python使用pandas进行量化回测

import os
import akshare as ak
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import talib as ta
%matplotlib inline
plt.style.use("ggplot")

def backtest_trend_strategy(ohlc: pd.DataFrame,
                            fast_period: int = 50,
                            slow_period: int = 200,
                            threshold: float = 1.0) -> pd.DataFrame:
    """封装向量化回测的逻辑"""
    # 计算指标
    ohlc["fast_ema"] = talib.EMA(ohlc.close, fast_period)
    ohlc["slow_ema"] = talib.EMA(ohlc.close, slow_period)
    ohlc["pct_diff"] = (ohlc["fast_ema"] / ohlc["slow_ema"] - 1) * 100
 
    # 生成信号，1表示做多，-1表示做空，0表示空仓
    ohlc["signal"] = np.where(ohlc["pct_diff"] > threshold, 1, 0)
    ohlc["signal"] = np.where(ohlc["pct_diff"] < -threshold, -1, ohlc["signal"])
 
    # 计算策略收益率
    ohlc["returns"] = np.log(ohlc["close"] / ohlc["close"].shift(1))
    ohlc["strategy"] = ohlc["signal"].shift(1) * ohlc["returns"]
    ohlc["strategy_returns"] = ohlc["strategy"].cumsum()
    
    return ohlc

data = strategy1(data)
 
 
fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(12, 15), sharex=True)
 
ax[0].plot(data.index, data["close"])
ax[0].plot(data.index, data["fast_ema"])
ax[0].plot(data.index, data["slow_ema"])
ax[0].set_title("Price and Indicators")
 
ax[1].plot(data.index, data["signal"])
ax[1].set_title("Strategy Position")
 
data[["returns", "strategy"]].cumsum().plot(ax=ax[2], title="Strategy Return")

# 选择核心参数和扫描区间，其它参数保持不变
fast_period_rng = np.arange(5, 101, 5)
 
total_return = []
for fast_period in fast_period_rng:
    ohlc = data.filter(["open", "high", "low", "close"])
    res = backtest_trend_strategy(ohlc, fast_period, 200, 1.0)
    total_return.append(res["strategy_returns"].iloc[-1])
    
 
# 散点图：策略收益率 vs 快速均线回溯期
fig, ax = plt.subplots(figsize=(12, 7))
ax.plot(fast_period_rng, total_return, "r-o", markersize=10)
ax.set_title("Strategy Return vs Fast period")
ax.set_xlabel("fast_period")
ax.set_ylabel("return(%)")