def size_order(self, portfolio, initial_order):
"""
Size the order to reflect the dollar-weighting of the
current equity account size based on pre-specified
ticker weights.
"""
ticker = initial_order.ticker
if initial_order.action == "EXIT":
# Obtain current quantity and liquidate
cur_quantity = portfolio.positions[ticker].quantity
if cur_quantity > 0:
initial_order.action = "SLD"
initial_order.quantity = cur_quantity
else:
initial_order.action = "BOT"
initial_order.quantity = cur_quantity
else:
weight = self.ticker_weights[ticker]
# Determine total portfolio value, work out dollar weight
# and finally determine integer quantity of shares to purchase
price = portfolio.price_handler.tickers[ticker]["adj_close"]
price = PriceParser.display(price)
equity = PriceParser.display(portfolio.equity)
dollar_weight = weight * equity
weighted_quantity = int(floor(dollar_weight / price))
initial_order.quantity = weighted_quantity
return initial_order
def setUp(self):
"""
Set up the Position object that will store the PnL.
"""
self.position = Position("BOT", "XOM", 100, PriceParser.parse(74.78),
PriceParser.parse(1.00),
PriceParser.parse(74.78),
PriceParser.parse(74.80))
def setUp(self):
"""
Set up the Position object that will store the PnL.
"""
self.position = Position(
"BOT", "XOM", 100,
PriceParser.parse(74.78), PriceParser.parse(1.00),
PriceParser.parse(74.78), PriceParser.parse(74.80)
)
def test_get_best_bid_ask(self):
"""
Tests that the 'get_best_bid_ask' method produces the
correct values depending upon validity of ticker.
"""
bid, ask = self.price_handler.get_best_bid_ask("AMZN")
self.assertEqual(PriceParser.display(bid, 5), 502.10001)
self.assertEqual(PriceParser.display(ask, 5), 502.11999)
bid, ask = self.price_handler.get_best_bid_ask("C")
def test_get_best_bid_ask(self):
"""
Tests that the 'get_best_bid_ask' method produces the
correct values depending upon validity of ticker.
"""
bid, ask = self.price_handler.get_best_bid_ask("AMZN")
self.assertEqual(PriceParser.display(bid, 5), 502.10001)
self.assertEqual(PriceParser.display(ask, 5), 502.11999)
bid, ask = self.price_handler.get_best_bid_ask("C")
def __init__(self):
self.tickers = {
"AAA": {
"adj_close": PriceParser.parse(50.00)
},
"BBB": {
"adj_close": PriceParser.parse(100.00)
},
"CCC": {
"adj_close": PriceParser.parse(1.00)
},
}
def record_trade(self, fill):
"""
Append all details about the FillEvent to the CSV trade log.
"""
fname = os.path.expanduser(os.path.join(self.config.OUTPUT_DIR, self.csv_filename))
with open( fname, 'a') as csvfile:
writer = csv.writer(csvfile)
writer.writerow([
fill.timestamp, fill.ticker,
fill.action, fill.quantity,
fill.exchange, PriceParser.display(fill.price, 4),
PriceParser.display(fill.commission, 4)
])
def record_trade(self, fill):
"""
Append all details about the FillEvent to the CSV trade log.
"""
fname = os.path.expanduser(os.path.join(self.config.OUTPUT_DIR, self.csv_filename))
with open(fname, 'a') as csvfile:
writer = csv.writer(csvfile)
writer.writerow([
fill.timestamp, fill.ticker,
fill.action, fill.quantity,
fill.exchange, PriceParser.display(fill.price, 4),
PriceParser.display(fill.commission, 4)
])
def _create_event(self, index, period, ticker, row):
"""
Obtain all elements of the bar from a row of dataframe
and return a BarEvent
"""
open_price = PriceParser.parse(row["Open"])
low_price = PriceParser.parse(row["Low"])
high_price = PriceParser.parse(row["High"])
close_price = PriceParser.parse(row["Close"])
adj_close_price = PriceParser.parse(row["Close"])
volume = int(row["Volume"])
bev = BarEvent(ticker, index, period, open_price, high_price,
low_price, close_price, volume, adj_close_price)
return bev
def test_open_short_position(self):
self.assertEqual(PriceParser.display(self.position.cost_basis), -7768.00)
self.assertEqual(PriceParser.display(self.position.market_value), -7769.00)
self.assertEqual(PriceParser.display(self.position.unrealised_pnl), -1.00)
self.assertEqual(PriceParser.display(self.position.realised_pnl), -1.0)
self.position.update_market_value(
PriceParser.parse(77.72), PriceParser.parse(77.72)
)
self.assertEqual(PriceParser.display(self.position.cost_basis), -7768.00)
self.assertEqual(PriceParser.display(self.position.market_value), -7772.00)
self.assertEqual(PriceParser.display(self.position.unrealised_pnl), -4.00)
self.assertEqual(PriceParser.display(self.position.realised_pnl), -4.0)
def test_price_from_long(self):
parsed = PriceParser.parse(self.long)
self.assertEqual(parsed, 200)
if PY2:
self.assertIsInstance(parsed, long) # noqa
else:
self.assertIsInstance(parsed, int)
def subscribe_ticker(self, ticker):
"""
Subscribes the price handler to a new ticker symbol.
"""
if ticker not in self.tickers:
try:
self._open_ticker_price_csv(ticker)
dft = self.tickers_data[ticker]
row0 = dft.iloc[0]
close = PriceParser.parse(row0["Close"])
# adj_close = PriceParser.parse(row0["Adj Close"])
ticker_prices = {
"close": close,
# "adj_close": adj_close,
"timestamp": dft.index[0]
}
self.tickers[ticker] = ticker_prices
except OSError:
print(
"Could not subscribe ticker %s "
"as no data CSV found for pricing." % ticker
)
else:
print(
"Could not subscribe ticker %s "
"as is already subscribed." % ticker
)
def run_monthly_rebalance(config, testing, filename, benchmark, ticker_weights,
title_str, start_date, end_date, equity):
config = settings.from_file(config, testing)
tickers = [t for t in ticker_weights.keys()]
# Set up variables needed for backtest
events_queue = queue.Queue()
csv_dir = config.CSV_DATA_DIR
initial_equity = PriceParser.parse(equity)
# Use Yahoo Daily Price Handler
price_handler = YahooDailyCsvBarPriceHandler(csv_dir,
events_queue,
tickers,
start_date=start_date,
end_date=end_date)
# Use the monthly liquidate and rebalance strategy
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
strategy = Strategies(strategy, DisplayStrategy())
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# 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)
def test_price_from_long(self):
parsed = PriceParser.parse(self.long)
self.assertEqual(parsed, 200)
if PY2:
self.assertIsInstance(parsed, long) # noqa
else:
self.assertIsInstance(parsed, int)
def size_order(self, portfolio, initial_order):
ticker = initial_order.ticker
if initial_order.action == "EXIT":
from pudb import set_trace; set_trace()
invested_action = portfolio.positions[ticker].action
if invested_action == "BOT":
initial_order.action = "SLD"
else:
initial_order.action = "BOT"
initial_order.quantity = abs(portfolio.positions[ticker].quantity)
return initial_order
else:
cur_price = portfolio.price_handler.get_last_close(ticker)
'''
The reason for having 'min(size_1, size_2)' is that:
In the case that the cash available, after deducting 5% for volatility, is not
enough for placing the order with 'size_1', we can then only place whatever
amount we can afford after spending all (95%) we have.
'''
size_1 = int(portfolio.cur_cash * self.max_risk // PriceParser.parse(self.max_volatility))
size_2 = int(portfolio.cur_cash * (1 - self.max_risk) // cur_price)
initial_order.quantity = min(size_1, size_2)
initial_order.quantity = self.initial_quantity
return initial_order
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(100000.00)
# Use Yahoo Daily Price Handler
start_date = datetime.datetime(2009, 8, 3)
end_date = datetime.datetime(2019, 8, 1)
price_handler = YahooDailyCsvBarPriceHandler(csv_dir,
events_queue,
tickers,
start_date=start_date,
end_date=end_date)
# Use the KalmanPairsTrading Strategy
strategy = KalmanPairsTradingStrategy(tickers, events_queue)
strategy = Strategies(strategy)
# Use the Naive Position Sizer (suggested quantities are followed)
position_sizer = NaivePositionSizer()
# 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 Tearsheet Statistics
title = ["Kalman Filter Pairs Trade on TLT/IEI"]
statistics = TearsheetStatistics(config, portfolio_handler, title)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
price_handler=price_handler,
portfolio_handler=portfolio_handler,
execution_handler=execution_handler,
position_sizer=position_sizer,
risk_manager=risk_manager,
statistics=statistics)
results = backtest.start_trading(testing=testing)
statistics.save(filename)
return results
def run(config, testing, tickers, filename):
# Benchmark ticker
benchmark = 'SP500TR'
# Set up variables needed for backtest
title = [
'Moving Average Crossover Example',
__file__,
','.join(tickers) + ': 100x400'
]
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)
# 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 = TearsheetStatistics(
config, portfolio_handler, title, benchmark
)
# 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 setUp(self):
"""
Set up the Portfolio object that will store the
collection of Position objects, supplying it with
$500,000.00 USD in initial cash.
"""
ph = PriceHandlerMock()
cash = PriceParser.parse(500000.00)
self.portfolio = Portfolio(ph, cash)
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):
# Set up variables needed for backtest
title = ["Intraday AREX Machine Learning Prediction Strategy"]
events_queue = queue.Queue()
csv_dir = "/path/to/your/csv/data/"
initial_equity = 500000.0
# Use DTN IQFeed Intraday Bar Price Handler
start_date = datetime.datetime(2013, 1, 1)
end_date = datetime.datetime(2014, 3, 11)
price_handler = IQFeedIntradayCsvBarPriceHandler(csv_dir,
events_queue,
tickers,
start_date=start_date)
# Use the ML Intraday Prediction Strategy
model_pickle_file = '/path/to/your/ml_model_lda.pkl'
strategy = IntradayMachineLearningPredictionStrategy(tickers,
events_queue,
model_pickle_file,
lags=5)
# Use the Naive Position Sizer where
# suggested quantities are followed
position_sizer = NaivePositionSizer()
# Use an example Risk Manager
risk_manager = ExampleRiskManager()
# Use the default Portfolio Handler
portfolio_handler = PortfolioHandler(PriceParser.parse(initial_equity),
events_queue, price_handler,
position_sizer, risk_manager)
# Use the Tearsheet Statistics
statistics = TearsheetStatistics(
config,
portfolio_handler,
title=title,
periods=int(252 * 6.5 * 60) # Minutely periods
)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
portfolio_handler=portfolio_handler,
position_sizer=position_sizer,
price_handler=price_handler,
statistics=statistics)
results = backtest.start_trading(testing=testing)
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)
start_date = datetime.datetime(2006, 11, 1)
end_date = datetime.datetime(2016, 10, 12)
# Use Yahoo Daily Price Handler
price_handler = YahooDailyCsvBarPriceHandler(csv_dir,
events_queue,
tickers,
start_date=start_date,
end_date=end_date)
# Use the monthly liquidate and rebalance strategy
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
strategy = Strategies(strategy, DisplayStrategy())
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
ticker_weights = {
"SPY": 0.6,
"AGG": 0.4,
}
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# 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
title = ["US Equities/Bonds 60/40 ETF Strategy"]
benchmark = "SPY"
statistics = TearsheetStatistics(config, portfolio_handler, title,
benchmark)
# 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_will_liquidate_positions(self):
"""
Ensure positions will be liquidated completely when asked.
Include a long & a short.
"""
self.portfolio._add_position("BOT", "AAA", 100,
PriceParser.parse(60.00), 0.0)
self.portfolio._add_position("BOT", "BBB", -100,
PriceParser.parse(60.00), 0.0)
exit_a = SuggestedOrder("AAA", "EXIT", 0)
exit_b = SuggestedOrder("BBB", "EXIT", 0)
sized_a = self.position_sizer.size_order(self.portfolio, exit_a)
sized_b = self.position_sizer.size_order(self.portfolio, exit_b)
self.assertEqual(sized_a.action, "SLD")
self.assertEqual(sized_b.action, "BOT")
self.assertEqual(sized_a.quantity, 100)
self.assertEqual(sized_a.quantity, 100)
def test_open_short_position(self):
self.assertEqual(PriceParser.display(self.position.cost_basis),
-7768.00)
self.assertEqual(PriceParser.display(self.position.market_value),
-7769.00)
self.assertEqual(PriceParser.display(self.position.unrealised_pnl),
-1.00)
self.assertEqual(PriceParser.display(self.position.realised_pnl), -1.0)
self.position.update_market_value(PriceParser.parse(77.72),
PriceParser.parse(77.72))
self.assertEqual(PriceParser.display(self.position.cost_basis),
-7768.00)
self.assertEqual(PriceParser.display(self.position.market_value),
-7772.00)
self.assertEqual(PriceParser.display(self.position.unrealised_pnl),
-4.00)
self.assertEqual(PriceParser.display(self.position.realised_pnl), -4.0)
def run(config, testing, tickers, filename):
# Set up variables needed for backtest
events_queue = queue.Queue()
csv_dir = "/path/to/your/csv/data/"
initial_equity = PriceParser.parse(500000.00)
# Use DTN IQFeed Intraday Bar Price Handler
start_date = datetime.datetime(2013, 1, 1)
price_handler = IQFeedIntradayCsvBarPriceHandler(csv_dir,
events_queue,
tickers,
start_date=start_date)
# Use the ML Intraday Prediction Strategy
model_pickle_file = '/path/to/your/ml_model_lda.pkl'
strategy = IntradayMachineLearningPredictionStrategy(tickers,
events_queue,
model_pickle_file,
lags=5)
strategy = Strategies(strategy, DisplayStrategy())
# Use the Naive Position Sizer (suggested quantities are followed)
position_sizer = NaivePositionSizer()
# 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 Tearsheet Statistics
statistics = TearsheetStatistics(
config,
portfolio_handler,
title=["Intraday AREX Machine Learning Prediction Strategy"],
periods=int(252 * 6.5 * 60) # Minutely periods
)
# 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
start_date = datetime.datetime(2013, 7, 1)
end_date = datetime.datetime(2019, 6, 1)
price_handler = YahooDailyCsvBarPriceHandler(csv_dir, events_queue, tickers, start_date=start_date, end_date=end_date)
# Use the Cointegration Bollinger Bands trading strategy
weights = np.array([1.0, -1.213])
lookback = 15
entry_z = 1.5
exit_z = 0.5
base_quantity = 10000
strategy = CointegrationBollingerBandsStrategy(tickers[1:], events_queue, lookback, weights, entry_z, exit_z, base_quantity)
strategy = Strategies(strategy)
# Use the Naive Position Sizer
# where suggested quantities are followed
position_sizer = NaivePositionSizer()
# 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 Tearsheet Statistics
title = ["Aluminum Smelting Strategy - ARNC/UNG"]
statistics = TearsheetStatistics(config, portfolio_handler, title, benchmark=tickers[0])
# Set up the backtest
backtest = TradingSession(config, strategy,
tickers[1:], initial_equity, start_date, end_date,
events_queue, price_handler=price_handler,
portfolio_handler=portfolio_handler,
execution_handler=execution_handler,
position_sizer=position_sizer,
risk_manager=risk_manager,
statistics=statistics, benchmark=tickers[0])
results = backtest.start_trading(testing=testing)
statistics.save(filename)
return results
def subscribe_ticker(self, ticker):
"""
Subscribes the price handler to a new ticker symbol.
"""
if ticker not in self.tickers:
ticker_id = next(self.tickers_loop_id)
ticker = next(self.tickers_loop)
data = self.get_data_from_IB(ticker_id)
close = PriceParser.parse(data[1])
adj_close = PriceParser.parse(data[2])
timestamp = data[0]
ticker_prices = {
"close": close,
"adj_close": adj_close,
"timestamp": timestamp
}
self.tickers[ticker] = ticker_prices
else:
print("Could not subscribe ticker %s "
"as is already subscribed." % ticker)
def run_monthly_rebalance(config, testing, filename, benchmark, ticker_weights,
title_str, start_date, end_date, equity):
config = settings.from_file(config, testing)
tickers = [t for t in ticker_weights.keys()]
# Set up variables needed for backtest
events_queue = queue.Queue()
csv_dir = config.CSV_DATA_DIR
initial_equity = PriceParser.parse(equity)
# Use Yahoo Daily Price Handler
price_handler = YahooDailyCsvBarPriceHandler(csv_dir,
events_queue,
tickers,
start_date=start_date,
end_date=end_date)
# Use the monthly liquidate and rebalance strategy
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
#strategy = Strategies(strategy, DisplayStrategy())
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# 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
title = [title_str]
statistics = TearsheetStatistics(config, portfolio_handler, title,
benchmark)
# 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, start_date, end_date):
# 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 = SqliteDBBarPriceHandler(
csv_dir, events_queue, tickers, start_date, end_date
)
# Use the Buy and Hold Strategy
strategy = CustomStrategy(tickers, events_queue)
strategy = Strategies(strategy, DisplayStrategy())
# Use an example Position Sizer
position_sizer = CustomPositionSizer()
# 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 = TearsheetStatistics(
config, portfolio_handler, title=""
)
# 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):
# Benchmark ticker
benchmark = 'SP500TR'
# Set up variables needed for backtest
title = [
'Moving Average Crossover Example', __file__,
','.join(tickers) + ': 100x400'
]
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)
# 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 = TearsheetStatistics(config, portfolio_handler, title,
benchmark)
# 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 Buy and Hold Strategy
strategy = BuyAndHoldStrategy(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 run(config, testing, tickers, filename):
# Set up variables needed for backtest
events_queue = queue.Queue()
csv_dir = config.CSV_DATA_DIR
initial_invst = 1000000.00
initial_equity = PriceParser.parse(initial_invst)
# Use Yahoo Daily Price Handler
price_handler = YahooDailyCsvBarPriceHandler(
csv_dir,
events_queue,
tickers,
)
# Use the KalmanPairsTrading Strategy
strategy = KalmanPairsTradingStrategy(tickers, events_queue, initial_invst)
strategy = Strategies(strategy, DisplayStrategy())
# Use the Naive Position Sizer (suggested quantities are followed)
position_sizer = NaivePositionSizer()
# 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 = TearsheetStatistics(config, portfolio_handler, title="")
# 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)
hist = results['cum_returns']
print('==:++==')
print(hist.to_csv('6pair.csv', header=['date,total asset']))
statistics.save('output')
return results
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)
# Use Yahoo Daily Price Handler
price_handler = YahooDailyCsvBarPriceHandler(csv_dir, events_queue,
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 test_display(self):
parsed = PriceParser.parse(self.float)
displayed = PriceParser.display(parsed)
self.assertEqual(displayed, 10.12)
def setUp(self):
self.position = Position(
"SLD", "PG", 100,
PriceParser.parse(77.69), PriceParser.parse(1.00),
PriceParser.parse(77.68), PriceParser.parse(77.70)
)
def test_stream_all_ticks(self):
"""
The initialisation of the class will open the three
test CSV files, then merge and sort them. They will
then be stored in a member "tick_stream". This will
be used for streaming the ticks.
"""
# Stream to Tick #1 (GOOG)
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["GOOG"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"), "01-02-2016 00:00:01.358000")
self.assertEqual(
PriceParser.display(self.price_handler.tickers["GOOG"]["bid"], 5),
683.56000)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["GOOG"]["ask"], 5),
683.58000)
# Stream to Tick #2 (AMZN)
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["AMZN"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"), "01-02-2016 00:00:01.562000")
self.assertEqual(
PriceParser.display(self.price_handler.tickers["AMZN"]["bid"], 5),
502.10001)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["AMZN"]["ask"], 5),
502.11999)
# Stream to Tick #3 (MSFT)
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["MSFT"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"), "01-02-2016 00:00:01.578000")
self.assertEqual(
PriceParser.display(self.price_handler.tickers["MSFT"]["bid"], 5),
50.14999)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["MSFT"]["ask"], 5),
50.17001)
# Stream to Tick #10 (GOOG)
for i in range(4, 11):
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["GOOG"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"), "01-02-2016 00:00:05.215000")
self.assertEqual(
PriceParser.display(self.price_handler.tickers["GOOG"]["bid"], 5),
683.56001)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["GOOG"]["ask"], 5),
683.57999)
# Stream to Tick #20 (GOOG)
for i in range(11, 21):
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["MSFT"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"), "01-02-2016 00:00:09.904000")
self.assertEqual(
PriceParser.display(self.price_handler.tickers["MSFT"]["bid"], 5),
50.15000)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["MSFT"]["ask"], 5),
50.17000)
# Stream to Tick #30 (final tick, AMZN)
for i in range(21, 31):
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["AMZN"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"), "01-02-2016 00:00:14.616000")
self.assertEqual(
PriceParser.display(self.price_handler.tickers["AMZN"]["bid"], 5),
502.10015)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["AMZN"]["ask"], 5),
502.11985)
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)
start_date = datetime.datetime(2006, 11, 1)
end_date = datetime.datetime(2016, 10, 12)
# Use Yahoo Daily Price Handler
price_handler = YahooDailyCsvBarPriceHandler(
csv_dir, events_queue, tickers,
start_date=start_date, end_date=end_date
)
# Use the monthly liquidate and rebalance strategy
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
strategy = Strategies(strategy, DisplayStrategy())
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
ticker_weights = {
"SPY": 0.6,
"AGG": 0.4,
}
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# 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
title = ["US Equities/Bonds 60/40 ETF Strategy"]
benchmark = "SPY"
statistics = TearsheetStatistics(
config, portfolio_handler, title, benchmark
)
# 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_calculate_round_trip(self):
"""
Purchase/sell multiple lots of AMZN and GOOG
at various prices/commissions to check the
arithmetic and cost handling.
"""
# Buy 300 of AMZN over two transactions
self.portfolio.transact_position(
"BOT", "AMZN", 100,
PriceParser.parse(566.56), PriceParser.parse(1.00)
)
self.portfolio.transact_position(
"BOT", "AMZN", 200,
PriceParser.parse(566.395), PriceParser.parse(1.00)
)
# Buy 200 GOOG over one transaction
self.portfolio.transact_position(
"BOT", "GOOG", 200,
PriceParser.parse(707.50), PriceParser.parse(1.00)
)
# Add to the AMZN position by 100 shares
self.portfolio.transact_position(
"SLD", "AMZN", 100,
PriceParser.parse(565.83), PriceParser.parse(1.00)
)
# Add to the GOOG position by 200 shares
self.portfolio.transact_position(
"BOT", "GOOG", 200,
PriceParser.parse(705.545), PriceParser.parse(1.00)
)
# Sell 200 of the AMZN shares
self.portfolio.transact_position(
"SLD", "AMZN", 200,
PriceParser.parse(565.59), PriceParser.parse(1.00)
)
# Multiple transactions bundled into one (in IB)
# Sell 300 GOOG from the portfolio
self.portfolio.transact_position(
"SLD", "GOOG", 100,
PriceParser.parse(704.92), PriceParser.parse(1.00)
)
self.portfolio.transact_position(
"SLD", "GOOG", 100,
PriceParser.parse(704.90), PriceParser.parse(0.00)
)
self.portfolio.transact_position(
"SLD", "GOOG", 100,
PriceParser.parse(704.92), PriceParser.parse(0.50)
)
# Finally, sell the remaining GOOG 100 shares
self.portfolio.transact_position(
"SLD", "GOOG", 100,
PriceParser.parse(704.78), PriceParser.parse(1.00)
)
# The figures below are derived from Interactive Brokers
# demo account using the above trades with prices provided
# by their demo feed.
self.assertEqual(len(self.portfolio.positions), 0)
self.assertEqual(len(self.portfolio.closed_positions), 2)
self.assertEqual(PriceParser.display(self.portfolio.cur_cash), 499100.50)
self.assertEqual(PriceParser.display(self.portfolio.equity), 499100.50)
self.assertEqual(PriceParser.display(self.portfolio.unrealised_pnl), 0.00)
self.assertEqual(PriceParser.display(self.portfolio.realised_pnl), -899.50)
def setUp(self):
self.position = Position("SLD", "PG", 100, PriceParser.parse(77.69),
PriceParser.parse(1.00),
PriceParser.parse(77.68),
PriceParser.parse(77.70))
def test_realised_unrealised_calcs(self):
self.assertEqual(
PriceParser.display(self.position.unrealised_pnl), -1.00
)
self.assertEqual(
PriceParser.display(self.position.realised_pnl), 0.00
)
self.position.update_market_value(
PriceParser.parse(75.77), PriceParser.parse(75.79)
)
self.assertEqual(
PriceParser.display(self.position.unrealised_pnl), 99.00
)
self.position.transact_shares(
"SLD", 100,
PriceParser.parse(75.78), PriceParser.parse(1.00)
)
self.assertEqual(
PriceParser.display(self.position.unrealised_pnl), 99.00
) # still high
self.assertEqual(
PriceParser.display(self.position.realised_pnl), 98.00
)
self.position.update_market_value(
PriceParser.parse(75.77), PriceParser.parse(75.79)
)
self.assertEqual(
PriceParser.display(self.position.unrealised_pnl), 0.00
)
def test_calculate_round_trip(self):
"""
After the subsequent purchase, carry out two more buys/longs
and then close the position out with two additional sells/shorts.
The following prices have been tested against those calculated
via Interactive Brokers' Trader Workstation (TWS).
"""
self.position.transact_shares(
"BOT", 100, PriceParser.parse(74.63), PriceParser.parse(1.00)
)
self.position.transact_shares(
"BOT", 250, PriceParser.parse(74.620), PriceParser.parse(1.25)
)
self.position.transact_shares(
"SLD", 200, PriceParser.parse(74.58), PriceParser.parse(1.00)
)
self.position.transact_shares(
"SLD", 250, PriceParser.parse(75.26), PriceParser.parse(1.25)
)
self.position.update_market_value(
PriceParser.parse(77.75), PriceParser.parse(77.77)
)
self.assertEqual(self.position.action, "BOT")
self.assertEqual(self.position.ticker, "XOM")
self.assertEqual(self.position.quantity, 0)
self.assertEqual(self.position.buys, 450)
self.assertEqual(self.position.sells, 450)
self.assertEqual(self.position.net, 0)
self.assertEqual(
PriceParser.display(self.position.avg_bot, 5), 74.65778
)
self.assertEqual(
PriceParser.display(self.position.avg_sld, 5), 74.95778
)
self.assertEqual(PriceParser.display(self.position.total_bot), 33596.00)
self.assertEqual(PriceParser.display(self.position.total_sld), 33731.00)
self.assertEqual(PriceParser.display(self.position.net_total), 135.00)
self.assertEqual(PriceParser.display(self.position.total_commission), 5.50)
self.assertEqual(PriceParser.display(self.position.net_incl_comm), 129.50)
self.assertEqual(
PriceParser.display(self.position.avg_price, 3), 74.665
)
self.assertEqual(PriceParser.display(self.position.cost_basis), 0.00)
self.assertEqual(PriceParser.display(self.position.market_value), 0.00)
self.assertEqual(PriceParser.display(self.position.unrealised_pnl), 0.00)
self.assertEqual(PriceParser.display(self.position.realised_pnl), 129.50)
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 test_rounded_float(self):
parsed = PriceParser.parse(self.rounded_float)
# Expect 100,000,000
self.assertEqual(parsed, 100000000)
self.assertIsInstance(parsed, int)
def test_price_from_int(self):
parsed = PriceParser.parse(self.int)
self.assertEqual(parsed, 200)
self.assertIsInstance(parsed, int)
def test_price_from_float(self):
parsed = PriceParser.parse(self.float)
self.assertEqual(parsed, 101234567)
self.assertIsInstance(parsed, int)
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
def test_unparsed_display(self):
displayed = PriceParser.display(self.float)
self.assertEqual(displayed, 10.12)
def test_calculate_round_trip(self):
"""
After the subsequent purchase, carry out two more buys/longs
and then close the position out with two additional sells/shorts.
The following prices have been tested against those calculated
via Interactive Brokers' Trader Workstation (TWS).
"""
self.position.transact_shares("BOT", 100, PriceParser.parse(74.63),
PriceParser.parse(1.00))
self.position.transact_shares("BOT", 250, PriceParser.parse(74.620),
PriceParser.parse(1.25))
self.position.transact_shares("SLD", 200, PriceParser.parse(74.58),
PriceParser.parse(1.00))
self.position.transact_shares("SLD", 250, PriceParser.parse(75.26),
PriceParser.parse(1.25))
self.position.update_market_value(PriceParser.parse(77.75),
PriceParser.parse(77.77))
self.assertEqual(self.position.action, "BOT")
self.assertEqual(self.position.ticker, "XOM")
self.assertEqual(self.position.quantity, 0)
self.assertEqual(self.position.buys, 450)
self.assertEqual(self.position.sells, 450)
self.assertEqual(self.position.net, 0)
self.assertEqual(PriceParser.display(self.position.avg_bot, 5),
74.65778)
self.assertEqual(PriceParser.display(self.position.avg_sld, 5),
74.95778)
self.assertEqual(PriceParser.display(self.position.total_bot),
33596.00)
self.assertEqual(PriceParser.display(self.position.total_sld),
33731.00)
self.assertEqual(PriceParser.display(self.position.net_total), 135.00)
self.assertEqual(PriceParser.display(self.position.total_commission),
5.50)
self.assertEqual(PriceParser.display(self.position.net_incl_comm),
129.50)
self.assertEqual(PriceParser.display(self.position.avg_price, 3),
74.665)
self.assertEqual(PriceParser.display(self.position.cost_basis), 0.00)
self.assertEqual(PriceParser.display(self.position.market_value), 0.00)
self.assertEqual(PriceParser.display(self.position.unrealised_pnl),
0.00)
self.assertEqual(PriceParser.display(self.position.realised_pnl),
129.50)
def setUp(self):
self.position = Position(
"BOT", "XOM", 100,
PriceParser.parse(74.78), PriceParser.parse(1.00),
PriceParser.parse(74.77), PriceParser.parse(74.79)
)
def test_calculate_round_trip(self):
"""
After the subsequent sale, carry out two more sells/shorts
and then close the position out with two additional buys/longs.
The following prices have been tested against those calculated
via Interactive Brokers' Trader Workstation (TWS).
"""
self.position.transact_shares("SLD", 100, PriceParser.parse(77.68),
PriceParser.parse(1.00))
self.position.transact_shares("SLD", 50, PriceParser.parse(77.70),
PriceParser.parse(1.00))
self.position.transact_shares("BOT", 100, PriceParser.parse(77.77),
PriceParser.parse(1.00))
self.position.transact_shares("BOT", 150, PriceParser.parse(77.73),
PriceParser.parse(1.00))
self.position.update_market_value(PriceParser.parse(77.72),
PriceParser.parse(77.72))
self.assertEqual(self.position.action, "SLD")
self.assertEqual(self.position.ticker, "PG")
self.assertEqual(self.position.quantity, 0)
self.assertEqual(self.position.buys, 250)
self.assertEqual(self.position.sells, 250)
self.assertEqual(self.position.net, 0)
self.assertEqual(PriceParser.display(self.position.avg_bot, 3), 77.746)
self.assertEqual(PriceParser.display(self.position.avg_sld, 3), 77.688)
self.assertEqual(PriceParser.display(self.position.total_bot),
19436.50)
self.assertEqual(PriceParser.display(self.position.total_sld),
19422.00)
self.assertEqual(PriceParser.display(self.position.net_total), -14.50)
self.assertEqual(PriceParser.display(self.position.total_commission),
5.00)
self.assertEqual(PriceParser.display(self.position.net_incl_comm),
-19.50)
self.assertEqual(PriceParser.display(self.position.avg_price, 5),
77.67600)
self.assertEqual(PriceParser.display(self.position.cost_basis), 0.00)
self.assertEqual(PriceParser.display(self.position.market_value), 0.00)
self.assertEqual(PriceParser.display(self.position.unrealised_pnl),
0.00)
self.assertEqual(PriceParser.display(self.position.realised_pnl),
-19.50)
def get_best_bid_ask(self, ticker):
prices = {
"GOOG": (PriceParser.parse(705.46), PriceParser.parse(705.46)),
"AMZN": (PriceParser.parse(564.14), PriceParser.parse(565.14)),
}
return prices[ticker]
def test_stream_all_ticks(self):
"""
The initialisation of the class will open the three
test CSV files, then merge and sort them. They will
then be stored in a member "tick_stream". This will
be used for streaming the ticks.
"""
# Stream to Tick #1 (GOOG)
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["GOOG"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"
),
"01-02-2016 00:00:01.358000"
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["GOOG"]["bid"], 5),
683.56000
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["GOOG"]["ask"], 5),
683.58000
)
# Stream to Tick #2 (AMZN)
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["AMZN"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"
),
"01-02-2016 00:00:01.562000"
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["AMZN"]["bid"], 5),
502.10001
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["AMZN"]["ask"], 5),
502.11999
)
# Stream to Tick #3 (MSFT)
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["MSFT"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"
),
"01-02-2016 00:00:01.578000"
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["MSFT"]["bid"], 5),
50.14999
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["MSFT"]["ask"], 5),
50.17001
)
# Stream to Tick #10 (GOOG)
for i in range(4, 11):
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["GOOG"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"
),
"01-02-2016 00:00:05.215000"
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["GOOG"]["bid"], 5),
683.56001
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["GOOG"]["ask"], 5),
683.57999
)
# Stream to Tick #20 (GOOG)
for i in range(11, 21):
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["MSFT"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"
),
"01-02-2016 00:00:09.904000"
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["MSFT"]["bid"], 5),
50.15000
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["MSFT"]["ask"], 5),
50.17000
)
# Stream to Tick #30 (final tick, AMZN)
for i in range(21, 31):
self.price_handler.stream_next()
self.assertEqual(
self.price_handler.tickers["AMZN"]["timestamp"].strftime(
"%d-%m-%Y %H:%M:%S.%f"
),
"01-02-2016 00:00:14.616000"
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["AMZN"]["bid"], 5),
502.10015
)
self.assertEqual(
PriceParser.display(self.price_handler.tickers["AMZN"]["ask"], 5),
502.11985
)
def test_calculate_round_trip(self):
"""
After the subsequent sale, carry out two more sells/shorts
and then close the position out with two additional buys/longs.
The following prices have been tested against those calculated
via Interactive Brokers' Trader Workstation (TWS).
"""
self.position.transact_shares(
"SLD", 100, PriceParser.parse(77.68), PriceParser.parse(1.00)
)
self.position.transact_shares(
"SLD", 50, PriceParser.parse(77.70), PriceParser.parse(1.00)
)
self.position.transact_shares(
"BOT", 100, PriceParser.parse(77.77), PriceParser.parse(1.00)
)
self.position.transact_shares(
"BOT", 150, PriceParser.parse(77.73), PriceParser.parse(1.00)
)
self.position.update_market_value(
PriceParser.parse(77.72), PriceParser.parse(77.72)
)
self.assertEqual(self.position.action, "SLD")
self.assertEqual(self.position.ticker, "PG")
self.assertEqual(self.position.quantity, 0)
self.assertEqual(self.position.buys, 250)
self.assertEqual(self.position.sells, 250)
self.assertEqual(self.position.net, 0)
self.assertEqual(
PriceParser.display(self.position.avg_bot, 3), 77.746
)
self.assertEqual(
PriceParser.display(self.position.avg_sld, 3), 77.688
)
self.assertEqual(PriceParser.display(self.position.total_bot), 19436.50)
self.assertEqual(PriceParser.display(self.position.total_sld), 19422.00)
self.assertEqual(PriceParser.display(self.position.net_total), -14.50)
self.assertEqual(PriceParser.display(self.position.total_commission), 5.00)
self.assertEqual(PriceParser.display(self.position.net_incl_comm), -19.50)
self.assertEqual(
PriceParser.display(self.position.avg_price, 5), 77.67600
)
self.assertEqual(PriceParser.display(self.position.cost_basis), 0.00)
self.assertEqual(PriceParser.display(self.position.market_value), 0.00)
self.assertEqual(PriceParser.display(self.position.unrealised_pnl), 0.00)
self.assertEqual(PriceParser.display(self.position.realised_pnl), -19.50)