CloudQuant Blog

In our study of Environmental, Social, and Governance (ESG) data, we looked at holding positions based upon the G&S Quotient short term price predictor score. We found that 5-day holds and 20-day holds using this score were very interesting and produced positive returns. Based on this data set we saw that you could have traded and held some of these stocks and closed your positions at the end of the day last Friday. See the charts for $ADBE, $AAPL, $MSFT, and $LDOS

Some traders are visually oriented. They need charts. As data scientists, we need to be able to present information in a way that others can understand. Presenting traders a candlestick chart is one of the best ways to transfer useful data. Blog Purpose: ✅ Demonstrate how to create a basic candlestick chart in Python 3 ✅ Demonstrate how to highlight/annotate points on the chart Topics covered in this post: Python, Plotly, OHLC, Candlestick Charts, Jupyter, Pandas, Traders

A few of our power users reported that long-running backtests would sometimes run out of memory. These power-users are the people who often find new trading strategies and so we wanted to work with them to improve the performance of our backtesting tools. Over the past couple of weeks, our senior engineer found that the problem wasn't in our code, but in one of the popular Python libraries that we use. We found the problem in numpy and numba. 

Jeff Holden develops algo trading models at SMB Capital in New York. He utilizes CloudQuant extensively during his research and development process. In this video he details his process for developing an idea into a model.

What effect can alternative data sets have on trading algorithms? We asked a few of our teammates and systematic traders what the effect of alternative data sets is on trading algos. We thought we could spread some insight as to why our alternative…