Social Market Analytics, Inc. (SMA) has been the leading provider of predictive quantitative signal in the alternative data space for 7 years. Over the years, we have developed patented algorithms that use machine learning and natural language processing to provide content that generates alpha. Our NLP is unique because of our proprietary processes that tag sentiment weights based on the language used in finance per asset class. The processing of tweets for futures and commodities is different from what we use for equities.
There have been a lot of conversations around Natural Gas futures in social media lately. Through our partner CME Group , people have been monitoring social sentiment from SMA in real time on their active trader site.
Recently, we did as study on using SMA signals to create a Long Short trading strategy using Natural Gas futures. In the example here, we are using front month futures contract and trading daily using a combination of S-Score (a measure of unusual sentiment) and SV-Score (a measure of unusual twitter volume activity).
The strategy buys contracts when the sentiment (S-Score) is positive. We scale up the long position when the sentiment is positive, and the volume of tweets is also significantly high (SV-Score). Conversely, when the sentiment is negative, we sell contracts and go short. We sell more contracts when sentiment is negative with significantly high number of tweets. For this study, we use a maximum of 100 contracts when going long and 100 contracts when taking short positions.
The sentiment strategy performs significantly better than the Natural gas prices, returning 87.42% YTD. We also avoid the volatility in the price and get a Sharpe of 3.53 on the strategy.
A PnL curve of investment of $300,000 in 100,000 contracts on Jan 1, 2018 is shown in the chart below.
The strategy is profitable throughout the period, and the maximum drawdown is only 7.19%, which is significantly better than the 29.72% drawdown in the natural gas prices YTD.
