Genetic Algorithm for Quant Strategy Optimization Tutorial (Pyjuque): 1. INTRODUCTION

Hello Friends! In this miniseries I will go over a method you can use to optimize your trading strategies. We will be using a genetic algorithm, which is a type of optimization algorithm inspired by Natural Selection and Evolution, in order to find the best parameters for the Indicators used in our Technical Analysis Strategies. Over the course of these videos we will implement and run an example of this algorithm, aiming to optimize a TA based strategy combining Bollinger Bands and RSI, which I posted for free in my open source bot PYJUQUE, built with Python. Genetic Algorithms are most famously used for optimizing hyper parameters in machine learning models; I chose to cover them because of their beauty, uniqueness and the fact that they fit well in this strategy optimization scenario. ~~ LINKS ~~ The code featured here can be found on my open source trading bot - PYJUQUE: https://github.com/tudorelu/pyjuque (contributions welcome) This Optimization Algorithm was inspired by Joos Korstanje’s article: https://towardsdatascience.com/a-simple-genetic-algorithm-from-scratch-in-python-4e8c66ac3121 ~~ SUPPORT ~~ Please Like & Subscribe! Donations Welcome! BTC: 17Q7PLFAKFDsNdC36PEM7WVLH9DWkFiJXz ETH: 0x43C8E81bCCb4Dc7f63b7c995F2FdAC365EAA78a1 Or Sponsor me on Github (like Patreon): https://github.com/sponsors/tudorelu Binance Referral: https://www.binance.com/?ref=10961872 ~~ CONTACT ~~ Inquiries & Work: [email protected] Join our Discord: https://discord.gg/7VzPV5a Join me on Telegram: https://t.me/joinchat/G0Ey-xVAjNdYcZcAR6Vkjw Sometimes I stream on Twitch, follow me there: https://www.twitch.tv/tudorelu01