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
Upload V5_DESIGN.md with huggingface_hub
Browse files- V5_DESIGN.md +34 -0
V5_DESIGN.md
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V5 Trading System — Design and Roadmap
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Goal
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----
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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.
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Key Requirements
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----------------
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- Realistic backtests with slippage, commissions, partial fills, and order types
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- Walk-forward training & validation to reduce overfitting
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- Modular model artifacts: tree-only + Keras artifacts (same as V4) but with versioning
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- Live inference API with strict risk manager, position sizing, and circuit-breakers
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- Paper-trade adapter to test on real exchange simulator/broker sandbox
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- Monitoring & alerting (P&L, drawdown, latency, model drift)
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Milestones
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----------
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1. V5 spec + scaffolding (this doc + FastAPI scaffold)
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2. Realistic backtester improvements
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3. Walk-forward validation pipeline
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4. Ensemble retrain with robust techniques
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5. Live inference + paper-trade adapter
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6. Monitoring, HF model card, and deployment
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Acceptance Criteria
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-------------------
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- Backtest Sharpe > target (configurable) on OOS sets with realistic costs
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- Max drawdown within risk budget
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- Live inference latency < 100ms per request (p95) on target hardware
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- Automated rollback on model drift or large drawdown
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Notes
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-----
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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.
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