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Lukeed/
Predictor-Models

quantitative-analysis

tabular-classification

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🔮 Nexus Shadow-Quant — Trained Models

Institutional-Grade Crypto Intelligence Engine

GitHub

📋 Overview

This repository contains the pre-trained model artifacts for Nexus Shadow-Quant — a 16-model ensemble engine for BTC/USDT directional forecasting.

Why this exists: Training the full model stack from scratch takes ~6 hours on a modern GPU. By hosting the trained weights here, new installations can pull them instantly and skip the initial training phase entirely.

🏗️ Model Architecture

Model	Type	Parameters	Trained	Purpose
`predictor_v3.joblib`	XGBoost Ensemble	~500 trees	15 Feb 2026, 02:31	Primary directional classifier
`nexus_lstm_v3.pth`	Bi-LSTM	~2M	14 Feb 2026, 11:45	Sequence pattern recognition
`nexus_transformer_v2.pth`	Transformer (152M)	5 epochs	15 Feb 2026, 04:44	Long-range dependency modeling
`nexus_medium_transformer_v1.pth`	Transformer (Medium)	5 epochs	15 Feb 2026, 05:49	Balanced capacity/speed
`nexus_small_transformer_v1.pth`	Transformer (Small)	10 epochs	15 Feb 2026, 05:24	Fast inference, high accuracy
`nexus_transformer_pretrained.pth`	Pretrained base	—	14 Feb 2026, 07:22	Foundation weights
`feature_scaler_v3.pkl`	StandardScaler	—	15 Feb 2026, 02:31	Feature normalization state

Supporting Models (16-Model Quant Panel)

GARCH(1,1) — Volatility regime detection
MF-DFA — Multi-fractal detrended fluctuation analysis
TDA — Topological Data Analysis (persistent homology)
Bates SVJ — Stochastic volatility with jumps
HMM (3-state) — Hidden Markov Model for regime classification
RQA — Recurrence Quantification Analysis
- 10 more statistical models

📊 Performance (Audited)

Metric	Value
Audit Size	105,031 predictions on 3.15M candles
Accuracy	50.71% (statistically significant above 50%)
Sharpe Ratio	0.88 (annualized, fee-adjusted)
Prediction Horizon	15 minutes
Features	42 scale-invariant (returns/ratios/z-scores)
Fee Model	Binance taker 0.04% + slippage 0.01%

🕐 Training Log

📈 Small Transformer — 10 epochs (15 Feb 2026)

Epoch	Accuracy	Timestamp
1	60.0%	05:09
2	69.7%	05:10
3	72.6%	05:12
4	74.5%	05:14
5	75.2%	05:15
6	76.0%	05:17
7	76.8%	05:19
8	76.8%	05:20
9	76.9%	05:22
10	76.9% ✅	05:24

📈 Medium Transformer — 5 epochs (15 Feb 2026)

Epoch	Accuracy	Timestamp
1	58.1%	05:34
2	69.8%	05:37
3	72.7%	05:41
4	74.8%	05:45
5	76.2% ✅	05:49

📈 Nexus Transformer (152M) — 9 epochs (15 Feb 2026)

Epoch	Accuracy	Timestamp
1	51.3%	06:30
2	52.4%	06:51
3	52.4%	07:12
4	53.1%	07:32
5	54.6%	07:52
6	55.3%	08:13
7	57.3%	08:33
8	58.1%	08:54
9	58.7% ✅	09:14

Epoch 10 failed — weights from epoch 9 preserved.

⚡ Quick Start

Automatic (Recommended)

The Nexus Shadow-Quant app will auto-pull these models on first startup if no local models are found. Simply:

Set your HUGGINGFACE_TOKEN and HF_REPO_ID in Settings.
Restart the backend.
Models are downloaded and the predictor is ready instantly.

Manual

bash

pip install huggingface_hub
huggingface-cli download Lukeed/Predictor-Models --local-dir ./models

🔄 Sync Protocol

Action	What happens
Push to Hub	Uploads all files from `models/` folder to this repo
Pull from Hub	Downloads latest weights, re-initializes the predictor
Auto-Pull	On startup, if no local models found, pulls automatically

⚠️ Disclaimer

These models are trained on historical BTC/USDT data and are provided for educational and research purposes only. They are not financial advice. Cryptocurrency markets are volatile. Past performance does not guarantee future results.

Dr. Nexus · Quantitative intelligence, engineered locally.