# A quant finance degree is sitting on Github for free.. you need these 5 githu...
Canonical: https://social-archive.org/yena/JrA4ecpGnV
Original URL: https://x.com/Axel_bitblaze69/status/2072072752866144695
Author: Axel Bitblaze 🪓
Platform: x
## Content
A quant finance degree is sitting on Github for free.. you need these 5 github repos. the stuff expensive programs gatekeep.. from "what do i even learn" to running the same Machine learning trading models the desks use.. All built by real practitioners. here's the exact order i'd go in: ▫️ 1. the map → wilsonfreitas/awesome-quant (27k★) before you learn anything, you need to see the whole field. a curated list of every quant library, data source, paper and tool worth knowing, sorted by language and topic.. data, backtesting, risk, ml, crypto. do this first: open the section for your language. that's your entire toolset in one scroll.. ▫️ 2. the course → stefan-jansen/machine-learning-for-trading (19k★) a full ML-for-trading course delivered as code, not slides. it takes you from sourcing raw data all the way to backtesting a live strategy. what's inside: alpha factors, every model from linear regression to boosting to deep learning to RL, and real backtesting. do this first: clone it, open the data-sourcing notebook, run one cell. you learn by doing, not reading. ▫️ 3. the platform → microsoft/qlib (45k★) microsoft's open-source ai platform for quant research. this is where you stop writing one-off scripts and start running real experiments. what's inside: ready-made datasets, example ml models you can train out of the box, and a one-config backtest that spits out returns and sharpe. do this first: run the quickstart workflow and you've got a full ML backtest in front of you, no setup marathon. ▫️ 4. the pro tools → goldmansachs/gs-quant (11k★) goldman sachs open-sourced their own python toolkit for pricing, risk and strategy. you're literally reading how a top desk structures its code. what's inside: derivatives pricing, risk measures, portfolio tools, each with tutorial notebooks. do this first: open the tutorials folder. closest you'll get to sitting on their desk. ▫️ 5. the theory → cantaro86/Financial-Models-Numerical-Methods (6.9k★) for when you want to understand why it works, not just call a library. interactive notebooks on the actual math. what's inside: black-scholes, the heston model, jump-diffusion, and the numerical methods (monte carlo, PDE, FFT) behind them. do this first: open the option-pricing notebook. read the derivation, then run it. the path: → 1 to map the field → 2 to actually learn it → 3 and 4 to build like a desk → 5 when you want the math underneath if you're lost, open awesome-quant. if you want to ship something this weekend, start with the ml-for-trading repo and don't stop until a backtest runs. follow me @Axel_bitblaze69 for more such repos..