This not only prolongs your lifecycle of designing strategies, but is dangerous after all: limited number of tests is similar to a tunnel vision - it prevents you from seeing the bigger picture and makes you dive into the market blindly.Īfter trying tweaking pandas, multiprocessing, and even evaluating my strategies on a cluster with Spark, I finally found myself using Numba - a Python library that can compile slow Python code to be run at native machine code speed. ![]() ![]() ![]() Questions like "Which strategy is better: X or Y?" require fast computation and transformation of data. Most open-source backtesting libraries are very evolved in terms of functionality, but simply lack speed. I want to share with you a tool that I was continuously developing during the last couple of months.Īs a data scientist, when I first started flirting with quant trading, I quickly realized that there is a shortage of Python packages that can actually enable me to iterate over a long list of possible strategies and hyper-parameters quickly.
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