Upload README.md with huggingface_hub

README.md

@@ -1,57 +1,53 @@
----
-license: mit
-tags:
-- finance
-- stock-prediction
-- forex
-- time-series
-- pytorch
-- ensemble-learning
-library_name: pytorch
----
-
-# ARA AI - Financial Prediction Models
-
-Continuously trained ensemble ML models for stock and forex prediction.
-
-## Models
-
-- **Stock Models**: Trained on random selection of stocks every 2 hours
-- **Forex Models**: EURUSD, GBPUSD, USDJPY trained every 2 hours
-
-## Training Schedule
-
-Models are automatically retrained every 2 hours (12 times daily) using:
-- 2 years of historical data
-- Incremental training on existing models
-- Ensemble of XGBoost, LightGBM, Random Forest, Transformers, CNN-LSTM
-
-## Usage
-
-```python
-from huggingface_hub import hf_hub_download
-from meridianalgo.unified_ml import UnifiedStockML
-
-# Download model
-model_path = hf_hub_download(
-    repo_id="MeridianAlgo/ARA.AI",
-    filename="models/stock_AAPL.pt"
-)
-
-# Load and predict
-ml = UnifiedStockML(model_path=model_path)
-prediction = ml.predict('AAPL', days=5)
-```
-
-## Experiment Tracking
-
-Training metrics tracked on [Weights & Biases](https://wandb.ai)
-
-## Repository
-
-Source code: [github.com/MeridianAlgo/AraAI](https://github.com/MeridianAlgo/AraAI)
-
-## Disclaimer
-
-These models are for educational and research purposes only. Not financial advice.
-Past performance does not guarantee future results.
+---
+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.