双均线策略代码多个股票-双均线策略赚了1亿

关于双均线策略代码多个股票的问题，我们总结了以下几点，给你解答：

1、双均线策略代码多个股票
2、双均线策略思路
3、双均线策略赚了1亿

双均线策略代码多个股票

# -*- coding: utf-8 -*-

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tushare as ts

# 获取股票数据
def get_stock_data(stock_code, start_date, end_date):
df = ts.get_k_data(stock_code, start=start_date, end=end_date)
df = df.sort_index(ascending=True)
return df

# 计算均线
def get_ma(df, n):
ma_list = []
for i in range(len(df)):
if i < n:
ma = np.mean(df['close'][:i+1])
else:
ma = np.mean(df['close'][i+1-n:i+1])
ma_list.append(round(ma, 2))
return ma_list

# 双均线策略
def double_ma_strategy(df, short_window, long_window):
# 计算短期均线
short_ma = get_ma(df, short_window)
# 计算长期均线
long_ma = get_ma(df, long_window)
# 初始化信号列表
signal_list = []
# 初始化买卖价格列表
buy_price_list = []
sell_price_list = []
# 初始化持仓状态
position = 0
# 遍历每一天的数据
for i in range(len(df)):
# 如果短期均线大于长期均线，且当前没有持仓，则买入
if short_ma[i] > long_ma[i] and position == 0:
signal_list.append(1)
buy_price_list.append(df['close'][i])
position = 1
# 如果短期均线小于长期均线，且当前持仓，则卖出
elif short_ma[i] < long_ma[i] and position == 1:
signal_list.append(-1)
sell_price_list.append(df['close'][i])
position = 0
# 其他情况，信号为0
else:
signal_list.append(0)
buy_price_list.append(np.nan)
sell_price_list.append(np.nan)
# 将信号列表添加到DataFrame中
df['signal'] = signal_list
df['buy_price'] = buy_price_list
df['sell_price'] = sell_price_list
return df

# 计算收益率
def get_return_rate(df):
# 计算每笔交易的收益率
return_list = []
for i in range(len(df)):
# 如果当天有买入信号
if df['signal'][i] == 1:
# 计算当天买入和卖出的收益率
return_rate = (df['sell_price'][i] - df['buy_price'][i]) / df['buy_price'][i]
return_list.append(return_rate)
else:
return_list.append(0)
# 计算每天的累计收益率
cum_return = np.cumsum(return_list)
# 将累计收益率添加到DataFrame中
df['cum_return'] = cum_return
return df

# 绘制图像
def plot_chart(df, stock_code):
# 绘制K线图
plt.subplot(211)
plt.title(stock_code)
plt.xlabel('Date')
plt.ylabel('Price')
df.plot(ax=plt.gca(), x='date', y='close')
# 绘制买卖信号
ax = plt.gca()
ax.scatter(df.index, df['buy_price'], color='green', marker='^', label='buy signal')
ax.scatter(df.index, df['sell_price'], color='red', marker='v', label='sell signal')
plt.legend(loc='best')
# 绘制累计收益率
plt.subplot(212)
plt.xlabel('Date')
plt.ylabel('Cum Return')
df.plot(ax=plt.gca(), x='date', y='cum_return')
plt.show()

# 主函数
def main():
# 设置参数
stock_code_list = ['600000', '600004']
start_date = '2015-01-01'
end_date = '2018-12-31'
short_window = 5
long_window = 10
# 遍历每只股票
for stock_code in stock_code_list:
# 获取股票数据
df = get_stock_data(stock_code, start_date, end_date)
# 双均线策略
df = double_ma_strategy(df, short_window, long_window)
# 计算收益率
df = get_return_rate(df)
# 绘制图像
plot_chart(df, stock_code)

if __name__ == '__main__':
main()