It was an absolute pleasure to work with Guangming. Communication was seamless and he went above and beyond original expectations to provide excellence. You will be hard pressed to find a data scientist with this level of creative problem solving and efficiency. There is no problem too large. From fairly undeveloped ideas, he created a trading strategy yielding greater than 1% daily returns. I look forward to working together again.
The client, a technical trader, hired us to develop trading signals for BTC perpetuals using machine learning. The client was already using two indicators to make trade decisions, and was looking for new signals to add in the mix.
Using tick-by-tick data (open interest, funding rate, last price, mark price, and index price) from tardis.dev, we trained logistic regressions to predict price movement over the next candle period for 12-hour, 6-hour, 4-hour, 2-hour, 1-hour, 30-min, and 15-min periods respectively. We built sub-models to capture local trends as features. After backtesting the models, we deployed them to a digital ocean cloud server, and had them output realtime trading signals to client’s google drive. In addition, we also created ten indicators using order book L2 data from tardis.dev. These indicators measured the average and extreme buying and selling pressures for each candle period. They were very telling when correlated with price.
We used Python for historical data downloading and realtime data streaming via tardis API. We wrote shell scripts for data cleaning and size reduction. We used R for data processing and visualization, feature engineering and selection, statistical analysis, and model training and backtesting.