advanced-algorithmic-trading

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tuples, it is useful to create a unified interface.
To handle the Sentdex sample CSV file a SentdexSentimentHandler object has been written
into QSTrader. As with most handlers it requires a handle to the events queue, a subset of tickers
to act upon as well as a starting and ending date:
class SentdexSentimentHandler(AbstractSentimentHandler):
"""
SentdexSentimentHandler is designed to provide a backtesting
sentiment analysis handler for the Sentdex sentiment analysis
provider (http://sentdex.com/financial-analysis/).
It uses a CSV file with date-ticker-sentiment tuples/rows.
Hence in order to avoid implicit lookahead bias a specific
method is provided "stream_sentiment_events_on_date" that only
allows sentiment signals to be retrieved for a particular date.
"""
def __init__(
self, csv_dir, filename,
events_queue, tickers=None,
start_date=None, end_date=None
):
self.csv_dir = csv_dir
self.filename = filename
self.events_queue = events_queue
self.tickers = tickers
self.start_date = start_date
self.end_date = end_date
self.sent_df = self._open_sentiment_csv()
There are two methods associated with this class. The first is _open_sentiment_csv. It
wraps the opening of a CSV into a Pandas DataFrame along with associated ticker and date
filtering:
def _open_sentiment_csv(self):
"""
Opens the CSV file containing the sentiment analysis
information for all represented stocks and places
it into a pandas DataFrame.
"""
sentiment_path = os.path.join(self.csv_dir, self.filename)
sent_df = pd.read_csv(
sentiment_path, parse_dates=True,
header=0, index_col=0,
names=("Date", "Ticker", "Sentiment")
)
if self.start_date is not None:
sent_df = sent_df[self.start_date.strftime("%Y-%m-%d"):]