def setUp(self): """ Set up the PortfolioHandler object supplying it with $500,000.00 USD in initial cash. """ initial_cash = Decimal("500000.00") events_queue = queue.Queue() price_handler = PriceHandlerMock() position_sizer = PositionSizerMock() risk_manager = RiskManagerMock() # Create the PortfolioHandler object from the rest self.portfolio_handler = PortfolioHandler(initial_cash, events_queue, price_handler, position_sizer, risk_manager)
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 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(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 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 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_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 setUp(self): """ Set up the PortfolioHandler object supplying it with $500,000.00 USD in initial cash. """ initial_cash = Decimal("500000.00") events_queue = queue.Queue() price_handler = PriceHandlerMock() position_sizer = PositionSizerMock() risk_manager = RiskManagerMock() # Create the PortfolioHandler object from the rest self.portfolio_handler = PortfolioHandler( initial_cash, events_queue, price_handler, position_sizer, risk_manager )
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 run(config, testing, tickers, cliente, strat_set): events_queue = queue.Queue() initial_equity = 30000.0 end_session_time = datetime(2018, 2, 23, 16, 1, 00) if end_session_time < datetime.now(): print("########################################") print(" Yoe, Increase \"end_section time\" please") print("########################################") exit() start_date = None end_date = None # # Defining up to 3 contracts in case Mean Reversing Strategies were used in the future # contract_1 = Contract() contract_1.symbol = tickers[0] contract_1.secType = "STK" contract_1.currency = "USD" contract_1.exchange = "SMART" contract_1.primaryExchange = "ISLAND" contract_2 = Contract() if len(tickers) > 1: contract_2.symbol = tickers[1] contract_2.secType = "STK" contract_2.currency = "USD" contract_2.exchange = "SMART" contract_2.primaryExchange = "ISLAND" contract_3 = Contract() if len(tickers) > 2: contract_3.symbol = tickers[2] contract_3.secType = "STK" contract_3.currency = "USD" contract_3.exchange = "SMART" contract_3.primaryExchange = "ISLAND" contract = [contract_1, contract_2, contract_3] # # Defining the Conection to TWS ###################################################### # current_directory = os.getcwd() app = conexion(tickers=tickers, port=7497, cliente=cliente, currentDir=current_directory) # devuelto en: currentTime # # Checking open positions on the market ############################################ # app.reqAccountUpdates(True, "DU931045") tiempo = datetime.now() continua = False while not continua: open_position = app.open_position if open_position[0] != None or datetime.now() > tiempo + timedelta( seconds=3): continua = True time.sleep(0.5) if open_position[0] == tickers[0]: print(" *******************************************************") print("") print(" There is an open possition for %s, please check in the " % tickers[0]) print(" market whether it is convenient to proced automatically") print("") print(" *******************************************************") app.reqAccountUpdates(False, "DU931045") # # Requesting real time data from the Market ########################################## # reqId = [None for i in range(len(tickers))] for i in range(len(tickers)): reqId[i] = app.nuevoId() app.reqMarketDataType(1) app.reqMktData(reqId[i], contract[i], genericTickList='', snapshot=False, regulatorySnapshot=False, mktDataOptions=[]) print("request Id: ", reqId[i]) # # Defining modules ################################################################### # price_handler = IBAPI_yoe(events_queue, init_tickers=tickers, app=app, reqId=reqId) # start_date = datetime.datetime(2015, 1, 1) # end_date = datetime.datetime(2016, 9, 30) # price_handler = IQFeedIntradayCsvBarPriceHandler( # csv_dir, events_queue, tickers, start_date=start_date # ) contract_dict = { contract_1.symbol: contract_1, contract_2.symbol: contract_2, contract_3.symbol: contract_3 } LiveCompl = LiveCompliance(config, tickers) # Use the ML Intraday Prediction Strategy model_pickle_file = current_directory + "/" + tickers[0] + "_Full_time.pkl" strategy = IntradayMachineLearningPredictionStrategy( tickers, events_queue, model_pickle_file, strat_set, open_position) execution_handler = IB_execution_yoe(events_queue, price_handler, compliance=LiveCompl, app=app, contract_dict=contract_dict, currentDir=current_directory, Id=reqId, strategy=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(PriceParser.parse(initial_equity), events_queue, price_handler, position_sizer, risk_manager) title = [tickers[0] + "Machine Learning_Long_lags3"] # 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, session_type="live", end_session_time=end_session_time, price_handler=price_handler, portfolio_handler=portfolio_handler, compliance=None, position_sizer=position_sizer, execution_handler=execution_handler, risk_manager=None, statistics=statistics, sentiment_handler=None, title=None, benchmark=None) results = backtest.start_trading(testing=testing) return results for i in range(len(tickers)): app.cancelMktData(i) app.disconnect() app.f.close()
def run(config, testing, tickers, filename): #build_HMM_model() build_HMM_model2() title = ["Trend Following Regime Detection without HMM"] #pickle_path = "hmm_model_spy.pkl" pickle_path = "hmm_model_spy2.pkl" #pickle_path = "hmm_model_fx.pkl" events_queue = queue.Queue() csv_dir = config.CSV_DATA_DIR initial_equity = 100000.00 start_date = datetime.datetime(2011, 1, 1) end_date = datetime.datetime(2018, 3, 31) # Use the Moving Average Crossover trading strategy base_quantity = 10000 strategy = MovingAverageCrossStrategy(tickers, events_queue, base_quantity, short_window=10, long_window=30) # Use Yahoo Daily Price Handler price_handler = YahooDailyCsvBarPriceHandler(csv_dir, events_queue, tickers, start_date=start_date, end_date=end_date, calc_adj_returns=True) # Use the Naive Position Sizer # where suggested quantities are followed position_sizer = NaivePositionSizer() # Use an example Risk Manager #risk_manager = ExampleRiskManager() # Use regime detection HMM risk manager hmm_model = pickle.load(open(pickle_path, "rb")) risk_manager = RegimeHMMRiskManager(hmm_model) # Use the default Portfolio Handler portfolio_handler = PortfolioHandler(PriceParser.parse(initial_equity), events_queue, price_handler, position_sizer, risk_manager) # Use the Tearsheet Statistics class statistics = TearsheetStatistics( config, portfolio_handler, #title,benchmark = "EEM" title, benchmark="VWO") # Set up the backtest backtest = TradingSession(config, strategy, tickers, initial_equity, start_date, end_date, events_queue, title=title, price_handler=price_handler, position_sizer=position_sizer, risk_manager=risk_manager, statistics=statistics, portfolio_handler=portfolio_handler) results = backtest.start_trading(testing=testing) 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) 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 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
class TestSimpleSignalOrderFillCycleForPortfolioHandler(unittest.TestCase): """ Tests a simple Signal, Order and Fill cycle for the PortfolioHandler. This is, in effect, a sanity check. """ def setUp(self): """ Set up the PortfolioHandler object supplying it with $500,000.00 USD in initial cash. """ initial_cash = Decimal("500000.00") events_queue = queue.Queue() price_handler = PriceHandlerMock() position_sizer = PositionSizerMock() risk_manager = RiskManagerMock() # Create the PortfolioHandler object from the rest self.portfolio_handler = PortfolioHandler( initial_cash, events_queue, price_handler, position_sizer, risk_manager ) def test_create_order_from_signal_basic_check(self): """ Tests the "_create_order_from_signal" method as a basic sanity check. """ signal_event = SignalEvent("MSFT", "BOT") order = self.portfolio_handler._create_order_from_signal(signal_event) self.assertEqual(order.ticker, "MSFT") self.assertEqual(order.action, "BOT") self.assertEqual(order.quantity, 0) def test_place_orders_onto_queue_basic_check(self): """ Tests the "_place_orders_onto_queue" method as a basic sanity check. """ order = OrderEvent("MSFT", "BOT", 100) order_list = [order] self.portfolio_handler._place_orders_onto_queue(order_list) ret_order = self.portfolio_handler.events_queue.get() self.assertEqual(ret_order.ticker, "MSFT") self.assertEqual(ret_order.action, "BOT") self.assertEqual(ret_order.quantity, 100) def test_convert_fill_to_portfolio_update_basic_check(self): """ Tests the "_convert_fill_to_portfolio_update" method as a basic sanity check. """ fill_event_buy = FillEvent( datetime.datetime.utcnow(), "MSFT", "BOT", 100, "ARCA", Decimal("50.25"), Decimal("1.00") ) self.portfolio_handler._convert_fill_to_portfolio_update(fill_event_buy) # Check the Portfolio values within the PortfolioHandler port = self.portfolio_handler.portfolio self.assertEqual(port.cur_cash, Decimal("494974.00")) # TODO: Finish this off and check it works via Interactive Brokers fill_event_sell = FillEvent( datetime.datetime.utcnow(), "MSFT", "SLD", 100, "ARCA", Decimal("50.25"), Decimal("1.00") ) self.portfolio_handler._convert_fill_to_portfolio_update(fill_event_sell) def test_on_signal_basic_check(self): """ Tests the "on_signal" method as a basic sanity check. """ signal_event = SignalEvent("MSFT", "BOT") self.portfolio_handler.on_signal(signal_event) ret_order = self.portfolio_handler.events_queue.get() self.assertEqual(ret_order.ticker, "MSFT") self.assertEqual(ret_order.action, "BOT") self.assertEqual(ret_order.quantity, 100)
def run(config, testing, tickers, csv_filepath, pklfile, start_date, end_date, lags, title, folder_name, model, return_win, percent_factor, salida): # Set up variables needed for backtest # title = [ # "Intraday AREX Machine Learning Prediction Strategy" # ] events_queue = queue.Queue() csv_dir = csv_filepath initial_equity = 30000.0 # Use DTN IQFeed Intraday Bar Price Handler # start_date = datetime.datetime(2016, 1, 1) # end_date = datetime.datetime(2014, 3, 11) # start_date = datetime.datetime(2013, 1, 1) # end_date = datetime.datetime(2014, 3, 11) price_handler = IQFeedIntradayCsvBarPriceHandler(csv_dir, events_queue, tickers, start_date, end_date) # Use the ML Intraday Prediction Strategy model_pickle_file = pklfile strategy = IntradayMachineLearningPredictionStrategy( tickers, events_queue, model_pickle_file, lags, model, return_win, percent_factor, salida) # 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 compliance = LiveCompliance(config, tickers) execution_handler = IBSimulatedExecutionHandler( events_queue=events_queue, price_handler=price_handler, compliance=compliance) backtest = TradingSession(config, strategy, tickers, initial_equity, start_date, end_date, events_queue, title=title, portfolio_handler=portfolio_handler, compliance=compliance, position_sizer=position_sizer, execution_handler=execution_handler, price_handler=price_handler, statistics=statistics) results = backtest.start_trading(testing=testing, folder_name=folder_name) return results
class TestSimpleSignalOrderFillCycleForPortfolioHandler(unittest.TestCase): """ Tests a simple Signal, Order and Fill cycle for the PortfolioHandler. This is, in effect, a sanity check. """ def setUp(self): """ Set up the PortfolioHandler object supplying it with $500,000.00 USD in initial cash. """ initial_cash = Decimal("500000.00") events_queue = queue.Queue() price_handler = PriceHandlerMock() position_sizer = PositionSizerMock() risk_manager = RiskManagerMock() # Create the PortfolioHandler object from the rest self.portfolio_handler = PortfolioHandler(initial_cash, events_queue, price_handler, position_sizer, risk_manager) def test_create_order_from_signal_basic_check(self): """ Tests the "_create_order_from_signal" method as a basic sanity check. """ signal_event = SignalEvent("MSFT", "BOT") order = self.portfolio_handler._create_order_from_signal(signal_event) self.assertEqual(order.ticker, "MSFT") self.assertEqual(order.action, "BOT") self.assertEqual(order.quantity, 0) def test_place_orders_onto_queue_basic_check(self): """ Tests the "_place_orders_onto_queue" method as a basic sanity check. """ order = MarketOrderEvent("MSFT", "BOT", 100) order_list = [order] self.portfolio_handler._place_orders_onto_queue(order_list) ret_order = self.portfolio_handler.events_queue.get() self.assertEqual(ret_order.ticker, "MSFT") self.assertEqual(ret_order.action, "BOT") self.assertEqual(ret_order.quantity, 100) def test_convert_fill_to_portfolio_update_basic_check(self): """ Tests the "_convert_fill_to_portfolio_update" method as a basic sanity check. """ fill_event_buy = FillEvent(datetime.datetime.utcnow(), "MSFT", "BOT", 100, "ARCA", Decimal("50.25"), Decimal("1.00")) self.portfolio_handler._convert_fill_to_portfolio_update( fill_event_buy) # Check the Portfolio values within the PortfolioHandler port = self.portfolio_handler.portfolio self.assertEqual(port.cur_cash, Decimal("494974.00")) # TODO: Finish this off and check it works via Interactive Brokers fill_event_sell = FillEvent(datetime.datetime.utcnow(), "MSFT", "SLD", 100, "ARCA", Decimal("50.25"), Decimal("1.00")) self.portfolio_handler._convert_fill_to_portfolio_update( fill_event_sell) def test_on_signal_basic_check(self): """ Tests the "on_signal" method as a basic sanity check. """ signal_event = SignalEvent("MSFT", "BOT") self.portfolio_handler.on_signal(signal_event) ret_order = self.portfolio_handler.events_queue.get() self.assertEqual(ret_order.ticker, "MSFT") self.assertEqual(ret_order.action, "BOT") self.assertEqual(ret_order.quantity, 100)