本人看法，也就比excel高級一點，距離backtrader這些框架又差一點。做最基礎的測試可以，如果后期加入加倉功能，或者是止盈止損等功能，很不合適。只能做最簡單的技術指標測試。所以別太當回事。
導包，常用包導入：
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")獲取數據，本文使用akshare中債券數據為對象分析：
bond_zh_hs_daily_df = ak.bond_zh_hs_daily(symbol="sh010107")添加指標：
def backtest_trend_strategy(ohlc: pd.DataFrame,fast_period: int = 50,slow_period: int = 200,threshold: float = 1.0) -> pd.DataFrame:"""封裝向量化回測的邏輯"""# 計算指標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# 生成信號，1表示做多，-1表示做空，0表示空倉ohlc["signal"] = np.where(ohlc["pct_diff"] > threshold, 1, 0)ohlc["signal"] = np.where(ohlc["pct_diff"] < -threshold, -1, ohlc["signal"])# 計算策略收益率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運行策略，并繪制圖片：
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")
?參數優化：
# 選擇核心參數和掃描區間，其它參數保持不變
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])# 散點圖：策略收益率 vs 快速均線回溯期
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(%)")
)
