| | ---
|
| | license: mit
|
| | tags:
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| | - finance
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| | - trading
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| | - bitcoin
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| | - cryptocurrency
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| | - quantitative-analysis
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| | - 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
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| |
|
| | ### Institutional-Grade Crypto Intelligence Engine
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| |
|
| | [](https://github.com/lukeedIII/Predictor)
|
| | []()
|
| | []()
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| |
|
| | </div>
|
| |
|
| | ---
|
| |
|
| | ## 📋 Overview
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| |
|
| | 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.
|
| |
|
| | ---
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| |
|
| | ## 🏗️ Model Architecture
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| |
|
| | | 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 |
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| | | `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 |
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| |
|
| | ### Supporting Models (16-Model Quant Panel)
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| | - **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
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| |
|
| | ---
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| |
|
| | ## 📊 Performance (Audited)
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| |
|
| | | Metric | Value |
|
| | |:---|:---|
|
| | | **Audit Size** | 105,031 predictions on 3.15M candles |
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| | | **Accuracy** | 50.71% (statistically significant above 50%) |
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| | | **Sharpe Ratio** | 0.88 (annualized, fee-adjusted) |
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| | | **Prediction Horizon** | 15 minutes |
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| | | **Features** | 42 scale-invariant (returns/ratios/z-scores) |
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| | | **Fee Model** | Binance taker 0.04% + slippage 0.01% |
|
| |
|
| | ---
|
| |
|
| | ## 🕐 Training Log
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| |
|
| | <details>
|
| | <summary><strong>📈 Small Transformer — 10 epochs (15 Feb 2026)</strong></summary>
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| |
|
| | | Epoch | Accuracy | Timestamp |
|
| | |:---|:---|:---|
|
| | | 1 | 60.0% | 05:09 |
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| | | 2 | 69.7% | 05:10 |
|
| | | 3 | 72.6% | 05:12 |
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| | | 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 |
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| | | 9 | 76.9% | 05:22 |
|
| | | **10** | **76.9%** ✅ | **05:24** |
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| |
|
| | </details>
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| |
|
| | <details>
|
| | <summary><strong>📈 Medium Transformer — 5 epochs (15 Feb 2026)</strong></summary>
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| |
|
| | | Epoch | Accuracy | Timestamp |
|
| | |:---|:---|:---|
|
| | | 1 | 58.1% | 05:34 |
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| | | 2 | 69.8% | 05:37 |
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| | | 3 | 72.7% | 05:41 |
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| | | 4 | 74.8% | 05:45 |
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| | | **5** | **76.2%** ✅ | **05:49** |
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| |
|
| | </details>
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| |
|
| | <details>
|
| | <summary><strong>📈 Nexus Transformer (152M) — 9 epochs (15 Feb 2026)</strong></summary>
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| |
|
| | | Epoch | Accuracy | Timestamp |
|
| | |:---|:---|:---|
|
| | | 1 | 51.3% | 06:30 |
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| | | 2 | 52.4% | 06:51 |
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| | | 3 | 52.4% | 07:12 |
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| | | 4 | 53.1% | 07:32 |
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| | | 5 | 54.6% | 07:52 |
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| | | 6 | 55.3% | 08:13 |
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| | | 7 | 57.3% | 08:33 |
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| | | 8 | 58.1% | 08:54 |
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| | | **9** | **58.7%** ✅ | **09:14** |
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| |
|
| | *Epoch 10 failed — weights from epoch 9 preserved.*
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| |
|
| | </details>
|
| |
|
| | ---
|
| |
|
| | ## ⚡ Quick Start
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| |
|
| | ### Automatic (Recommended)
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| | The Nexus Shadow-Quant app will **auto-pull** these models on first startup if no local models are found. Simply:
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| | 1. Set your `HUGGINGFACE_TOKEN` and `HF_REPO_ID` in Settings.
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| | 2. Restart the backend.
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| | 3. Models are downloaded and the predictor is ready instantly.
|
| |
|
| | ### Manual
|
| | ```bash
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| | pip install huggingface_hub
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| | huggingface-cli download Lukeed/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 |
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| | | **Pull from Hub** | Downloads latest weights, re-initializes the predictor |
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| | | **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">
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| |
|
| | **Dr. Nexus** · *Quantitative intelligence, engineered locally.*
|
| |
|
| | </div>
|
| |
|
