---
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.