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 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 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_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):
# 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):
# 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
price_handler = YahooDailyCsvBarPriceHandler(csv_dir, events_queue, tickers)
# Use the KalmanPairsTrading Strategy
strategy = KalmanPairsTradingStrategy(tickers, events_queue)
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)
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(2012, 10, 15)
end_date = datetime.datetime(2016, 2, 2)
price_handler = YahooDailyCsvBarPriceHandler(
csv_dir, events_queue, tickers,
start_date=start_date, end_date=end_date
)
# Use the Sentdex Sentiment trading strategy
sentiment_handler = SentdexSentimentHandler(
config.CSV_DATA_DIR, "sentdex_sample.csv",
events_queue, tickers=tickers,
start_date=start_date, end_date=end_date
)
base_quantity = 2000
sent_buy = 6
sent_sell = -1
strategy = SentdexSentimentStrategy(
tickers, events_queue,
sent_buy, sent_sell, base_quantity
)
strategy = Strategies(strategy, DisplayStrategy())
# 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 = ["Sentiment Sentdex Strategy"]
statistics = TearsheetStatistics(
config, portfolio_handler, title,
benchmark="SPY"
)
# Set up the backtest
backtest = Backtest(
price_handler, strategy,
portfolio_handler, execution_handler,
position_sizer, risk_manager,
statistics, initial_equity,
sentiment_handler=sentiment_handler
)
results = backtest.simulate_trading(testing=testing)
statistics.save(filename)
return results
def run(config, testing, tickers, filename):
# Set up variables needed for backtest
pickle_path = "/path/to/your/model/hmm_model_spy.pkl"
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(2005, 1, 1)
end_date = datetime.datetime(2014, 12, 31)
price_handler = YahooDailyCsvBarPriceHandler(csv_dir,
events_queue,
tickers,
start_date=start_date,
end_date=end_date,
calc_adj_returns=True)
# Use the Moving Average Crossover trading strategy
base_quantity = 10000
strategy = MovingAverageCrossStrategy(tickers,
events_queue,
base_quantity,
short_window=10,
long_window=30)
strategy = Strategies(strategy, DisplayStrategy())
# Use the Naive Position Sizer
# where suggested quantities are followed
position_sizer = NaivePositionSizer()
# Use regime detection HMM risk manager
hmm_model = pickle.load(open(pickle_path, "rb"))
risk_manager = RegimeHMMRiskManager(hmm_model)
# 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 = ["Trend Following Regime Detection with HMM"]
statistics = TearsheetStatistics(config,
portfolio_handler,
title,
benchmark="SPY")
# 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)
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