Scikit-learn
English
trading
finance
xauusd
gold
forex
machine-learning
ensemble
super-ensemble
xgboost
lightgbm
catboost
neural-network
tensorflow
Instructions to use JonusNattapong/romeo-v8-super-ensemble-trading-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use JonusNattapong/romeo-v8-super-ensemble-trading-ai with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("JonusNattapong/romeo-v8-super-ensemble-trading-ai", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
V5 Trading System — Design and Roadmap
Goal
Build a production-ready V5 trading system for XAUUSD that is robust to market costs and suitable for live paper/live trading. Focus on risk controls, walk-forward validation, realistic fills, and a live inference API.
Key Requirements
- Realistic backtests with slippage, commissions, partial fills, and order types
- Walk-forward training & validation to reduce overfitting
- Modular model artifacts: tree-only + Keras artifacts (same as V4) but with versioning
- Live inference API with strict risk manager, position sizing, and circuit-breakers
- Paper-trade adapter to test on real exchange simulator/broker sandbox
- Monitoring & alerting (P&L, drawdown, latency, model drift)
Milestones
- V5 spec + scaffolding (this doc + FastAPI scaffold)
- Realistic backtester improvements
- Walk-forward validation pipeline
- Ensemble retrain with robust techniques
- Live inference + paper-trade adapter
- Monitoring, HF model card, and deployment
Acceptance Criteria
- Backtest Sharpe > target (configurable) on OOS sets with realistic costs
- Max drawdown within risk budget
- Live inference latency < 100ms per request (p95) on target hardware
- Automated rollback on model drift or large drawdown
Notes
Start from existing V4 codebase for feature engineering and model saving. Reuse safe serialization approach. Incrementally add safety features and validate each milestone with tests.