{"repo":"MeridianAlgo/AraAI","free":true,"listed":false,"github":"https://github.com/MeridianAlgo/AraAI","clone":"git clone https://github.com/MeridianAlgo/AraAI.git","description":"Machine learning platform for market analysis and forecasting, with a focus on stock volatility prediction, market trend forecasting, and portfolio optimization.","language":"Python","stars":16,"topics":["meridianalgo","forecasting-models","forex-prediction","hugging-face","stock-prediction","open-source","training","training-project","stock-analysis","stock-market"],"license":null,"category":"trading","readme_excerpt":"Ara.AI Cross-sectional daily stock ranking Overview Ara.AI v8 ranks a universe of stocks by their expected next-day return relative to each other , and holds that view as a dollar-neutral long/short book. It is a gradient-boosted tree ensemble over 40 scale-free daily features. It trains in about eleven seconds on a CPU, needs no GPU, and the whole GitHub Actions pipeline — fetch, four-fold walk-forward backtest, final fit, publish — finishes in a few minutes. It replaces v7, a 433K-parameter transformer that predicted each stock's absolute next-day return, retrained hourly, and had no measured edge : 50.23% direction accuracy against a 51.44% always-up baseline. The full rationale, the diagnosis, and every number are in docs/ARA V8.md . The v7 stack is frozen under legacy/ and still runs on demand. Trained models: meridianal/ARA.AI. What changed, in one paragraph Most of a stock's next-day return is the market's next-day return, which daily OHLCV cannot predict — so a model trained on absolute returns spends all its capacity on noise, and \"always up\" beats it because the market drifts up. v8 subtracts the universe's mean return out of the label and predicts only the residual: which names beat their peers. That target is forecastable, the baseline it must beat is a true 50%, and the natural output is a ranking rather than a price. Switching from a transformer to gradient-boosted trees followed from the same honesty: 40 weak tabular features is the regime where trees win, and ","default_branch":null,"files":null,"tree":[],"storefront":"/r/MeridianAlgo","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/MeridianAlgo/AraAI/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."}