自动化cpu数和文件路径

futures_backtrader.py

@@ -5,14 +5,25 @@ import pymysql
 import backtrader as bt
 import backtrader.indicators as btind
 import datetime
+import math
 import threading
 from datetime import datetime as dt
 import multiprocessing as mp
 from backtrader.feeds import PandasData
-
+import os
 # import multiprocessing
 import matplotlib
 
+cpu_count = os.cpu_count()
+os_name = os.name
+if os_name == 'posix':
+    path = '/Users/daniel/Documents/策略穷举2.csv'
+else:
+    path = 'D:\\BackTrader\\策略穷举0105。csv'
+
+
+
+
 class MyPandasData(PandasData):
     lines = ('change_pct', 'net_amount_main', 'net_pct_main', 'net_amount_xl', 'net_pct_xl', 'net_amount_l', 'net_pct_l'
              , 'net_amount_m', 'net_pct_m', 'net_amount_s', 'net_pct_s',)
@@ -216,7 +227,8 @@ if __name__ == '__main__':
     for num in range(20, 180, 10):
         for Volatility in range(10, 20, 1):
             for rate in range(5, 20, 1):
-                step = 400
+                #获得cpu数量，计算进程数
+                step = math.ceil(len(table_list)/cpu_count/100)*100
                 thread_list = []
                 result = mp.Manager().list()
                 result_change = mp.Manager().list()
@@ -255,4 +267,4 @@ if __name__ == '__main__':
                 print('每轮耗时：', endtime-stattime)
     edtime = dt.now()
     print('总耗时：', edtime - sttime)
-    df.to_csv('/Users/daniel/Documents/策略穷举2.csv', index=True)
+    df.to_csv(path, index=True)