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QuantFlux 3.0 XGBoost Trading Model - HuggingFace Package Contents
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RELEASE DATE: 2025-11-19
MODEL ID: trial_244_xgb
VERSION: 1.0

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DOCUMENTATION FILES
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1. README.md (4.2 KB)
   - Quick start guide
   - Model overview and performance summary
   - Feature descriptions
   - Usage examples
   - Risk disclaimers

2. MODEL_CARD.md (19 KB) - COMPREHENSIVE TECHNICAL DOCUMENTATION
   - Model Summary & Performance Metrics
   - Model Architecture (XGBoost specifics)
   - Training Data Details (2.54B ticks, 5.25 years)
   - All 17 Features with Formulas
   - Model Hyperparameters
   - Input/Output Specifications
   - Validation Results & Confusion Matrix
   - Feature Importance Scores
   - Risk Management Framework
   - Usage Guide with Code Examples
   - Limitations & Disclaimers
   - Performance Interpretation Guide

3. TECHNICAL_ARCHITECTURE.md (29 KB) - COMPLETE SYSTEM DESIGN
   - End-to-End System Overview
   - Dollar Bar Aggregation (algorithm & implementation)
   - Feature Engineering Pipeline (with Python code)
   - Model Training & Optimization (Optuna integration)
   - Signal Generation Logic (entry/exit rules)
   - Risk Management Framework (6-layer enforcement)
   - Data Processing Pipeline
   - Deployment Architecture (AWS specs)
   - Research references

4. FEATURE_FORMULAS.json (7.5 KB) - DETAILED FEATURE SPECIFICATION
   - All 17 feature formulas in mathematical notation
   - Python implementation for each feature
   - Feature importance scores
   - Value ranges and units
   - Feature category classification

5. model_metadata.json (6.6 KB) - MACHINE-READABLE METADATA
   - Model architecture and hyperparameters
   - Training data specifications
   - Performance metrics (forward test + historical)
   - Signal generation parameters
   - Deployment requirements
   - Feature list and order
   - Validation methodology
   - Risk management configuration

6. feature_names.json (2.7 KB) - FEATURE NAME INDEX
   - Feature count and names (in required order)
   - Feature descriptions
   - Feature types (continuous vs binary)
   - Feature importance scores
   - Expected value ranges

7. PACKAGE_CONTENTS.txt (this file)
   - Index of all package contents
   - File descriptions and sizes

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MODEL FILES
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1. trial_244_xgb.pkl (79 MB)
   - Trained XGBoost classifier
   - 2,000 trees, depth=7
   - Binary classification (Buy/Hold)
   - Serialized format: Python pickle
   - Load with: pickle.load(open('trial_244_xgb.pkl', 'rb'))

2. scaler.pkl (983 bytes)
   - StandardScaler for feature normalization
   - Mean=0, Std=1 normalization
   - MUST be used before model prediction
   - Load with: pickle.load(open('scaler.pkl', 'rb'))
   - Apply with: scaler.transform(features)

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CONFIGURATION FILES
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1. .gitattributes
   - Git LFS configuration for large model files
   - Ensures proper handling of 79MB pickle file

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MODEL SPECIFICATIONS
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PERFORMANCE (Forward Test: Aug 18 - Nov 16, 2025)
- Directional Accuracy: 84.38%
- Sharpe Ratio: 12.46
- Win Rate: 84.38%
- Profit Factor: 4.78x
- Max Drawdown: -9.46%
- Total Trades: 224
- Test Duration: 3 months (completely unseen data)

ARCHITECTURE
- Type: XGBoost Binary Classifier
- Framework: xgboost==2.0.3
- Trees: 2,000
- Max Depth: 7
- Learning Rate: 0.1
- Model Size: 79 MB

TRAINING DATA
- Symbol: BTC/USDT perpetual futures
- Ticks: 2.54 billion
- Period: 2020-08-01 to 2025-11-16 (5.25 years)
- Training Samples: 418,410
- Test Samples: 139,467
- Bar Type: Dollar bars ($500k per bar)

FEATURES
- Total Count: 17
- Categories: Price (5), Volume (3), Volatility (2), MACD (1), Time (4), Other (2)
- Look-Ahead Bias: None (all features use minimum 1-bar lag)
- Normalization: StandardScaler (mean=0, std=1)

INPUT SPECIFICATION
- Shape: (N, 17) where N = batch size
- Data Type: float32 preferred
- Scaling: MUST use provided scaler.pkl
- Order: CRITICAL - must match feature_names.json order

OUTPUT SPECIFICATION
- Predictions: Binary (0 or 1)
- Probabilities: Float32 (0.0 to 1.0)
- Confidence Threshold: 0.55 minimum recommended

LATENCY
- Feature Computation: <20ms
- Model Inference: <30ms
- Risk Management: <10ms
- Target Total: <100ms

DEPLOYMENT REQUIREMENTS
- Python: 3.9+
- XGBoost: 2.0.3
- scikit-learn: 1.3.2
- NumPy: 1.20+
- pandas: 1.3+
- Memory: 500MB minimum (model + features)
- Disk: 80MB for model files

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VALIDATION METHODOLOGY
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Walk-Forward Validation:
- Training Window: 3-6 months rolling
- Test Window: 1-2 weeks
- Embargo Period: 10 days between train/test
- Purged K-Fold: 5 folds with temporal awareness
- PBO Score: <0.5 (acceptable threshold <0.7)

Cross-Year Performance:
- 2020: Sharpe 7.61, Win 83.35%, DD -32.05%
- 2021: Sharpe 5.93, Win 82.80%, DD -2.26%
- 2022: Sharpe 6.38, Win 83.18%, DD -2.51%
- 2023: Sharpe 6.49, Win 83.27%, DD -0.21%
- 2024: Sharpe 8.11, Win 84.06%, DD -0.12%

Conclusion: Consistent 83-84% accuracy across all market regimes

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SIGNAL GENERATION
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Trial 244 Configuration:
- Momentum Threshold: -0.9504
- Volume Threshold: 1.5507x
- VWAP Deviation: -0.7815%
- Minimum Signals: 2 of 3 required
- Holding Period: 42 bars (7 days on 4-hour bars)
- Stop Loss: 1.0x ATR
- Take Profit: 1.0x ATR
- Position Size: 1% of capital (scaled by confidence)

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RISK MANAGEMENT
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6-Layer Enforcement:
1. Position Sizing: Max 1% per trade, 10% portfolio max
2. Confidence Threshold: 0.55 minimum
3. Volatility Filter: Halt if >10% 1-min ATR
4. In-Trade Monitoring: Stop-loss and take-profit
5. Daily Loss Limit: -5% maximum per day
6. Drawdown Control: -15% maximum from peak

Position Sizing by Confidence:
- 0.55-0.60: 25% position
- 0.60-0.65: 50% position
- 0.65-0.70: 75% position
- 0.70+: 100% position

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RESEARCH FOUNDATION
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Academic Papers Incorporated:
1. "Geometric Alpha: Temporal Graph Networks for Microsecond-Scale 
   Cryptocurrency Order Book Dynamics"
2. "Heterogeneous Graph Neural Networks for Real-Time Bitcoin Whale 
   Detection and Market Impact Forecasting"
3. "Discrete Ricci Curvature-Based Graph Rewiring for Latent Structure 
   Discovery in Cryptocurrency Markets"

Books Referenced:
- de Prado, M. L. (2018). "Advances in Financial Machine Learning"
- Aronson, D. (2007). "Evidence-Based Technical Analysis"

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USAGE WORKFLOW
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Step 1: Load Model and Scaler
  with open('trial_244_xgb.pkl', 'rb') as f:
      model = pickle.load(f)
  with open('scaler.pkl', 'rb') as f:
      scaler = pickle.load(f)

Step 2: Compute 17 Features
  - ret_1, ret_3, ret_5, ret_accel, close_pos (price)
  - vol_20, high_vol, low_vol (volume)
  - rsi_oversold, rsi_neutral, macd_positive (volatility/macd)
  - london_open, london_close, nyse_open, hour (time)
  - vwap_deviation, atr_stops (additional)

Step 3: Scale Features
  features_scaled = scaler.transform(features.reshape(1, -1))

Step 4: Generate Prediction
  signal = model.predict(features_scaled)[0]
  confidence = model.predict_proba(features_scaled)[0][1]

Step 5: Check Risk Management
  if confidence >= 0.55:
      position_size = calculate_position_size(confidence)
      # Entry signal with sized position

Step 6: Execute and Monitor
  - Entry at current price
  - Stop loss at entry - 1.0x ATR
  - Take profit at entry + 1.0x ATR
  - Exit after 42 bars if no TP/SL

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IMPORTANT DISCLAIMERS
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1. RISK WARNING
   Cryptocurrency futures trading involves extreme risk of total loss.
   Past performance does not guarantee future results.

2. PAPER TRADING REQUIREMENT
   Minimum 4 weeks paper trading REQUIRED before live money deployment.

3. CAPITAL REQUIREMENTS
   Start with 5-10% of total trading capital, not more.
   Never risk more than you can afford to lose.

4. MARKET CONDITIONS
   - Model optimal 13:00-16:00 UTC (London-NYSE overlap)
   - Avoid 21:00-23:00 UTC (42% liquidity drop)
   - Requires retraining every 1-2 weeks for regime adaptation

5. LIMITATIONS
   - BTC/USDT only (not tested on altcoins)
   - Binary classification (no price targets)
   - 4-hour bars optimal (other timeframes untested)
   - Does NOT predict extreme events or crashes

6. NO WARRANTY
   Provided AS-IS without any warranty or guarantee.
   Users assume all responsibility for trading decisions and outcomes.

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FILE SIZES SUMMARY
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trial_244_xgb.pkl       79.0 MB   (Model weights)
MODEL_CARD.md           19.0 KB   (Comprehensive documentation)
TECHNICAL_ARCHITECTURE  29.0 KB   (System design)
model_metadata.json     6.6 KB    (Machine-readable metadata)
FEATURE_FORMULAS.json   7.5 KB    (Feature specifications)
feature_names.json      2.7 KB    (Feature index)
scaler.pkl              983 B     (Feature scaler)
README.md               4.2 KB    (Quick start)
.gitattributes          150 B     (Git LFS config)
PACKAGE_CONTENTS.txt    ~13 KB    (This file)

TOTAL: ~165 MB (primarily model file)

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RECOMMENDED READING ORDER
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1. README.md - Quick overview and usage examples
2. MODEL_CARD.md - Performance metrics and feature descriptions
3. TECHNICAL_ARCHITECTURE.md - System design and implementation
4. FEATURE_FORMULAS.json - Feature computation details
5. model_metadata.json - Hyperparameters and validation results

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SUPPORT & QUESTIONS
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For comprehensive documentation, consult:
- MODEL_CARD.md: Full specifications and usage
- TECHNICAL_ARCHITECTURE.md: Implementation details
- FEATURE_FORMULAS.json: Feature definitions
- model_metadata.json: Metadata and hyperparameters

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VERSION HISTORY
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v1.0 (2025-11-19) - Initial Release
- Trial 244 XGBoost model
- 84.38% accuracy on forward test
- Complete documentation package
- 2,000 trees, 79MB model file
- 17 features, no look-ahead bias

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LICENSE
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Model License: CC-BY-4.0 (Attribution required)
Code License: MIT
Commercial Use: Permitted with attribution
Modification: Encouraged with results sharing

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CONTACT & ATTRIBUTION
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QuantFlux 3.0 Research Team
Released: November 19, 2025
Model: Trial 244 XGBoost (Bayesian optimization, 1,000 trials)
Forward Test: August 18 - November 16, 2025 (Completely unseen)

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END OF PACKAGE CONTENTS
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