MeridianAlgo/ARA.AI

MeridianAlgo Financial Prediction Models

Model Overview

This repository contains financial prediction models built on the latest state-of-the-art 2026 architecture. These models leverage machine learning techniques, including Mamba State Space Models, Mixture of Experts, and Flash Attention 2, for accurate market forecasting.

Architecture Highlights

• Latest 2026 Architecture: Implementing the latest advances in deep learning for time series.
• 388M Parameters: Large-scale model for comprehensive pattern recognition.
• Unified Design: A single high-capacity model architectures for all assets within its class.
• Specialized Logic: Distinct optimization paths for Stocks and Forex markets.

Technical Specifications

Core Technologies

Component	Description
Mamba SSM	State Space Models for efficient long-range sequence modeling with linear complexity.
RoPE	Rotary Position Embeddings for enhanced temporal relationship encoding.
GQA	Grouped Query Attention for optimized computational throughput.
MoE	Mixture of Experts with top-k routing for specialized regime recognition.
SwiGLU	Swish-Gated Linear Unit activation for improved transformer performance.
RMSNorm	Root Mean Square Normalization for enhanced gradient stability.
Flash Attention 2	High-performance, memory-efficient attention implementation.

Model Specifications

Architecture: Revolutionary 2026
Parameters: 388,000,000
Input Features: 44 technical indicators
Sequence Length: 30 time steps
Hidden Dimensions: 768
Transformer Layers: 6
Attention Heads: 12 (Query), 4 (Key/Value)
Experts: 12 specialized models
Prediction Heads: 8 ensemble heads

Available Models

1. Meridian.AI Stocks Model

• Repo ID: MeridianAlgo/ARA.AI
• File: models/Meridian.AI_Stocks.pt
• Purpose: Comprehensive stock market forecasting.
• Coverage: Broad equity market compatibility.
• Accuracy: Optimized for directional consistency.

2. Meridian.AI Forex Model

• Repo ID: MeridianAlgo/ARA.AI
• File: models/Meridian.AI_Forex.pt
• Purpose: High-precision currency pair forecasting.
• Coverage: Major, Minor, and Exotic pairs.
• Accuracy: Optimized for pip-based movement prediction.

Performance Metrics

Attribute	Stocks Model	Forex Model
Parameters	388 Million	388 Million
Inference Latency	<100ms	<100ms
Model Size	~1.5 GB	~1.5 GB
Accuracy	Optimized	Optimized

Usage and Implementation

Installation

pip install torch transformers huggingface_hub

Loading and Inference

from huggingface_hub import hf_hub_download
from meridianalgo.unified_ml import UnifiedStockML

# Download the unified stocks model
model_path = hf_hub_download(
    repo_id="MeridianAlgo/ARA.AI",
    filename="models/Meridian.AI_Stocks.pt"
)

# Initialize the system
ml = UnifiedStockML(model_path=model_path)

# Execute prediction
prediction = ml.predict_ultimate('AAPL', days=5)

print(f"Current Price: {prediction['current_price']}")

Training Methodology

Configuration

• Optimizer: AdamW with Weight Decay
• Scheduler: Cosine Annealing with Warm Restarts
• Loss Function: Balanced Directional Loss
• Batch Size: 64
• Learning Rate: 0.0005

Infrastructure

Models are trained using a distributed pipeline with Accelerate, tracking all metrics via Comet ML for rigorous validation.

Limitations and Ethical Use

1. Market Volatility: Performance may degrade during black swan events or extreme volatility.
2. Horizon Decay: Predictive accuracy naturally decreases as the forecast horizon extends.
3. Historical Bias: Models reflect patterns in historical data which may not repeat.
4. Professional Use Only: Intended for research; users bear all financial risk.

Citation

If utilizing these models in professional research or applications, please cite:

@software{meridianalgo_2026,
  title = {MeridianAlgo: Revolutionary Financial Prediction Platform},
  author = {MeridianAlgo},
  year = {2026},
  version = {4.0.0}
}

Disclaimer

IMPORTANT: These models are for research and educational purposes only. They do not constitute financial advice. All trading involves risk of capital loss. The developers and contributors are not registered financial advisors.