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(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
    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(100000.00)

    # Use Yahoo Daily Price Handler
    start_date = datetime.datetime(2009, 8, 3)
    end_date = datetime.datetime(2016, 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, 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
    title = ["Kalman Filter Pairs Trade on TLT/IEI"]
    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, 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
Exemple #7
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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
Exemple #8
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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
Exemple #10
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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(2014, 11, 11)
    end_date = datetime.datetime(2016, 9, 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, events_queue, 
        lookback, weights, 
        entry_z, exit_z, 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 = ["Aluminium Smelting Strategy - ARNC/UNG"]
    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
Exemple #12
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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, 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