{"repo":"ababber/pyhou-02-17-2026","free":true,"listed":false,"github":"https://github.com/ababber/pyhou-02-17-2026","clone":"git clone https://github.com/ababber/pyhou-02-17-2026.git","description":"A companion repo to \"Quantitative Trading: A First Look With QuantConnect\". This YouTube series is a reproduction of a live PyHou Meetup from February 17, 2026.","language":"HTML","stars":11,"topics":["deep-learn","finance","machine-learning","python","quantconnect","quantitative-trading","time-series-forecasting","algorithmic-trading"],"license":"MIT","category":"trading","readme_excerpt":"Quantitative Trading: A First Look With QuantConnect Can machine learning predict financial markets? This repo accompanies a 3-part video series where I test three generations of ML — from a 1970 linear model to a 2024 foundation model — on the same backtesting platform. 📺 Click here to watch the full playlist on YouTube! --- Quick Navigation Part 1: Classical ML (Ridge Regression) Part 2: Deep Learning (Temporal CNN) Part 3: Foundation Models (Amazon Chronos) Note: When opening in Colab, you'll see a \"This notebook was not authored by Google\" warning — click Run anyway to proceed. --- Part 1: Classical ML (Ridge Regression) ▶️ Watch the Part 1 Video The first video covers ridge regression — a classical linear model from 1970 — applied to inverse volatility weighting on 12 futures contracts. The strategy: - Trade 12 futures (indices, energy, grains) - Predict next-week volatility using ridge regression - Allocate inversely: less volatile contracts get more capital - Rebalance weekly The result: Sharpe 0.212, Alpha -0.062. The model tracks the market with extra drawdown. It doesn't generate alpha. Why it matters: Understanding why a simple model fails sets up everything that follows. Linear models can't capture the nonlinear patterns in financial data. --- Part 2: Deep Learning (Temporal CNN) ▶️ Watch the Part 2 Video The second video covers temporal convolutional networks — detecting patterns across multiple timescales in price data. The strategy: - Trade top 3 QQQ holdings ","default_branch":null,"files":null,"tree":[],"storefront":"/r/ababber","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ababber/pyhou-02-17-2026/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."}