虎年最终版

.idea/misc.xml

@@ -1,4 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Py38Quant" project-jdk-type="Python SDK" />
+  <component name="PyPackaging">
+    <option name="earlyReleasesAsUpgrades" value="true" />
+  </component>
 </project>

.idea/stock.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.8" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Py38Quant" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
     <orderEntry type="module" module-name="stocks_to_sql" />
     <orderEntry type="module" module-name="quantify01" />

QMT/demo.py

@@ -118,8 +118,10 @@ if __name__ == '__main__':
     # 对交易回调进行订阅，订阅后可以收到交易主推，返回0表示订阅成功
     subscribe_result = xt_trader.subscribe(acc)
     print('对交易回调进行订阅，订阅后可以收到交易主推，返回0表示订阅成功', subscribe_result)
-
-    xtdata.subscribe_whole_quote(["SH", "SZ"], callback=f)
+    # asset = xt_trader.query_stock_asset(acc)
+    # cash = asset.cash
+    # print(cash)
+    # xtdata.subscribe_whole_quote(["SH", "SZ"], callback=f)
     xtdata.run()
 
     #进入交互模式

futures_backtrader.py

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