advanced-algorithmic-trading

456
sentiment_handler = SentdexSentimentHandler(
config.CSV_DATA_DIR, "sentdex_sample.csv",
events_queue, tickers=tickers,
start_date=start_date, end_date=end_date
)
base_quantity = 2000
sent_buy = 6
sent_sell = -1
strategy = SentdexSentimentStrategy(
tickers, events_queue,
sent_buy, sent_sell, base_quantity
)
strategy = Strategies(strategy, DisplayStrategy())
In order to execute this strategy it is necessary to utilise your QSTrader virtual environment,
as always, and type the following into the terminal, where the list of tickers must be adapted to
suit the particular strategy at hand. Make sure to include SPY if a benchmark comparison is
desired.
The following example simulation consists of a selection of S&P500 defence stocks, including
Boeing, General Dynamics, Lockheed Martin, Northrop-Grumman and Raytheon:
$ python sentdex_sentiment_backtest.py --tickers=BA,GD,LMT,NOC,RTN,SPY
The truncated output of the defence stocks example will be as follows:
..
..
---------------------------------
Backtest complete.
Sharpe Ratio: 1.62808089233
Max Drawdown: 0.0977963517677
Max Drawdown Pct: 0.0977963517677
30.5 Strategy Results
30.5.1 Transaction Costs
The strategy results presented here are given net of transaction costs. The costs are simulated
using Interactive Brokers US equities fixed pricing for shares in North America. They are
reasonably representative of what could be achieved in a real trading strategy.
30.5.2 Sentiment on S&P500 Tech Stocks
The base quantity of shares used for each ticker is 2,000.
The tech stocks sentiment analysis strategy posts a CAGR of 21.0% compared to the benchmark
of 9.4%, using 2,000 shares of each of the five tickers. It generates large gains in only
three months, namely May 2013, October 2013 and July 2015. The remainder of the time it