meridianal/ARA.AI

Meridian.AI - Financial Prediction Models

Overview

Meridian.AI is a 45M-parameter deep learning system for predicting price movements across stocks and forex pairs. It combines Mamba-2 state space models, sparse mixture-of-experts routing, and grouped query attention into a single unified architecture, trained continuously via GitHub Actions.

Architecture

Component	Implementation	Purpose
Sequence Modeling	Mamba-2 SSM with selective scan	Linear-time long-range dependencies
Attention	Grouped Query Attention (GQA) + QK-Norm	Efficient multi-head attention with reduced KV cache
Position Encoding	Rotary Position Embeddings (RoPE)	Relative temporal awareness
Expert Routing	Mixture of Experts (4 experts, top-2)	Regime-specific specialization
Activations	SwiGLU (gated linear units)	Improved gradient flow
Normalization	RMSNorm + Layer Scale	Training stability at scale
Regularization	Stochastic Depth (drop path)	Better generalization
Training	Mixed precision (FP16/BF16) via Accelerate	2x throughput on compatible hardware
Loss	BalancedDirectionLoss (Huber + BCE)	Joint price regression and direction accuracy

Model Specifications

Architecture: Revolutionary v4.1
Parameters: ~45 Million (33,071,045)
Input Features: 44 technical indicators
Sequence Length: 30 timesteps
Hidden Dimension: 384
Transformer Layers: 6
Attention Heads: 6 (2 KV heads)
Experts: 4 (top-2 routing)
Prediction Heads: 4

Available Models

Meridian.AI Stocks

• File: models/Meridian.AI_Stocks.pt
• Coverage: 49 equities across sectors (AAPL, MSFT, GOOGL, AMZN, TSLA, etc.)
• Data: Daily + hourly + weekly OHLCV with 44 technical indicators

Meridian.AI Forex

• File: models/Meridian.AI_Forex.pt
• Coverage: 22 currency pairs (EUR/USD, GBP/USD, USD/JPY, etc.)
• Data: Multi-timeframe OHLCV with 44 technical indicators

Usage

from huggingface_hub import hf_hub_download
from meridianalgo.unified_ml import UnifiedStockML

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

ml = UnifiedStockML(model_path=model_path)
prediction = ml.predict_ultimate("AAPL", days=5)
print(prediction)

from meridianalgo.forex_ml import ForexML

# Download the forex model
model_path = hf_hub_download(
    repo_id="meridianal/ARA.AI",
    filename="models/Meridian.AI_Forex.pt"
)

ml = ForexML(model_path=model_path)
prediction = ml.predict("EURUSD=X", days=5)
print(prediction)

Training

• Optimizer: AdamW (weight decay 0.02, betas 0.9/0.95)
• Scheduler: Cosine annealing with warm restarts
• Loss: BalancedDirectionLoss (60% Huber + 40% balanced BCE)
• Batch Size: Effective 256 via gradient accumulation
• EMA: Exponential moving average of weights (decay 0.999)
• Data Augmentation: Gaussian noise (0.5%) + random timestep masking (5%)
• Early Stopping: Patience of 20 epochs on EMA validation loss
• Pipeline: Automated via GitHub Actions every 6 hours

Metrics are tracked with Comet ML. Version gating ensures stale pre-v4.1 checkpoints are never loaded.

Technical Indicators (44 features)

Category	Indicators
Price	Returns, Log Returns, Volatility, ATR
Trend	SMA (5/10/20/50/200), EMA (5/10/20/50/200)
Momentum	RSI, Fast RSI, Stochastic RSI, Momentum, ROC, Williams %R
Oscillators	MACD, MACD Signal, MACD Histogram, Stochastic K/D, CCI
Volatility	Bollinger Bands (Upper/Lower/Width/%B), Keltner Channels (Upper/Lower/%K)
Volume	Volume SMA, Volume Ratio, OBV (normalized)
Trend Strength	ADX, +DI, -DI, Price vs SMA50/SMA200, ATR%
Mean Reversion	Z-Score (20d), Distance from 52-week High

Limitations

1. Performance may degrade during black swan events or extreme volatility.
2. Predictive accuracy decreases as forecast horizon extends.
3. Models reflect patterns in historical data which may not repeat.
4. For research and educational use only -- not financial advice.

Citation

@software{meridianalgo_2026,
  title = {Meridian.AI: Financial Prediction Engine},
  author = {MeridianAlgo},
  year = {2026},
  version = {4.1.0}
}

Disclaimer

These models are for research and educational purposes only. They do not constitute financial advice. Trading financial instruments carries significant risk. The developers and contributors are not liable for any financial losses. All trading decisions are yours alone.

License

MIT License. See the GitHub repository for details.