metadata
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
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.