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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
---
<div align="center">
# 🔮 Nexus Shadow-Quant — Trained Models
### Institutional-Grade Crypto Intelligence Engine
[](https://github.com/lukeedIII/Predictor)
[]()
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## 📋 Overview
This repository contains the **pre-trained model artifacts** for [Nexus Shadow-Quant](https://github.com/lukeedIII/Predictor) — 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 | 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
---
## 📊 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% |
---
## ⚡ 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
```bash
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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<div align="center">
**Dr. Nexus** · *Quantitative intelligence, engineered locally.*
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