Added a Backtest class, which replaces the script in backtest.py. Also added an examples directory, to make strategy testing straightforward.

backtest/backtest.py

@@ -1,81 +1,83 @@
 from __future__ import print_function
 
-import copy
 try:
     import Queue as queue
 except ImportError:
     import queue
-import threading
 import time
-from decimal import Decimal, getcontext
 
-from qsforex.execution.execution import SimulatedExecution
-from qsforex.portfolio.portfolio import Portfolio
 from qsforex import settings
-from qsforex.strategy.strategy import TestStrategy, MovingAverageCrossStrategy
-from qsforex.data.price import HistoricCSVPriceHandler
 
 
-def backtest(
-        events, ticker, strategy, portfolio, 
-        execution, heartbeat, max_iters=5000000
+class Backtest(object):
+    """
+    Enscapsulates the settings and components for carrying out
+    an event-driven backtest on the foreign exchange markets.
+    """
+    def __init__(
+        self, pairs, data_handler, strategy, 
+        strategy_params, portfolio, execution, 
+        equity=100000.0, heartbeat=0.0, 
+        max_iters=100000000
     ):
-    """
-    Carries out an infinite while loop that polls the 
-    events queue and directs each event to either the
-    strategy component of the execution handler. The
-    loop will then pause for "heartbeat" seconds and
-    continue unti the maximum number of iterations is
-    exceeded.
-    """
-    iters = 0
-    while iters < max_iters and ticker.continue_backtest:
-        try:
-            event = events.get(False)
-        except queue.Empty:
-            ticker.stream_next_tick()
-        else:
-            if event is not None:
-                if event.type == 'TICK':
-                    strategy.calculate_signals(event)
-                    portfolio.update_portfolio(event)
-                elif event.type == 'SIGNAL':
-                    portfolio.execute_signal(event)
-                elif event.type == 'ORDER':
-                    execution.execute_order(event)
-        time.sleep(heartbeat)
-        iters += 1
-    portfolio.output_results()
+        """
+        Initialises the backtest.
+        """
+        self.pairs = pairs
+        self.events = queue.Queue()
+        self.csv_dir = settings.CSV_DATA_DIR
+        self.ticker = data_handler(self.pairs, self.events, self.csv_dir)
+        self.strategy_params = strategy_params
+        self.strategy = strategy(
+            self.pairs, self.events, **self.strategy_params
+        )
+        self.equity = equity
+        self.heartbeat = heartbeat
+        self.max_iters = max_iters
+        self.portfolio = portfolio(
+            self.ticker, self.events, equity=self.equity, backtest=True
+        )
+        self.execution = execution()
 
+    def _run_backtest(self):
+        """
+        Carries out an infinite while loop that polls the 
+        events queue and directs each event to either the
+        strategy component of the execution handler. The
+        loop will then pause for "heartbeat" seconds and
+        continue unti the maximum number of iterations is
+        exceeded.
+        """
+        print("Running Backtest...")
+        iters = 0
+        while iters < self.max_iters and self.ticker.continue_backtest:
+            try:
+                event = self.events.get(False)
+            except queue.Empty:
+                self.ticker.stream_next_tick()
+            else:
+                if event is not None:
+                    if event.type == 'TICK':
+                        self.strategy.calculate_signals(event)
+                        self.portfolio.update_portfolio(event)
+                    elif event.type == 'SIGNAL':
+                        self.portfolio.execute_signal(event)
+                    elif event.type == 'ORDER':
+                        self.execution.execute_order(event)
+            time.sleep(self.heartbeat)
+            iters += 1
 
-if __name__ == "__main__":
-    heartbeat = 0.0
-    events = queue.Queue()
-    equity = settings.EQUITY
+    def _output_performance(self):
+        """
+        Outputs the strategy performance from the backtest.
+        """
+        print("Calculating Performance Metrics...")
+        self.portfolio.output_results()
 
-    # Load the historic CSV tick data filesw
-    pairs = ["GBPUSD"]
-    csv_dir = settings.CSV_DATA_DIR
-    if csv_dir is None:
-        print("No historic data directory provided - backtest terminating.")
-        sys.exit()
-
-    # Create the historic tick data streaming class
-    ticker = HistoricCSVPriceHandler(pairs, events, csv_dir)
-
-    # Create the strategy/signal generator, passing the 
-    # instrument and the events queue
-    strategy = MovingAverageCrossStrategy(
-        pairs, events, 500, 2000
-    )
-
-    # Create the portfolio object to track trades
-    portfolio = Portfolio(
-        ticker, events, equity=equity, backtest=True
-    )
-
-    # Create the simulated execution handler
-    execution = SimulatedExecution()
-    
-    # Carry out the backtest loop
-    backtest(events, ticker, strategy, portfolio, execution, heartbeat)
+    def simulate_trading(self):
+        """
+        Simulates the backtest and outputs portfolio performance.
+        """
+        self._run_backtest()
+        self._output_performance()
+        print("Backtest complete.")

examples/mac.py

@@ -0,0 +1,29 @@
+from __future__ import print_function
+
+from qsforex.backtest.backtest import Backtest
+from qsforex.execution.execution import SimulatedExecution
+from qsforex.portfolio.portfolio import Portfolio
+from qsforex import settings
+from qsforex.strategy.strategy import TestStrategy, MovingAverageCrossStrategy
+from qsforex.data.price import HistoricCSVPriceHandler
+
+
+if __name__ == "__main__":
+    # Trade on the "Cable" - GBP/USD
+    pairs = ["GBPUSD"]
+    
+    # Create the strategy parameters for the
+    # MovingAverageCrossStrategy
+    strategy_params = {
+        "short_window": 500, 
+        "long_window": 2000
+    }
+   
+    # Create and execute the backtest
+    backtest = Backtest(
+        pairs, HistoricCSVPriceHandler, 
+        MovingAverageCrossStrategy, strategy_params, 
+        Portfolio, SimulatedExecution, 
+        equity=settings.EQUITY
+    )
+    backtest.simulate_trading()