ARA.AI

Meridian.AI — Financial Prediction Models

Overview

Meridian.AI is a deep-learning system that forecasts next-day price movement for stocks and forex pairs. It reads recent market history, turns it into 44 technical indicators, and outputs a next-day return estimate plus a direction signal (up/down).

This is the v1.2.0 production release, shipping checkpoint format v7.0.0. v7 makes all 44 input features scale-invariant (raw price/volume levels mostly encoded symbol identity after cross-symbol normalisation), rescales the loss to percent units so return magnitudes are actually calibrated, and adds a 1-day embargo for forex after discovering that the source's daily forex bars leak next-day information (see Honest performance). The v6 reset that preceded it — per-symbol windowing, daily bars only, train-split-only scaling, a compact ~430K-parameter network — is retained unchanged.

The models retrain automatically every hour via GitHub Actions and publish each fresh checkpoint here. You do not need a GPU.

What changed in the v6 architecture

Area	Before (≤ v5.x)	Now (v6)
Training data	Daily + hourly + weekly bars mixed in one table	Daily only — one consistent prediction target
Windowing	One flat array concatenated across all symbols (windows spanned symbol boundaries)	Per-symbol windowing, then sorted by date
Targets	Raw next-step return, clip ±1.0 (allowed impossible +100% targets)	Next-day return, clip ±0.25 (stocks) / ±0.10 (forex)
Feature scaler	Fit on the whole dataset (val leaked into train)	Fit on the train split only
Price adjustment	Splits/dividends showed up as fake returns	auto_adjust=True — no fake split-day moves
Capacity	~11M params (collapsed to a constant)	~430K params (forced to extract signal)
Push safety	None	Sanity gate blocks degenerate models from publishing

Honest performance

These are next-day models, evaluated truly out of sample: trained only on data before 2025-06-01 and tested on the year after (scripts/benchmark_model.py --holdout-start 2025-06-01).

Model (v7)	Holdout samples	Directional accuracy	Always-up baseline	Return MAE	Zero-pred MAE floor
Stocks	12,800	50.2%	51.4%	0.0127	0.0127
Forex (1-day embargo)	5,830	48.7%	52.0%	0.0031	0.0030

Magnitudes are calibrated; direction is not an edge. v7's return-size predictions sit at the zero-prediction MAE floor (v6 was up to 3.7× worse and predicted ~17%/day moves). Neither model beats the always-up drift baseline on next-day direction out of sample — the market-efficiency expectation for daily OHLCV + technical indicators. Treat direction as a weak tilt only.

The pre-1.2.0 forex claim (63.5%, "z = 8.4") is retracted. The daily *=X forex bars are internally inconsistent: day-t high/low span a later window than the stored close and leak the t+1 close (a plain OLS on day-t OHL ratios scores 81% sign accuracy; see scripts/diag_feat_corr.py). Forex now trains and evaluates with a 1-day embargo, which blocks the leak — and with it, the apparent edge disappears.

Neither is a multi-day or week-ahead forecaster — daily price direction is close to efficient, error compounds quickly past one step, and any tool claiming reliable week-ahead price prediction from OHLCV alone is overfitting. Use this for a next-day directional tilt, not as a crystal ball.

Repository layout

meridianal/ARA.AI/
├── models/
│   ├── Meridian.AI_Stocks.pt    ← current v7 stock checkpoint
│   └── Meridian.AI_Forex.pt     ← current v7 forex checkpoint
└── legacy/
    ├── Meridian.AI_Stocks_v5.2.2.pt   ← archived pre-v6 (biased) checkpoint
    └── Meridian.AI_Forex_v5.2.2.pt    ← archived pre-v6 (biased) checkpoint

The loader only accepts checkpoints at version 7.0 or newer — v6 checkpoints have identical tensor shapes but incompatible feature semantics (raw levels vs scale-invariant ratios) and are refused. The v5.x checkpoints in legacy/ are kept only for reference — do not use them for live prediction.

Architecture

The model is MeridianModel: a compact transformer adapted for financial time series. Each block contains:

1. RMSNorm — pre-norm before attention
2. Grouped Query Attention (GQA) — fewer KV heads, QK-Norm for stability, RoPE positions
3. Optional Mamba SSM — vectorised selective scan (off by default on CPU)
4. Layer Scale — per-block learnable scalar for stable training at depth
5. Stochastic Depth — drop-path regularisation
6. RMSNorm — pre-norm before the MoE
7. Mixture of Experts — SwiGLU experts, top-2 routing

Component	Implementation	Purpose
Attention	GQA + QK-Norm	Reduced KV cache, training stability
Position	RoPE	Relative temporal awareness
Expert routing	MoE, top-2, SwiGLU	Regime-specific specialization
Activations	SwiGLU	Better gradient flow vs GELU/ReLU
Normalisation	RMSNorm + Layer Scale	Stable training at depth
Regularisation	Stochastic Depth	Generalisation
Optional SSM	Mamba (vectorised scan)	Long-range dependencies
Loss	BalancedDirectionLoss	Joint regression + direction accuracy

Model specifications (v7.0 default)

Spec	Value
Parameters	~430K
Hidden dimension	96
Layers	3
Attention heads	4 (2 KV heads)
Experts	2 (top-2)
Prediction heads	2
Mamba SSM	Disabled (CPU default)
Input features	44 scale-invariant technical indicators
Sequence length	30 timesteps (daily; forex: window ends 1 day before the prediction base)

Available models

Meridian.AI Stocks

• File: models/Meridian.AI_Stocks.pt
• Coverage: ~50 large-cap US equities per run (AAPL, MSFT, GOOGL, AMZN, NVDA, JPM, SPY, ...)
• Data: Max daily history, split/dividend adjusted, 44 technical indicators
• Training: Automatic hourly CI retrain (GitHub Actions)
• Tracking: Comet project meridianalgo/meridian-ai-stock-v5

Meridian.AI Forex

• File: models/Meridian.AI_Forex.pt
• Coverage: 22 currency pairs (EUR/USD, GBP/USD, USD/JPY, AUD/USD, ...)
• Data: Max daily history, 44 technical indicators
• Training: Automatic hourly CI retrain (GitHub Actions)
• Tracking: Comet project meridianalgo/meridian-ai-forex-v5

Usage

from huggingface_hub import hf_hub_download
from meridianalgo.unified_ml import UnifiedStockML

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 huggingface_hub import hf_hub_download
from meridianalgo.forex_ml import ForexML

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_forex("EUR/USD", days=5)
print(prediction)

Note: the model needs ~200 days of price history to compute its indicators (it uses a 200-day moving average), and it outputs a single next-day return. A multi-day horizon is produced by rolling that one-step prediction forward, so confidence drops sharply after the first day.

Training configuration

Setting	Value
Optimizer	AdamW (weight_decay=0.02, betas=(0.9, 0.95))
LR warmup	Linear ramp, then CosineAnnealingWarmRestarts
Loss	BalancedDirectionLoss (60% Huber + 40% BCE, percent units)
Effective batch size	256 via gradient accumulation
Gradient clipping	Max norm 1.0
EMA	Decay 0.999 — used for validation and the saved checkpoint
Data augmentation	Gaussian noise + timestep masking
Train/val split	Chronological — last 20% held out (no shuffle)
Scaler fit	Train split only (no validation leakage)
Target clip	±0.25 (stocks) / ±0.10 (forex)
Feature clamping	[-10, 10] after z-score normalisation
Sample cap	60K most-recent rows per run
CI step budget	Up to 2000 optimizer steps per run
Checkpoint write	Atomic (.tmp → os.replace)
Push safety	Sanity gate blocks degenerate models
Comet logging	Every step loss/LR/grad-norm + per-epoch metrics + per-symbol dataset audit

Checkpoint format

{
    "model_state_dict": ...,       # PyTorch weights
    "model_type": "stock",         # or "forex"
    "architecture": "MeridianModel-2026",
    "version": "7.0.0",
    "input_size": 44,
    "seq_len": 30,
    "dim": 96,
    "num_layers": 3,
    "num_heads": 4,
    "num_kv_heads": 2,
    "num_experts": 2,
    "num_prediction_heads": 2,
    "dropout": 0.0,                # saved from the live model, not hardcoded
    "use_mamba": False,
    "scaler_mean": Tensor,         # shape (30, 44)
    "scaler_std": Tensor,          # shape (30, 44)
    "metadata": {
        "best_val_loss": float,
        "training_history": [...],
        "trained_symbols": [...],
        "training_date": str,
    }
}

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. Next-day model only. Multi-day output is recursive and degrades fast past day one.
2. Daily direction is near-efficient; the live edge over a naive baseline is small.
3. Performance degrades during black-swan events and regime shifts.
4. Patterns are statistical and may not persist.
5. Pre-v6 checkpoints in legacy/ have a known downward-bias bug — do not use them.
6. For research and educational use only — not financial advice.

Citation

@software{meridianalgo_2026,
  title  = {Meridian.AI: Financial Prediction Engine},
  author = {MeridianAlgo},
  year   = {2026},
  version = {1.0.0},
  url    = {https://github.com/MeridianAlgo/AraAI}
}

Disclaimer

These models are for research and educational purposes only. They do not constitute financial advice. Trading carries significant risk and past performance does not guarantee future results. 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.