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Aialpha

Aialpha is a Python project that demonstrates stacked neural networks for stock-return prediction, using tick-data sampling, feature engineering, a stacked autoencoder and downstream LSTM regression and Random Forest classification models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Aialpha — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Aialpha as an educational reference or research starting point when examining stacked neural networks and stock-return prediction, rather than as a ready-to-use live-trading system.

Who it is for

  • People learning how multilayer neural networks can be applied to financial data.
  • Quantitative-finance researchers seeking a codebase to study or adapt for stock-return prediction.

Documented uses

  • Study a pipeline that turns tick data into sampled bars and engineered features for modeling.
  • Explore dimensionality reduction with a stacked autoencoder before prediction.
  • Compare an LSTM regression approach with Random Forest classification for stock-move direction.
Before you choose

Requirements & limits

The project is a public GitHub repository with an MIT license. Its documented quickstart uses Python 3.

  • The workflow requires tick data, but the README says only a partial dataset is included and notes the original data source may no longer be available.
  • The example uses tick-, volume- or dollar-based bar sampling and moving-average and rolling-volatility features.
  • The included prediction approaches are LSTM regression and Random Forest classification; the project says live-trading use requires modifications.
  • The README cautions that backtesting performance does not guarantee live-trading results.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install dependencies and start the Python example

Prepare the documented Python environment and invoke the project’s quickstart script.

Before you start

  • Python 3
  • The repository’s requirements.txt file

Steps

  1. Install dependencies with pip install -r requirements.txt.

  2. Start the example with python run.py.

Expected outcome

The repository’s quickstart invokes its model workflow.

Guide limitations

  • Only part of the dataset is included; the README says one million rows are uploaded and results may differ from those shown.
  • The README characterizes the project as educational and says it is not intended for live trading without modifications.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
VivekPa/AIAlpha · Official homepage title · retained in its source language
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