整合到我之前发的代码中
时间: 2024-03-24 22:40:45 浏览: 18
好的,下面是一个整合了网格搜索的代码示例,你可以将其加入你之前发的代码中:
```
import numpy as np
from sklearn.model_selection import ParameterGrid
def calculate_profit(df, params):
# 策略参数
n = int(params['n'])
m = params['m']
ma_n = int(params['ma_n'])
volatility_factor = 2
# 计算均线和标准差
close = df['close'].values
ma = np.mean(close[-n:])
std = np.std(close[-n:], ddof=1)
# 计算上下轨道
upper = ma + volatility_factor * std
lower = ma - volatility_factor * std
# 计算MA指标
ma_values = df['close'].rolling(ma_n).mean().values
ma_current = ma_values[-1]
ma_previous = ma_values[-2]
# 寻找交易信号
signal = 0
close_current = close[-1]
close_previous = close[-2]
# 做多信号
if (close_current > upper) and (close_previous <= upper) and (close_current > ma_current) and (close_previous <= ma_previous) and (std < volatility_factor * ma_current):
signal = 1
# 做空信号
elif (close_current < lower) and (close_previous >= lower) and (close_current < ma_current) and (close_previous >= ma_previous) and (std < volatility_factor * ma_current):
signal = -1
# 平仓信号
elif ((close_current < ma) and (close_previous >= ma)) or ((close_current > ma) and (close_previous <= ma)):
signal = 0
return signal
def main():
# 加载数据
df = load_data()
# 定义参数范围
param_grid = {
'n': [10, 20, 30],
'm': [2, 3],
'ma_n': [5, 10, 20]
}
# 生成参数组合
param_list = list(ParameterGrid(param_grid))
# 遍历参数组合
best_profit = -np.inf
best_params = {}
for params in param_list:
# 计算收益率
profit = calculate_profit(df, params)
# 更新最优参数
if profit > best_profit:
best_profit = profit
best_params = params
# 输出最优参数和收益率
print('最优参数组合为:', best_params)
print('最优收益率为:', best_profit)
if __name__ == '__main__':
main()
```
其中,`load_data`函数用于加载数据,`calculate_profit`函数用于计算收益率,`param_grid`定义了参数范围,`ParameterGrid(param_grid)`生成了所有的参数组合,遍历参数组合时计算收益率,并记录最优的参数组合和收益率。运行`main`函数即可输出最优参数和收益率。
需要注意的是,这里的`calculate_profit`函数需要根据实际情况进行修改,以适应你的交易策略。