By Sergei Belov, Ernest Chan, Nahid Jetha, and Akshay Nautiyal ABSTRACT We applied Corrective AI (Chan, 2022) to a trading model that takes advantage of the intraday seasonality of forex returns. Breedon and Ranaldo (2012) observed that foreign currencies depreciate vs. the US dollar during their local working hours and appreciate during the local working hours of the US dollar. We first backtested the results of Breedon and Ranaldo on recent EURUSD data from September 2021 to January 2023 and then applied Corrective AI to this trading strategy to achieve a significant increase in performance. Breedon and Ranaldo (2012) described a trading strategy that shorted EURUSD during European working hours (3 AM ET to 9 AM ET, where ET denotes the local time in New York, accounting for daylight savings) and bought EURUSD during US working hours (11 AM ET to 3 PM ET). The rationale is that large-scale institutional buying of the US dollar takes place during European working hours to pa
By Quentin Viville, Sudarshan Sawal, and Ernest Chan PredictNow.ai is excited to announce that we’re expanding our feature zoo to cover crypto features! This follows our work on US stock features, and features based on options activities, ETFs, futures, and macroeconomic indicators. To read more on our previous work, click here . These new crypto features can be used as input to our machine-learning API to help improve your trading strategy. In this blog we have outlined the new crypto features as well as demonstrated how we have used them for short term alpha generation and crypto portfolio optimization. Our new crypto features are designed to capture market activity from subtle movements to large overarching trends. These features will quantify the variations of the price, the return, the order flow, the volatility and the correlations that appear among them. To create these features, we first constructed the Base Features using raw market data that includes microstructure inform