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
File size: 1,536 Bytes
2b5698f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | 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
----------
1. V5 spec + scaffolding (this doc + FastAPI scaffold)
2. Realistic backtester improvements
3. Walk-forward validation pipeline
4. Ensemble retrain with robust techniques
5. Live inference + paper-trade adapter
6. 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.
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