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..