| | ---
|
| | language: en
|
| | license: mit
|
| | tags:
|
| | - financial-forecasting
|
| | - stock-prediction
|
| | - time-series
|
| | - pytorch
|
| | - ara-ai
|
| | - ensemble-learning
|
| | datasets:
|
| | - yfinance
|
| | metrics:
|
| | - accuracy
|
| | - mse
|
| | ---
|
| |
|
| | # Ara AI (ARA.AI) - Financial Prediction Engine
|
| |
|
| | ## Overview
|
| | Ara AI is an advanced financial prediction system designed for multi-asset forecasting. This repository contains the latest weights for the ensemble models trained on market data.
|
| |
|
| | ## Model Architecture
|
| | The system employs a sophisticated ensemble architecture:
|
| | - **Feature Extraction**: 44+ technical indicators (RSI, MACD, Bollinger Bands, ATR, etc.)
|
| | - **Neural Core**: A large PyTorch model with 4M+ parameters
|
| | - **Attention Mechanism**: Multi-head attention for identifying key temporal features
|
| | - **Ensemble Heads**: Specialized prediction heads inspired by XGBoost, LightGBM, Random Forest, and Gradient Boosting
|
| | - **Dynamic Weighting**: Softmax-based attention weights for weighted prediction averaging
|
| |
|
| | ## Latest Training Stats (2026-01-10 21:11:54)
|
| | - **Last Trained Symbol**: Initialization
|
| | - **Validation Accuracy**: N/A%
|
| | - **Validation Loss (MSE)**: N/A
|
| | - **Total Unique Symbols in Training History**: 0
|
| |
|
| | ## Continuous Training
|
| | This model is part of a self-evolving system. It is retrained daily on a rotation of 6,800+ tickers and 20+ forex pairs to maintain high accuracy across different market conditions and time horizons (1D, 1H).
|
| |
|
| | ## Usage
|
| | ### Loading the model
|
| | ```python
|
| | import torch
|
| | from meridianalgo.unified_ml import UnifiedStockML
|
| |
|
| | # Download the model file from this repo first
|
| | ml = UnifiedStockML(model_path="stock_AAPL.pt")
|
| | prediction = ml.predict_ultimate("AAPL", days=5)
|
| | print(prediction)
|
| | ```
|
| |
|
| | ## Disclaimer
|
| | **Not Financial Advice.** This software is for educational purposes only. Trading involves significant risk. The authors are not responsible for any financial losses incurred.
|
| |
|
