量行情：中等仓位套利    elif row[”structure_market”]:        return 0.5    else:        return 0.2df[”pos_weight”] = df.apply(get_rot_weight, axis=1)# 实盘执行价格df[”trade_open”] = df[”open”]# 输出最终交易信号res = df[[”time”,”open”,”ema_l”,”atr”,”trend_strength”,”strong_trend”,”structure_market”,”final_entry”,”final_exit”,”pos_weight”]]print(res.tail(120))