这个模型使用的数据是从CSV文件中读取的股票价格数据。然后，计算短期和长期移动平均线，并基于这些移动平均线产生买卖信号。接下来，计算持仓和账户余额，并绘制表展示。
import pandas as pd
import numpy as np
# 读取历史股票数据
stock_data = pd.read_csv("stock_data.csv")
stock_data["Date"] = pd.to_datetime(stock_data["Date"])
stock_data = stock_data.set_index("Date")
# 计算移动平均线
short_ma = stock_data["Close"].rolling(window=20).mean()
long_ma = stock_data["Close"].rolling(window=50).mean()
# 创建一个新的dataframe来存储信号和持仓
signals = pd.DataFrame(index=stock_data.index)
signals["signal"] = 0.0
signals["short_ma"] = short_ma
signals["long_ma"] = long_ma
# 生成买入信号
signals["signal"][20:] = np.where(signals["short_ma"][20:] > signals["long_ma"][20:], 1.0, 0.0)
# 生成卖出信号
signals["signal"][20:] = np.where(signals["short_ma"][20:] < signals["long_ma"][20:], -1.0, signals["signal"][20:])
# 计算持仓
signals["positions"] = signals["signal"].diff()
# 初始化账户余额和持有股票数
initial_capital = 100000.0
positions = pd.DataFrame(index=signals.index).fillna(0.0)
positions["stock"] = 100 * signals["signal"]
# 计算账户余额
portfolio = positions.multiply(stock_data["Close"], axis=0)
pos_diff = positions.diff()
portfolio["holdings"] = (positions.multiply(stock_data["Close"], axis=0)).sum(axis=1)
portfolio["cash"] = initial_capital - (pos_diff.multiply(stock_data["Close"], axis=0)).sum(axis=1).cumsum()
portfolio["total"] = portfolio["cash"] + portfolio["holdings"]
# 绘制图表
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111, ylabel="Portfolio value in $")
portfolio["total"].plot(ax=ax1, lw=2.)
ax1.plot(portfolio.loc[signals.positions == 1.0].index,
portfolio.total[signals.positions == 1.0],
"^", markersize=10, color="m")
ax1.plot(portfolio.loc[signals.positions == -1.0].index,
portfolio.total[signals.positions == -1.0],
"v", markersize=10, color="k")
plt.show()
文章为作者独立观点，不代表股票交易接口观点
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