def run(cache_name, cache_backend, expire_after, data_source, start, end, config, testing, tickers, filename, n, n_window):
# Set up variables needed for backtest
events_queue = queue.Queue()
initial_equity = PriceParser.parse(500000.00)
session = init_session(cache_name, cache_backend, expire_after)
period = 86400 # Seconds in a day
if len(tickers) == 1:
data = web.DataReader(tickers[0], data_source, start, end, session=session)
else:
data = web.DataReader(tickers, data_source, start, end, session=session)
# Use Generic Bar Handler with Pandas Bar Iterator
price_event_iterator = PandasBarEventIterator(data, period, tickers[0])
price_handler = GenericPriceHandler(events_queue, price_event_iterator)
# Use the Display Strategy
strategy1 = DisplayStrategy(n=n, n_window=n_window)
strategy2 = BuyAndHoldStrategy(tickers, events_queue)
strategy = Strategies(strategy1, strategy2)
# Use an example Position Sizer
position_sizer = FixedPositionSizer()
# Use an example Risk Manager
risk_manager = ExampleRiskManager()
# Use the default Portfolio Handler
portfolio_handler = PortfolioHandler(
initial_equity, events_queue, price_handler,
position_sizer, risk_manager
)
# Use the ExampleCompliance component
compliance = ExampleCompliance(config)
# Use a simulated IB Execution Handler
execution_handler = IBSimulatedExecutionHandler(
events_queue, price_handler, compliance
)
# Use the default Statistics
statistics = SimpleStatistics(config, portfolio_handler)
# Set up the backtest
backtest = Backtest(
price_handler, strategy,
portfolio_handler, execution_handler,
position_sizer, risk_manager,
statistics, initial_equity
)
results = backtest.simulate_trading(testing=testing)
statistics.save(filename)
return results
def run(config, testing, tickers, filename, n, n_window):
# Set up variables needed for backtest
events_queue = queue.Queue()
ig_service = IGService(config.IG.USERNAME, config.IG.PASSWORD,
config.IG.API_KEY, config.IG.ACCOUNT.TYPE)
ig_stream_service = IGStreamService(ig_service)
ig_session = ig_stream_service.create_session()
accountId = ig_session[u'accounts'][0][u'accountId']
ig_stream_service.connect(accountId)
initial_equity = PriceParser.parse(500000.00)
# Use IG Tick Price Handler
price_handler = IGTickPriceHandler(events_queue, ig_stream_service,
tickers)
# Use the Display Strategy
strategy = DisplayStrategy(n=n, n_window=n_window)
# Use an example Position Sizer
position_sizer = FixedPositionSizer()
# Use an example Risk Manager
risk_manager = ExampleRiskManager()
# Use the default Portfolio Handler
portfolio_handler = PortfolioHandler(initial_equity, events_queue,
price_handler, position_sizer,
risk_manager)
# Use the ExampleCompliance component
compliance = ExampleCompliance(config)
# Use a simulated IB Execution Handler
execution_handler = IBSimulatedExecutionHandler(events_queue,
price_handler, compliance)
# Use the default Statistics
statistics = SimpleStatistics(config, portfolio_handler)
# Set up the backtest
backtest = Backtest(price_handler, strategy, portfolio_handler,
execution_handler, position_sizer, risk_manager,
statistics, initial_equity)
results = backtest.simulate_trading(testing=testing)
statistics.save(filename)
return results
def run(config, testing, tickers, filename):
# Set up variables needed for backtest
events_queue = queue.Queue()
csv_dir = config.CSV_DATA_DIR
initial_equity = PriceParser.parse(500000.00)
# Use Yahoo Daily Price Handler
price_handler = YahooDailyCsvBarPriceHandler(csv_dir, events_queue,
tickers)
# Use the MAC Strategy
strategy = MovingAverageCrossStrategy(tickers, events_queue)
strategy = Strategies(strategy, DisplayStrategy())
# Use an example Position Sizer,
position_sizer = FixedPositionSizer()
# Use an example Risk Manager,
risk_manager = ExampleRiskManager()
# Use the default Portfolio Handler
portfolio_handler = PortfolioHandler(initial_equity, events_queue,
price_handler, position_sizer,
risk_manager)
# Use the ExampleCompliance component
compliance = ExampleCompliance(config)
# Use a simulated IB Execution Handler
execution_handler = IBSimulatedExecutionHandler(events_queue,
price_handler, compliance)
# Use the default Statistics
statistics = SimpleStatistics(config, portfolio_handler)
# Set up the backtest
backtest = Backtest(price_handler, strategy, portfolio_handler,
execution_handler, position_sizer, risk_manager,
statistics, initial_equity)
results = backtest.simulate_trading(testing=testing)
statistics.save(filename)
return results
def test_calculating_statistics(self):
"""
Purchase/sell multiple lots of AMZN, GOOG
at various prices/commissions to ensure
the arithmetic in calculating equity, drawdowns
and sharpe ratio is correct.
"""
# Create Statistics object
price_handler = PriceHandlerMock()
self.portfolio = Portfolio(price_handler, PriceParser.parse(500000.00))
portfolio_handler = PortfolioHandlerMock(self.portfolio)
statistics = SimpleStatistics(self.config, portfolio_handler)
# Check initialization was correct
self.assertEqual(PriceParser.display(statistics.equity[0]), 500000.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[0]), 00)
self.assertEqual(statistics.equity_returns[0], 0.0)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("BOT", "AMZN", 100,
PriceParser.parse(566.56),
PriceParser.parse(1.00))
t = "2000-01-01 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[1]), 499807.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[1]), 193.00)
self.assertEqual(statistics.equity_returns[1], -0.0386)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("BOT", "AMZN", 200,
PriceParser.parse(566.395),
PriceParser.parse(1.00))
t = "2000-01-02 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[2]), 499455.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[2]), 545.00)
self.assertEqual(statistics.equity_returns[2], -0.0705)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("BOT", "GOOG", 200,
PriceParser.parse(707.50),
PriceParser.parse(1.00))
t = "2000-01-03 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[3]), 499046.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[3]), 954.00)
self.assertEqual(statistics.equity_returns[3], -0.0820)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("SLD", "AMZN", 100,
PriceParser.parse(565.83),
PriceParser.parse(1.00))
t = "2000-01-04 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[4]), 499164.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[4]), 836.00)
self.assertEqual(statistics.equity_returns[4], 0.0236)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("BOT", "GOOG", 200,
PriceParser.parse(705.545),
PriceParser.parse(1.00))
t = "2000-01-05 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[5]), 499146.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[5]), 854.00)
self.assertEqual(statistics.equity_returns[5], -0.0036)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("SLD", "AMZN", 200,
PriceParser.parse(565.59),
PriceParser.parse(1.00))
t = "2000-01-06 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[6]), 499335.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[6]), 665.00)
self.assertEqual(statistics.equity_returns[6], 0.0379)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("SLD", "GOOG", 100,
PriceParser.parse(707.92),
PriceParser.parse(1.00))
t = "2000-01-07 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[7]), 499580.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[7]), 420.00)
self.assertEqual(statistics.equity_returns[7], 0.0490)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("SLD", "GOOG", 100,
PriceParser.parse(707.90),
PriceParser.parse(0.00))
t = "2000-01-08 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[8]), 499824.00)
self.assertEqual(PriceParser.display(statistics.drawdowns[8]), 176.00)
self.assertEqual(statistics.equity_returns[8], 0.0488)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("SLD", "GOOG", 100,
PriceParser.parse(707.92),
PriceParser.parse(0.50))
t = "2000-01-09 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[9]), 500069.50)
self.assertEqual(PriceParser.display(statistics.drawdowns[9]), 00.00)
self.assertEqual(statistics.equity_returns[9], 0.0491)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position("SLD", "GOOG", 100,
PriceParser.parse(707.78),
PriceParser.parse(1.00))
t = "2000-01-10 00:00:00"
statistics.update(t, portfolio_handler)
self.assertEqual(PriceParser.display(statistics.equity[10]), 500300.50)
self.assertEqual(PriceParser.display(statistics.drawdowns[10]), 00.00)
self.assertEqual(statistics.equity_returns[10], 0.0462)
# Test that results are calculated correctly.
results = statistics.get_results()
self.assertEqual(results["max_drawdown"], 954.00)
self.assertEqual(results["max_drawdown_pct"], 0.1908)
self.assertAlmostEqual(float(results["sharpe"]), 1.7575)
def run(config, testing, tickers, filename, n, n_window):
# Set up variables needed for backtest
events_queue = queue.Queue()
csv_dir = config.CSV_DATA_DIR
initial_equity = PriceParser.parse(500000.00)
d_tickers = OrderedDict()
for ticker in tickers:
ticker_path = os.path.join(csv_dir, "%s.csv" % ticker)
df = pd.io.parsers.read_csv(
ticker_path, header=0, parse_dates=True,
dayfirst=True, index_col=1,
names=("Ticker", "Time", "Bid", "Ask")
)
del df["Ticker"]
d_tickers[ticker] = df
if len(tickers) == 1:
ticker = tickers[0]
data = d_tickers[ticker]
else:
data = pd.Panel.from_dict(d_tickers)
data = data.transpose(2, 1, 0)
print(data)
print("Null:")
print(data.isnull().sum())
# Use Generic Tick Handler with Pandas Tick Iterator
price_event_iterator = PandasTickEventIterator(data, tickers[0])
price_handler = GenericPriceHandler(events_queue, price_event_iterator)
# Use the Display Strategy and ExampleStrategy
strategy1 = DisplayStrategy(n=n, n_window=n_window)
strategy2 = ExampleStrategy(tickers, events_queue)
strategy = Strategies(strategy1, strategy2)
# strategy = ExampleStrategy(tickers, events_queue)
# Use an example Position Sizer
position_sizer = FixedPositionSizer()
# Use an example Risk Manager
risk_manager = ExampleRiskManager()
# Use the default Portfolio Handler
portfolio_handler = PortfolioHandler(
initial_equity, events_queue, price_handler,
position_sizer, risk_manager
)
# Use the ExampleCompliance component
compliance = ExampleCompliance(config)
# Use a simulated IB Execution Handler
execution_handler = IBSimulatedExecutionHandler(
events_queue, price_handler, compliance
)
# Use the default Statistics
statistics = SimpleStatistics(config, portfolio_handler)
# Set up the backtest
backtest = Backtest(
price_handler, strategy,
portfolio_handler, execution_handler,
position_sizer, risk_manager,
statistics, initial_equity
)
results = backtest.simulate_trading(testing=testing)
statistics.save(filename)
return results
