[nyrulez]

The whole backend is in Python + Pandas (which is bulk of the work - market data, quantitative analysis, aggregation, some performance engineering and so on). For the Webapp itself, I used a library called Dash. I recommend it for data heavy applications :)

I am working on a more ambitious (for me) venture to bring Quantitative Finance + Data Science to the DIY Investor community, for Stocks/ETFs/MFs and Crypto. That means being able to distill the market data towards good performance and risk without all the manual research, managing Risk/Reward and Correlations for your portfolio in a more informed way vs basing your investment purely on performance and subjective news/opinions. Things are done quite differently in hedge funds vs how the mainstream still invests. This is a step towards that. I explain more here: https://www.benefits.coinquanta.com/