Yes. I traded equity options. The methodology can be summarized as sentiment analysis and "alternative" data gathering.
My algorithm earned about 127% on an initial outlay of $30,000 from August of 2016 to the beginning of January 2018. The algorithm only deployed 5% of available capital (defined risk exposure) at any time and targeted an aggregate win rate of 60% or greater. Its primary imperative was volatility prediction to sell options on equities with overrated volatility. Selling options is a good foundation for a strategy because you can easily make steady returns over time. But one loss can eliminate a year of profit (or your entire outlay); hence the volatility prediction is required to establish a probabilistic win rate above 50%. The goal is to profit on many small positions consistently, not to profit on fewer large positions. Risk is defined to limit total exposure for each trade. There are explicit stop loss and stop profit triggers, and leaving an indeterminate amount of profit "on the table" (selling a position early) is preferable to risking any amount of loss.
Volatility prediction happens in two stages. Stage one is this: first the algorithm seeks all equities with only one or two sources of revenue and a market cap above $1B. Next it crawls news and social media to assess the amount of "hype" attention the equity is receiving. Then it ranks this list according to the amount of hype, weighting social media (uninformed hype) and source of news (informed hype) differently, in ascending order. Lower hype is considered better (and to clarify this point: hype is considered a volatility indicator whether negative or positive). This task is executed daily.
Stage two is alternative data gathering. For each equity going down the list, common sources of financial data are crawled (analyst earnings consensus, prior 10Qs and 10Ks, etc). I receive a notification with a list of which companies are "candidates" for trading, and look into them to identify sources of alternative data. This data is mostly found through web crawling to track signals with a 1:1 indication to a given equity's revenue. Once I have automated the method of collecting the data, it gets incubated for timeseries analysis for at least two quarters. If it forecasts revenue correctly to within 95% accuracy, the equity is formally whitelisted for trading eligibility to the algorithm.
Finally the algorithm begins selling options on each whitelisted equity. On a daily basis a volatility forecast is made for the equity based on weighted social sentiment and the corresponding alternative data timeseries. When the volatility prediction reaches a certain threshold, the algorithm ceases selling options on that equity.