advanced-algorithmic-trading

Chapter 30
Sentiment Analysis via Sentdex
Vendor Sentiment Data with
QSTrader
In addition to the "usual" tricks of statistical arbitrage, trend-following and fundamental analysis,
many quant shops and retail quants engage in natural language processing (NLP) techniques to
build systematic strategies. Such techniques fall under the banner of Sentiment Analysis.
In this chapter a group of quantitative trading strategies will be developed that utilise a set
of sentiment signals generated from a vendor API. These signals provide an integer scale ranging
from -3 ("Strongest negative sentiment") to +6 ("Strongest positive sentiment"), associated with
a date and a ticker symbol, that can be used as entry and exit thresholds in an event-driven
backtesting simulation.
A key challenge in developing such a system is integrating the events representing sentiment,
as stored in a CSV file of "datetime-ticker-sentiment" rows, into an event-driven trading system
that is usually designed to trade directly off pricing data.
The chapter will begin with a brief discussion of how sentiment analysis is carried out, along
with an outline of the nature of vendor APIs and sample files. It will continue by discussing
the sentiment functionality present in QSTrader, including snippets of the associated Python
code. It will conclude by presenting the results of three separate backtests of the sentiment
strategy applied to S&P500 stocks in the tech, defence and energy sectors. The full code for
these strategies is presented at the end of the chapter.
30.1 Sentiment Analysis
The goal of sentiment analysis is, generally, to take large quantities of "unstructured" data (such
as blog posts, newspaper articles, research reports, tweets, video, images etc) and use NLP
techniques to quantify positive or negative "sentiment" about certain assets.
For equities in particular this often amounts to a statistical machine learning analysis of
the language utilised and whether it contains bullish or bearish phrasing. This phrasing can
be quantified in terms of strength of sentiment, which translates into numerical values. Often
this means positive values reflecting bullish sentiment and negative values representing bearish
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