Output image

Author: szy1900

.idea/.gitignore

@@ -4,19 +4,14 @@
 from __future__ import print_function
 
 import datetime
-
-import numpy as np
-import pandas as pd
-import statsmodels.api as sm
 import math
 import os
-from strategy import Strategy
-from event import SignalEvent, OrderEvent
+from event import OrderEvent
 from backtest import Backtest
 from data import HistoricCSVDataHandler
 from execution import SimulatedExecutionHandler
 from portfolio import Portfolio
-
+from Strategies.MovingAverageCrossStrategy import MovingAverageCrossStrategy
 ## Tasks to do:
 ## 1. Complete the sea turtle strategy
 ## 2. Multiple strategies
@@ -47,66 +42,6 @@ def generate_naive_order(self, signal):
 
         return order
 
-
-class MovingAverageCrossStrategy(Strategy):
-    """
-    用来进行基本的移动平均跨越测录的实现，这个策略有一组短期和长期的简单移动平均值。
-    默认的短期/长期的窗口分别是100天和400天。
-    """
-
-    def __init__(
-            self, bars, events, short_window=100, long_window=400
-    ):
-        self.bars = bars
-        self.symbol_list = self.bars.symbol_list
-        self.events = events
-        self.short_window = short_window
-        self.long_window = long_window
-
-        self.bought = self._calculate_initial_bought()
-
-    def _calculate_initial_bought(self):
-        """
-        给bought字典增加键，对于所有的代码都设置值为OUT
-        """
-        bought = {}
-        for s in self.symbol_list:
-            bought[s] = 'OUT'
-        return bought
-
-    def calculate_signals(self, event):
-        """
-        基于MAC SMA生成一组新的信号，进入市场的标志就是短期的移动平均超过
-        长期的移动平均。
-        """
-        if event.type == 'MARKET':
-            for s in self.symbol_list:
-                bars = self.bars.get_latest_bars_values(
-                    s, "adj_close", N=self.long_window
-                )
-                bar_date = self.bars.get_latest_bar_datetime(s)
-                if bars is not None and bars != []:
-                    short_sma = np.mean(bars[-self.short_window:])
-                    long_sma = np.mean(bars[-self.long_window:])
-
-                    symbol = s
-                    dt = datetime.datetime.utcnow()
-                    sig_dir = ""
-                    order_price = self.bars.get_latest_bars_values(s, 'adj_close')
-                    if short_sma > long_sma and self.bought[s] == "OUT":
-                        print("LONG: %s" % bar_date)
-                        sig_dir = 'LONG'
-                        signal = SignalEvent(1, bar_date, symbol, dt, sig_dir, order_price ,1.0)
-                        self.events.put(signal)
-                        self.bought[s] = 'LONG'
-                    elif short_sma < long_sma and self.bought[s] == "LONG":
-                        print("SHORT:%s" % bar_date)
-                        sig_dir = 'EXIT'
-                        signal = SignalEvent(1, bar_date, symbol, dt, sig_dir, order_price, 1.0)
-                        self.events.put(signal)
-                        self.bought[s] = 'OUT'
-
-
 if __name__ == "__main__":
     path1 = os.path.abspath('.')
     csv_dir = 'data_csv'
@@ -118,7 +53,7 @@ def calculate_signals(self, event):
     start_date = datetime.datetime(2015, 5, 1, 0, 0, 0)
     backtest = Backtest(
         csv_dir, symbol_list, initial_capital, heartbeat,
-        start_date, HistoricCSVDataHandler, SimulatedExecutionHandler,
-        My_portfolio, MovingAverageCrossStrategy
+        start_date, data_handler_cls=HistoricCSVDataHandler, execution_handler_cls=SimulatedExecutionHandler,
+        portfolio_cls=My_portfolio, strategy_cls=MovingAverageCrossStrategy
     )
     backtest.run_trading()

.idea/Event_driven_framework.iml

@@ -0,0 +1,63 @@
+from Strategies.strategy import Strategy
+import numpy as np
+import datetime
+from event import SignalEvent
+
+
+class MovingAverageCrossStrategy(Strategy):
+    """
+    用来进行基本的移动平均跨越测录的实现，这个策略有一组短期和长期的简单移动平均值。
+    默认的短期/长期的窗口分别是100天和400天。
+    """
+
+    def __init__(
+            self, bars, events, short_window=100, long_window=400
+    ):
+        self.bars = bars
+        self.symbol_list = self.bars.symbol_list
+        self.events = events
+        self.short_window = short_window
+        self.long_window = long_window
+
+        self.bought = self._calculate_initial_bought()
+
+    def _calculate_initial_bought(self):
+        """
+        给bought字典增加键，对于所有的代码都设置值为OUT
+        """
+        bought = {}
+        for s in self.symbol_list:
+            bought[s] = 'OUT'
+        return bought
+
+    def calculate_signals(self, event):
+        """
+        基于MAC SMA生成一组新的信号，进入市场的标志就是短期的移动平均超过
+        长期的移动平均。
+        """
+        if event.type == 'MARKET':
+            for s in self.symbol_list:
+                bars = self.bars.get_latest_bars_values(
+                    s, "adj_close", N=self.long_window
+                )
+                bar_date = self.bars.get_latest_bar_datetime(s)
+                if bars is not None and bars != []:
+                    short_sma = np.mean(bars[-self.short_window:])
+                    long_sma = np.mean(bars[-self.long_window:])
+
+                    symbol = s
+                    dt = datetime.datetime.utcnow()
+                    sig_dir = ""
+                    order_price = self.bars.get_latest_bars_values(s, 'adj_close')
+                    if short_sma > long_sma and self.bought[s] == "OUT":
+                        print("LONG: %s" % bar_date)
+                        sig_dir = 'LONG'
+                        signal = SignalEvent(1, bar_date, symbol, dt, sig_dir, order_price, 1.0)
+                        self.events.put(signal)
+                        self.bought[s] = 'LONG'
+                    elif short_sma < long_sma and self.bought[s] == "LONG":
+                        print("SHORT:%s" % bar_date)
+                        sig_dir = 'EXIT'
+                        signal = SignalEvent(1, bar_date, symbol, dt, sig_dir, order_price, 1.0)
+                        self.events.put(signal)
+                        self.bought[s] = 'OUT'