| ---
|
| license: mit
|
| tags:
|
| - finance
|
| - trading
|
| - bitcoin
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| - cryptocurrency
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| - quantitative-analysis
|
| - ensemble
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| - xgboost
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| - pytorch
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| - transformer
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| - lstm
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| - time-series
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| - forecasting
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| language:
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| - en
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| pipeline_tag: tabular-classification
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| library_name: pytorch
|
| ---
|
|
|
| <div align="center">
|
|
|
| # 🔮 Nexus Shadow-Quant — Trained Models
|
|
|
| ### Institutional-Grade Crypto Intelligence Engine
|
|
|
| [](https://github.com/lukeedIII/Predictor)
|
| []()
|
| []()
|
|
|
| </div>
|
|
|
| ---
|
|
|
| ## 📋 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 | 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 |
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| | `nexus_medium_transformer_v1.pth` | Transformer (Medium) | 5 epochs | 15 Feb 2026, 05:49 | Balanced capacity/speed |
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| | `nexus_small_transformer_v1.pth` | Transformer (Small) | 10 epochs | 15 Feb 2026, 05:24 | Fast inference, high accuracy |
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| | `nexus_transformer_pretrained.pth` | Pretrained base | — | 14 Feb 2026, 07:22 | Foundation weights |
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| | `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
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| - **MF-DFA** — Multi-fractal detrended fluctuation analysis
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| - **TDA** — Topological Data Analysis (persistent homology)
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| - **Bates SVJ** — Stochastic volatility with jumps
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| - **HMM (3-state)** — Hidden Markov Model for regime classification
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| - **RQA** — Recurrence Quantification Analysis
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| - + 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 |
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| | **Features** | 42 scale-invariant (returns/ratios/z-scores) |
|
| | **Fee Model** | Binance taker 0.04% + slippage 0.01% |
|
|
|
| ---
|
|
|
| ## 🕐 Training Log
|
|
|
| <details>
|
| <summary><strong>📈 Small Transformer — 10 epochs (15 Feb 2026)</strong></summary>
|
|
|
| | 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 |
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| | 6 | 76.0% | 05:17 |
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| | 7 | 76.8% | 05:19 |
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| | 8 | 76.8% | 05:20 |
|
| | 9 | 76.9% | 05:22 |
|
| | **10** | **76.9%** ✅ | **05:24** |
|
|
|
| </details>
|
|
|
| <details>
|
| <summary><strong>📈 Medium Transformer — 5 epochs (15 Feb 2026)</strong></summary>
|
|
|
| | 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** |
|
|
|
| </details>
|
|
|
| <details>
|
| <summary><strong>📈 Nexus Transformer (152M) — 9 epochs (15 Feb 2026)</strong></summary>
|
|
|
| | 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.*
|
|
|
| </details>
|
|
|
| ---
|
|
|
| ## ⚡ 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 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.
|
|
|
| ---
|
|
|
| <div align="center">
|
|
|
| **Dr. Nexus** · *Quantitative intelligence, engineered locally.*
|
|
|
| </div>
|
|
|
