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@@ -130,6 +130,20 @@ class TestStrategy(bt.Strategy):
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def stop(self):
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# pass
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+ print(f'以{num}内最低值波动{Volatility}为支撑、乖离率为{rate}%，结果状态为：')
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+ print('正盈利的个股为：', len(result_change), '成功率为：', len(result) / len(table_list))
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+ print(
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+ f'总盈利:{np.sum(result_change)} 平均盈利:{np.mean(result_change)},最大盈利:{np.max(result_change)}, 最小盈利:{np.min(result_change)}')
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+ print(
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+ f'总亏损:{np.sum(result_change_fall)},平均亏损:{np.mean(result_change_fall)},最大亏损:{np.min(result_change_fall)} 最小亏损:{np.max(result_change_fall)}')
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+ endtime = dt.now()
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+ df.loc[len(df)] = [num, Volatility, len(result), len(result) / len(table_list), np.sum(result_change),
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+ np.mean(result_change), np.max(result_change), np.min(result_change),
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+ np.sum(result_change_fall), np.mean(result_change_fall),
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+ np.min(result_change_fall), np.max(result_change_fall)]
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+ print(df)
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+ print('每轮耗时：', endtime - stattime)
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+ df.to_csv(r'D:\Daniel\策略\策略穷举.csv', index=True)
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self.log(u'(MA趋势交易效果) Ending Value %.2f' % (self.broker.getvalue()))
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@@ -216,20 +230,18 @@ if __name__ == '__main__':
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sttime = dt.now()
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-
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- for num in range(60, 140, 20):
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+ thread_list = []
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+ for num in range(30, 70, 10):
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for Volatility in range(8, 10, 1):
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for rate in range(7, 10, 1):
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step = math.ceil(len(table_list) / mp.cpu_count())
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- thread_list = []
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+
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result = mp.Manager().list()
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result_change = mp.Manager().list()
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result_change_fall = mp.Manager().list()
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err_list = mp.Manager().list()
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print(f'{num}天波动率为{Volatility}%乖离率为{rate}')
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for i in range(0, len(table_list), step):
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-
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-
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stattime = dt.now()
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# thd = threading.local()
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# print(i)
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@@ -239,24 +251,10 @@ if __name__ == '__main__':
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# p.start()
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# p.join()
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# print(thread_list)
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- for thread in thread_list:
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- thread.start()
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- for thread in thread_list:
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- thread.join()
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-
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- print(f'以{num}内最低值波动{Volatility}为支撑、乖离率为{rate}%，结果状态为：')
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- print('正盈利的个股为：', len(result_change), '成功率为：', len(result)/len(table_list))
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- print(f'总盈利:{np.sum(result_change)} 平均盈利:{np.mean(result_change)},最大盈利:{np.max(result_change)}, 最小盈利:{np.min(result_change)}')
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- print(
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- f'总亏损:{np.sum(result_change_fall)},平均亏损:{np.mean(result_change_fall)},最大亏损:{np.min(result_change_fall)} 最小亏损:{np.max(result_change_fall)}')
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- endtime = dt.now()
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- df.loc[len(df)] = [num, Volatility, len(result), len(result)/len(table_list), np.sum(result_change),
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- np.mean(result_change), np.max(result_change), np.min(result_change),
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- np.sum(result_change_fall), np.mean(result_change_fall),
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- np.min(result_change_fall), np.max(result_change_fall)]
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- print(df)
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- print('每轮耗时：', endtime-stattime)
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- df.to_csv(r'D:\Daniel\策略\策略穷举.csv', index=True)
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+ for thread in thread_list:
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+ thread.start()
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+ for thread in thread_list:
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+ thread.join()
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edtime = dt.now()
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print('总耗时：', edtime - sttime)
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# df.to_csv(r'C:\Users\Daniel\Documents\策略穷举2.csv', index=True)
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