I ran a Python paper trading bot for 6 weeks — here is what the data showed

I ran a Python paper trading bot for 6 weeks — here is what the data showed As part of my ongoing efforts to improve the performance of TradeSight (a Python-based paper trading bot), I recently completed a 6-week experiment. During this period, the bot continuously executed various strategies on a virtual portfolio, feeding me valuable insights into its strengths and weaknesses. In this article, we'll take a closer look at some key findings from my experiment. We'll cover win rates by strategy, max drawdown events, which strategies survived vs failed, and surprising patterns in the data that caught my attention. A Quick Background on TradeSight For those unfamiliar with TradeSight, it's an open-source Python library for backtesting trading strategies (find it on GitHub ). The project offers a simple way to define and execute various trading scenarios on historical market data. Experimental Setup During the experiment, I utilized five distinct trading strategies: Trend Following Mean Re