VivekPa/AIAlpha resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1955 stars · 449 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An educational Python project demonstrating a stacked neural network architecture (autoencoder plus LSTM and Random Forest models) for predicting stock returns from tick data. It walks through bar sampling, feature engineering, dimensionality reduction, and model training following the approach of Advances in Financial Machine Learning.

Use cases

• predict stock prices with machine learning
• learn to build LSTM models for financial data
• use autoencoders for feature reduction in trading
• build algorithmic trading strategies with neural networks
• apply Marcos Lopez de Prado techniques in Python
• classify stock price movement direction with random forest

When to choose

• you want to learn how stacked neural networks are applied to stock prediction
• you need a reference implementation of bar sampling, feature engineering, and autoencoders for financial data
• you are studying quantitative finance and machine learning concepts

When to avoid

• you need production-ready live trading software
• you expect maintained code with recent updates or support
• you need the original tick dataset, which is no longer available

Facets

learning-resource · maturity abandoned

machine-learning deep-learning data-science trading fintech machine-learning artificial-intelligence python stock-price-prediction lstm autoencoder algorithmic-trading quantitative-finance educational-project

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