Frequant

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Institutional grade platform for investment research, bringing backtesting and algorithmic trading to everyone.

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I’d recently developed a keen interest in trading, mainly Cryptocurrencies due to their volatility but I do dabble in other asset classes.

My passion for programming lead me down the path of wondering how best I could automate my strategies.

TradingView is where I would do all my analysis, so naturally I read up on the Pine Script documentation and had a go at building out my strategy using their platform. This was a great stepping stone as it made it really easy to understand how data flows through a trading engine. However I quickly noticed there were a few limitations, such as not being able to reference external data and how far back I could run my tests.

After much googling I came across some really awesome projects like gekko, zenbot, freqtrade, octobot, superalgos, etc. Playing around with these projects exposed me to even more of how trading systems work and how they’re built. However it just felt like something was missing from each of them, it just felt really opinionated.

I wanted an agnostic platform that could work for any strategy, across any asset class and timeframe.

Enter zipline from Quantopian - which is what this platform is based on.

“Quantopian aimed to create a crowd-sourced hedge fund by letting freelance quantitative analysts develop, test, and use trading algorithms to buy and sell securities.”

- Wikipedia

Unfortunately, after 9 years, in November 2020 - Quantopian announced they would be shutting down.

Thankfully, a few important components of what made up Quantopian - including zipline, alphalens, pyfolio and some public research jupyter notebooks including tons of tutorials are open-source and available on GitHub. Even though no active development seems to be happening in these repositories, it seems a user by the name of shlomikushchi has continued development over at shlomikushchi/zipline-trader.

Frequant aims to consolidate all these works in an easy to use and most importantly learn format by wrapping it in a friendly interface and making it easily deployable via docker, digital ocean or heroku.

“Press F to Pay Respects for Quantopian.”