advanced-algorithmic-trading

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’--testing/--no-testing’, default=False,
help=’Enable testing mode’
)
@click.option(’--tickers’, default=’SP500TR’, help=’Tickers (use comma)’)
@click.option(
’--filename’, default=’’,
help=’Pickle (.pkl) statistics filename’
)
def main(config, testing, tickers, filename):
tickers = tickers.split(",")
config = settings.from_file(config, testing)
run(config, testing, tickers, filename)
if __name__ == "__main__":
main()
As long as QSTrader is correctly installed and the data has been downloaded from Yahoo
Finance the code can be executed via the following command in the terminal:
$ python kalman_qstrader_backtest.py --tickers=TLT,IEI
Thanks to the efforts of many volunteer developers, particularly @ryankennedyio, @femtotrader
and @nwillemse, the code is well-optimised for OHLCV bar data and carries out the
backtesting rapidly.
28.3 Strategy Results
The equity curve is presented in Figure 28.1. It begins relatively flat for the first year of the
strategy but rapidly escalates during the latter half of 2010 and 2011. During 2012 the strategy
becomes significantly more volatile remaining "underwater" until 2015 and reaching a maximum
daily drawdown percentage of 17.46%. The performance gradually increases from the maximum
drawdown in late 2013 through to 2016.
The strategy has a CAGR of 7.66% with a Sharpe Ratio of 0.65. It also has a long maximum
drawdown duration of 817 days–well over two years! Note that this strategy is carried out net
of transaction costs so is reasonably reflective of real-world performance, under the assumption
of achieving closing prices of assets.
28.4 Next Steps
There is a lot of research work necessary to turn this into a profitable strategy that we would
deploy in a live setting. Potential avenues of research include:
• Parameter Optimisation - Varying the parameters of the Kalman Filter via crossvalidation
grid search or some form of machine learning optimisation. However, this introduces
the distinct possibility of overfitting to historical data.