python使用pandas进行量化回测
下面文章描述可能比excel
高级一点,距离backtrader
这些框架又差一点。做最基础的测试可以,如果后期加入加仓功能,或者是止盈止损等功能,很不合适。只能做最简单的技术指标测试。
导包,常用包导入:
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")
获取数据,本文使用akshare中债券数据为对象分析:
bond_zh_hs_daily_df = ak.bond_zh_hs_daily(symbol="sh010107")
添加指标:
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
运行策略,并绘制图片:
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")
参数优化:
# 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(%)")
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