advanced-algorithmic-trading

429
SignalEvent(self.tickers[0], "BOT", self.qty)
)
self.invested = True
if self.invested and pred == -1:
print("CLOSING LONG: %s" % event.time)
self.events_queue.put(
SignalEvent(self.tickers[0], "SLD", self.qty)
)
self.invested = False
Unfortunately for more complex models the model.predict call can be extremely slow. For
simpler models with minimal parameters (e.g. linear regression) the call is fast. On the desktop
workstation used here to generate the results it was possible to obtain approximately 5,000
bars/second for simple models.
For more complex models such as Random Forest classifiers with many deeply grown trees
this prediction speed can drop substantially, often by up to three orders of magnitude. For the
Random Forest ensemble in particular the initial model generated in this example produced a
4.1Gb pickle file, which is beyond the RAM capacity of some earlier PCs. Thus the method
has no choice but to swap to disk when predicting new data points, which can heavily reduce
performance.
One of the goals with optimising a model is to make sure that it is possible to actually
generate predictions in a reasonable time frame. In the case of the Random Forest classifier, it
was necessary to experiment with the maximum depth of the tree in order to reduce the final
pickle file size but still achieve reasonable prediction performance. The final model is a tradeoff
between prediction accuracy and backtest execution speed, which ran at approximately 60
bars/second in the example below.
29.4 QSTrader Backtest Script
Now that the Strategy class has been developed it is necessary to wrap it in an intraday backtest.
This code is given below in intraday_ml_backtest.py. It is very similar to other QSTrader
implementations.
The main differences are that a new price handler IQFeedIntradayCsvBarPriceHandler
is now imported instead of one designed for Yahoo Finance daily data, as well as the periods
parameter for the TearsheetStatistics class, which has been set to 252 × 6.5 × 60 = 98280.
This ensures that the calculated Sharpe Ratio is correct for the number of periods being used:
# intraday_ml_backtest.py
import click
import datetime
from qstrader import settings
from qstrader.compat import queue
from qstrader.price_parser import PriceParser
from qstrader.price_handler.iq_feed_intraday_csv_bar import \