python 枚举法选择最优策略参数

ゝ一纸荒年。 2023-06-06 04:59 2阅读 0赞

> 枚举20~60作为移动平均天数参数，选出金额最高的做为参数

#### 例程代码 ####

import datetime
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas_datareader as web
    class QuantAverBreak:
    
        def __init__(self):
            self.skip_days = 0  # 持股/持币状态
            self.cash_hold = 100000  # 初始资金
            self.posit_num = 0  # 持股数目
            self.market_total = 0  # 持股市值
    
        def run_factor_plot(self, stock_df, N):
    
            stock_df['Ma_n'] = stock_df.Close.rolling(window=N).mean()  # 增加N移动平均线
            list_diff = np.sign(stock_df.Close - stock_df.Ma_n)
    
            # 当符号为负时为收盘价向下跌破Ma_n，当符号为正时收盘价向上突破Ma_n
            stock_df['signal'] = np.sign(list_diff - list_diff.shift(1))
    
            for kl_index, today in stock_df.iterrows():
                # 买入/卖出执行代码
                if today.signal > 0 and self.skip_days == 0:  # 买入
                    start = stock_df.index.get_loc(kl_index)
                    self.skip_days = -1
                    self.posit_num = int(self.cash_hold / today.Close)
                    self.cash_hold = 0
    
                elif today.signal < 0 and self.skip_days == -1:  # 卖出 避免未买先卖
                    end = stock_df.index.get_loc(kl_index)
                    self.skip_days = 0
                    self.cash_hold = int(self.posit_num * today.Close)
                    self.market_total = 0
    
                if self.skip_days == -1:  # 持股
                    self.market_total = int(self.posit_num * today.Close)
                    stock_df.loc[kl_index, 'total'] = self.market_total
                else:  # 空仓
                    stock_df.loc[kl_index, 'total'] = self.cash_hold
    
            return stock_df['total'][-1]
    
    
    stock = web.DataReader("600410.SS", "yahoo", datetime.datetime(2018, 1, 1), datetime.datetime(2019, 1, 1))
    
    ma_list = []
    profit_list = []
    for ma in range(20, 60):
        examp_trade = QuantAverBreak()
        ma_list.append(ma)
        profit_list.append(examp_trade.run_factor_plot(stock, ma))
    
    profit_max=max(profit_list)
    print(profit_list.index(max(profit_list)))
    ma_max=ma_list[profit_list.index(max(profit_list))]
    
    
    plt.bar(ma_list, profit_list)
    plt.annotate('ma='+str(ma_max)+'\n'+str(profit_max),\
                 xy=(ma_max,profit_max),xytext=(ma_max-5, profit_max-10),arrowprops=dict(facecolor='yellow',shrink=0.1),\
                 horizontalalignment='left',verticalalignment='top')
    
    # 设置坐标标签字体大小
    plt.xlabel('均线参数')
    plt.ylabel('资金收益')
    # 设置坐标轴的取值范围
    plt.xlim(min(ma_list)-1, max(ma_list)+1)
    plt.ylim(min(profit_list)*0.99, max(profit_list)*1.01)
    # 设置x坐标轴刻度
    plt.xticks(np.arange(min(ma_list), max(ma_list)+1, 1))
    # 设置图例字体大小
    plt.legend(['profit_list'], loc='best')
    plt.title("均线最优参数")
    plt.show()

输出结果  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDU4MDk3Nw_size_16_color_FFFFFF_t_70]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDU4MDk3Nw_size_16_color_FFFFFF_t_70]: /images/20230601/2ac410626bdb48bba6058049fffe5708.png