def run_monthly_rebalance(tickers, ticker_weights, title, start_date, end_date,
initial_equity):
testing = False
config = settings.from_file(settings.DEFAULT_CONFIG_FILENAME, testing)
# Use the Monthly Liquidate And Rebalance strategy
events_queue = queue.Queue()
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
position_sizer=position_sizer,
title=[title],
benchmark=tickers[0],
)
results = backtest.start_trading(testing=testing)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Monthly Liquidate/Rebalance on 60%/40% SPY/AGG Portfolio']
initial_equity = 500000.0
start_date = datetime.datetime(2006, 11, 1)
end_date = datetime.datetime(2016, 10, 12)
# Use the Monthly Liquidate And Rebalance strategy
events_queue = queue.Queue()
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
ticker_weights = {
"SPY": 0.6,
"AGG": 0.4,
}
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
position_sizer=position_sizer,
title=title,
benchmark=tickers[0],
)
results = backtest.start_trading(testing=testing)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Moving Average Crossover Example on %s: 100x300' % filename]
initial_equity = 10000.0
start_date = datetime.datetime(2013, 1, 1)
end_date = datetime.datetime(2018, 1, 1)
cycle = 'D'
# Use the MAC Strategy
events_queue = queue.Queue()
strategy = MovingAverageCrossStrategy(tickers[0],
events_queue,
short_window=1,
long_window=20)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
price_handler='tushare',
title=title,
benchmark=tickers[1],
cycle=cycle)
results = backtest.start_trading(testing=testing, filename=filename)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ["Kalman Filter Pairs Trade on %s/%s" % (tickers[0], tickers[1])]
initial_equity = 100000.0
start_date = datetime.datetime(2017, 1, 1)
end_date = datetime.datetime(2018, 8, 19)
# Use the KalmanPairsTrading Strategy
events_queue = queue.Queue()
strategy = KalmanPairsTradingStrategy(tickers, events_queue)
# Use the Naive Position Sizer where
# suggested quantities are followed
position_sizer = NaivePositionSizer()
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
position_sizer=position_sizer)
results = backtest.start_trading(testing=testing)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Aluminium Smelting Strategy - ARNC/UNG']
initial_equity = 500000.0
start_date = datetime.datetime(2014, 11, 11)
end_date = datetime.datetime(2016, 9, 1)
# Use the Cointegration Bollinger Bands trading strategy
events_queue = queue.Queue()
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)
# Set the position size to use a
# fixed base quantity of shares
position_sizer = FixedPositionSizer(default_quantity=base_quantity)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
position_sizer=position_sizer)
results = backtest.start_trading(testing=testing)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Moving Average Crossover Example']
initial_equity = 10000.0
##start_date = datetime.datetime(2011, 1, 1)
##end_date = datetime.datetime(2020, 1, 1)
start_date = datetime.datetime(2015, 1, 1)
end_date = datetime.datetime(2020, 1, 1)
# Use the MAC Strategy
events_queue = queue.Queue()
strategy = MovingAverageCrossStrategy(tickers[0],
events_queue,
short_window=50,
long_window=100)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
session_type="backtest",
name="strategy1",
#benchmark=tickers[1],
title=title)
results = backtest.start_trading(testing=testing)
#print(type(backtest))
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Reblance Example']
initial_equity = 10000.0
##start_date = datetime.datetime(2011, 1, 1)
##end_date = datetime.datetime(2020, 1, 1)
start_date = datetime.datetime(2015, 1, 1)
end_date = datetime.datetime(2020, 1, 1)
# Use the MAC Strategy
events_queue = queue.Queue()
strategy = MonthRebalanceStrategy(tickers, events_queue)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
session_type="backtest",
name="month_reb",
title=title,
benchmark=None)
results = backtest.start_trading(testing=testing)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Buy and Hold Example on %s' % tickers[0]]
initial_equity = 10000.0
start_date = datetime.datetime(2005, 1, 1)
end_date = datetime.datetime(2018, 8, 17)
# Use the Buy and Hold Strategy
events_queue = queue.Queue()
strategy = BuyAndHoldStrategy(tickers[0], events_queue)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
price_handler='tushare',
title=title,
benchmark=tickers[1])
results = backtest.start_trading(testing=testing, filename=filename)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Moving Average Crossover Example on AAPL: 100x300']
initial_equity = 10000.0
start_date = datetime.datetime(2000, 1, 1)
end_date = datetime.datetime(2014, 1, 1)
# Use the MAC Strategy
events_queue = queue.Queue()
strategy = MovingAverageCrossStrategy(tickers[0],
events_queue,
short_window=100,
long_window=300)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
benchmark=tickers[1],
)
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(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):
# 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)
# 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(config, testing, tickers, filename):
# Backtest information
events_queue = queue.Queue()
title = [
'Sentiment Sentdex Strategy - Tech Stocks'
#'Sentiment Sentdex Strategy - Defence Stocks'
#'Sentiment Sentdex Strategy - Energy Stocks'
]
initial_equity = 500000.0
start_date = datetime.datetime(2012, 10, 15)
end_date = datetime.datetime(2016, 2, 2)
# 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)
# Use the Sentdex Sentiment trading strategy
base_quantity = 500
sent_buy = 6
sent_sell = -1
strategy = SentdexSentimentStrategy(tickers, events_queue, sent_buy,
sent_sell, base_quantity)
# Use the Fixed Position Sizer where
# suggested quantities are followed
position_sizer = FixedPositionSizer(default_quantity=base_quantity)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
position_sizer=position_sizer,
sentiment_handler=sentiment_handler,
benchmark="SPY")
results = backtest.start_trading(testing=testing)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Monthly Liquidate/Rebalance on 60%/40% SPY/AGG Portfolio']
initial_equity = 500000.0
start_date = datetime.datetime(2006, 11, 1)
end_date = datetime.datetime(2016, 10, 12)
# Use the Monthly Liquidate And Rebalance strategy
events_queue = queue.Queue()
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
ticker_weights = {
"ewg.us": 0.4,
"ewq.us": 0.6,
}
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
position_sizer=position_sizer,
price_handler=DailyCsvBarPriceHandler('~/Desktop/stock-data/ETFs',
ext_data='txt',
init_tickers=tickers,
events_queue=events_queue,
start_date=start_date,
end_date=end_date),
title=title,
benchmark=tickers[0],
)
results = backtest.start_trading(testing=testing)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = [
'Monthly Liquidate/Rebalance on 40%/30% 30% sz50/zz500/cyb Portfolio'
]
initial_equity = 300000.0
start_date = datetime.datetime(2010, 11, 1)
end_date = datetime.datetime(2018, 8, 18)
# Use the Monthly Liquidate And Rebalance strategy
events_queue = queue.Queue()
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
ticker_weights = {
"510050": 0.4,
"510500": 0.3,
"159915": 0.3,
}
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
price_handler='tushare',
position_sizer=position_sizer,
title=title,
benchmark=tickers[0],
)
results = backtest.start_trading(testing=testing)
return results
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, 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, 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
