{"repo":"MeridianAlgo/Learn-Quant","free":true,"listed":false,"github":"https://github.com/MeridianAlgo/Learn-Quant","clone":"git clone https://github.com/MeridianAlgo/Learn-Quant.git","description":"This repository offers beginners in Python and JavaScript a look at the utilities that go into creating each of our programs. Each of these programs is detailed with comments so you can learn more about quantitative finance through code.","language":"Python","stars":66,"topics":["beginner-friendly","meridianalgo","python","quantitative-finance","quantitative-methods","learning-python","learning-resources","learn-quant"],"license":null,"category":"trading","readme_excerpt":"Learn-Quant: Master Quantitative Finance & Python (v2.15.0) Welcome to Learn-Quant , your all-in-one, open-source toolkit for mastering algorithmic trading, quantitative finance theory, and professional Python engineering. Every folder is a fully self-contained lesson: run it, read it, extend it. Read the docs at meridianalgo.github.io/Learn-Quant The documentation site is the best way to explore Learn-Quant, searchable, with curated learning paths, a glossary, per-module difficulty badges and copy-paste run commands. --- What is New in v2.15.0 Four new modules, two in Python and two in JavaScript, covering the frequency view of a price series, the classifier that maximises its own margin, and the risk and portfolio maths that most people reach for a library to do. Quantitative Methods, Fourier Analysis finds the cycle length hiding in a series, filters out the fast wiggles with no lag at all, and computes autocorrelation at every lag at once, while being blunt about why a trend has to come out first and why the no lag filter cannot be traded live. Machine Learning, Support Vector Machine fits the widest possible corridor between two classes with a soft margin and hinge loss, identifies the handful of support vectors the boundary actually rests on, and refuses hyperparameters that would silently diverge into arithmetic garbage. Risk Metrics, JavaScript brings drawdown with a proper running peak, Sharpe, Sortino, Calmar, historical VaR, conditional VaR and the ulcer index to p","default_branch":null,"files":null,"tree":[],"storefront":"/r/MeridianAlgo","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/MeridianAlgo/Learn-Quant/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}