Is there an out of the box methods for statistical arbitrage to odds distributions….Lets say I want to see what is a higher probability 7 cross 21 or 8 cross 22, 21 cross 50 etc……I know how to build this out manually and test it per method but I am curious if there is a more machine learning method. I suppose I could create range base iteration methods and build maybe 7:12 as my increment rather than building it as a 7 or a 12. I understand the manual implementation but am wondering if there is a library or function thats more out of the box. I know there is a lot of stuff from the output after the computation like tables and such…that break it up for you after the computation is built but I am wondering more about the build of the computation in a variance. I know my hedge fund buddies will study stocks this way relative to a buy and hold strategy to see if a hold of 3 months is greater probability at x% then say a year or a week…is this something you would just have to build out manually or is there a quicker approach???
Hi Philip,
When comparing the probabilities or returns of different strategies, like moving average crossovers, automation can really help make the process more efficient and scalable. Instead of manually testing every combination, you can use parameter iteration frameworks to systematically explore ranges—say, short MAs of 7–12 days versus long MAs of 21–50 days. Modules like itertools can handle the heavy lifting for generating combinations, and libraries like scipy or statsmodels can help analyse results with statistical tests (e.g., t-tests) and confidence intervals.
Once you’ve narrowed down promising combinations, you can backtest them on historical data to understand their performance. Ideally, you’d optimize parameters using a test dataset and reserve a separate dataset for backtesting, but simulations like Monte Carlo can also be a powerful way to evaluate strategies without needing to build everything from scratch. For deeper insights, machine learning tools like scikit-learn or xgboost can identify patterns in historical data, framing the problem as a prediction task. These approaches not only save time but also allow for a systematic exploration of strategies, similar to how hedge funds analyse performance across different holding periods or parameters.