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_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 setUp(self):
self.test_sell_event = OrderEvent("CS.D.AUDUSD.TODAY.IP", "SELL", 1)
self.test_buy_event = OrderEvent("CS.D.AUDUSD.TODAY.IP", "BOT", 1)
self.events_queue = queue.Queue()
self.config = settings.from_file(settings.DEFAULT_CONFIG_FILENAME,
testing=True)
self.igclient = IGClient(self.config)
def main(max_rows, cache_name, cache_backend, expire_after, data_source, start, end, config, testing, tickers, filename, n, n_window):
pd.set_option('max_rows', max_rows)
if cache_name == '':
cache_name = "requests_cache"
else:
if cache_backend.lower() == 'sqlite':
cache_name = os.path.expanduser(cache_name)
tickers = tickers.split(",")
config = settings.from_file(config, testing)
run(cache_name, cache_backend, expire_after, data_source, start, end, config, testing, tickers, filename, n, n_window)
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 main(config, testing, tickers, filename, lookback, entry_z, exit_z):
df = pd.DataFrame()
config = settings.from_file(config, testing)
tickers = tickers.split(",")
lookback = np.linspace(lookback[0], lookback[1],
lookback[1] - lookback[0] + 1)
entry_range = np.linspace(entry_z[0] - entry_z[1], entry_z[0] + entry_z[1],
entry_z[2])
exit_range = np.linspace(exit_z[0] - exit_z[1], exit_z[0] + exit_z[1],
exit_z[2])
print(lookback)
print(entry_range)
print(exit_range)
range_list = []
entry_list = []
exit_list = []
sharpe_list = []
alpha_list = []
#alter parameters for rolling mean and std
for i in lookback:
#alter parameters for z-score
for j in entry_range:
for k in exit_range:
trial = run(config, testing, tickers, filename, i, j, k)
range_list.append(i)
entry_list.append(j)
exit_list.append(k)
sharpe_list.append(trial.get('sharpe'))
alpha_list.append(trial.get('alpha'))
data = [
list(i) for i in zip(range_list, entry_list, exit_list, sharpe_list,
alpha_list)
]
df = pd.DataFrame(data,
columns=['Range', 'Entry', 'Exit', 'Sharpe', 'Alpha'])
df.to_csv("Nice.csv")
def main():
config = settings.DEFAULT_CONFIG_FILENAME
testing = False
tickers = [
'43350', '82651', '44644', '90458', '24969', '24985', '42585', '83621',
'60186', '81095', '16548', '81577'
]
# tickers = [,]
# tickers = []
# pair = pair_list[1]
# print(pair)
# tickers = pair.split(",")
# tickers = ['43350', '82651']
# tickers = ['44644', '90458']
# tickers = ['24969', '24985']
# tickers = ['42585', '83621']
# tickers = ['60186', '81095']
# tickers = ['16548', '81577']
filename = 'vs'.join(tickers)
config = settings.from_file(config, testing)
run(config, testing, tickers, filename)
def main(config, testing, tickers, filename):
tickers = tickers.split(",")
config = settings.from_file(config, testing)
run(config, testing, tickers, filename)
start_date,
end_date,
events_queue,
title=title,
benchmark=tickers[1],
)
results = backtest.start_trading(testing=testing)
return results
if __name__ == "__main__":
# Configuration data
testing = False
tickers = ["AAPL", "SPY"]
filename = None
config = settings.from_file(settings.DEFAULT_CONFIG_FILENAME, testing)
run(config, testing, tickers, filename)
# ph = QuandlPriceHandler([], ['AAPL', 'GS'], 'WIKI', '1960-1-1', '2017-6-14')
# # Set up the backtest
# 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)
# tickers = ["AAPL", "SPY"]
# Use the MAC Strategy
# events_queue = queue.Queue()
# strategy = MovingAverageCrossStrategy(
# tickers[0], events_queue,
# short_window=100,
def main(config, testing, tickers, filename, n, n_window):
tickers = tickers.split(",")
config = settings.from_file(config, testing)
run(config, testing, tickers, filename, n, n_window)
def main(config, testing, tickers):
tickers = tickers.split(",")
config = settings.from_file(config, testing)
run(config, testing, tickers)
def main(config, testing, tickers, filename, start_date=None, end_date=None):
tickers = tickers.split(",")
config = settings.from_file(config, testing)
run(config, testing, tickers, filename, start_date, end_date)
def main(config, testing, tickers, filename):
tickers = ['IEI','TLT']#tickers.split(",")
config = settings.from_file(config, testing)
run(config, testing, tickers, filename)