advanced-algorithmic-trading

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threshold, then it closes the position.
Hence the strategy presented below only goes long. It is a straightforward matter to extend
this to short trading. Example code for shorting has been presented in other trading strategies
to date. In particular the code for the Kalman Filter Pairs Trade provides this capability.
def calculate_signals(self, event):
"""
Calculate the signals for the strategy.
"""
if event.type == EventType.SENTIMENT:
ticker = event.ticker
# Long signal
if (
self.invested[ticker] is False and
event.sentiment >= self.sent_buy
):
print("LONG %s at %s" % (ticker, event.timestamp))
self.events_queue.put(SignalEvent(ticker, "BOT", self.qty))
self.invested[ticker] = True
# Close signal
if (
self.invested[ticker] is True and
event.sentiment <= self.sent_sell
):
print("CLOSING LONG %s at %s" % (ticker, event.timestamp))
self.events_queue.put(SignalEvent(ticker, "SLD", self.qty))
self.invested[ticker] = False
As with all QSTrader implemented strategies, there is a corresponding backtest file that
specifies the parameters of the strategy. It is very similar to the other backtest files found within
the book. Hence the full listing is only presented at the end of this chapter.
The main differences are the instantiation of the SentimentHandler object and setting of
the parameters for the entry and exit thresholds. These are set to 6 for entry and -1 for exit, as
reflected in the underlying strategy rules above. It is instructive (and potentially more profitable!)
to optimise these values for various sets of tickers.
The sentdex_sample.csv is placed in the QSTrader CSV_DATA_DIR, which is where the
pricing data also usually resides. The start and end dates reflect the duration over which the
Sentdex sample file contains sentiment predictions.
..
..
start_date = datetime.datetime(2012, 10, 15)
end_date = datetime.datetime(2016, 2, 2)
..
..
# Use the Sentdex Sentiment trading strategy