本人看法，也就比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。所以别太当回事。

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(%)")