README.md

metadata

license: mit
tags:
  - finance
  - trading
  - bitcoin
  - cryptocurrency
  - quantitative-analysis
  - ensemble
  - xgboost
  - pytorch
  - transformer
  - lstm
  - time-series
  - forecasting
language:
  - en
pipeline_tag: tabular-classification
library_name: pytorch

🔮 Nexus Shadow-Quant — Trained Models

Institutional-Grade Crypto Intelligence Engine

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📋 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.

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🏗️ Model Architecture

Model	Type	Parameters	Purpose
predictor_v3.joblib	XGBoost Ensemble	~500 trees	Primary directional classifier
nexus_lstm_v3.pth	Bi-LSTM	~2M	Sequence pattern recognition
nexus_transformer_v2.pth	Transformer	~152M	Long-range dependency modeling
feature_scaler_v3.pkl	StandardScaler	—	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

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📊 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%

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⚡ Quick Start

Automatic (Recommended)

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

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

Manual

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

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🔄 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

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⚠️ 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.

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Dr. Nexus · Quantitative intelligence, engineered locally.