trading-platform-ml-engine-v2

3 Commits 1 Branch 0 Tags 4.5 MiB

Author	SHA1	Message	Date
Adrian Flores Cortes	b0b4a712eb	feat: Integrate Level 0 attention models into prediction pipeline - Add AttentionProvider service to load and serve attention scores - Integrate attention scoring into PredictionService.generate_signal() - Add AttentionInfo dataclass to TradingSignal - Boost confidence by 15% when high flow detected - Add /api/attention/{symbol} endpoint for direct attention queries - Add /api/attention/models endpoint to list loaded models - Add attention field to SignalResponse API model This completes the L0→L1 integration of the hierarchical ML architecture. Attention models identify high-flow market moments to improve signal quality. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-01-25 06:57:59 -06:00
Adrian Flores Cortes	475e913e3c	config: Deprecate MySQL and update PostgreSQL credentials - PostgreSQL credentials updated to trading_user/trading_dev_2026 - MySQL section marked as deprecated (remote server no longer accessible) - Table mappings updated to use market_data PostgreSQL schema - Password redacted from deprecated MySQL config Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-01-25 05:52:50 -06:00
rckrdmrd	75c4d07690	feat: Initial commit - ML Engine codebase Hierarchical ML Pipeline for trading predictions: - Level 0: Attention Models (volatility/flow classification) - Level 1: Base Models (XGBoost per symbol/timeframe) - Level 2: Metamodels (XGBoost Stacking + Neural Gating) Key components: - src/pipelines/hierarchical_pipeline.py - Main prediction pipeline - src/models/ - All ML model classes - src/training/ - Training utilities - src/api/ - FastAPI endpoints - scripts/ - Training and evaluation scripts - config/ - YAML configurations Note: Trained models (.joblib, .pt) are gitignored. Regenerate with training scripts. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-01-18 04:27:40 -06:00

Author

SHA1

Message

Date

Adrian Flores Cortes

b0b4a712eb

feat: Integrate Level 0 attention models into prediction pipeline

- Add AttentionProvider service to load and serve attention scores
- Integrate attention scoring into PredictionService.generate_signal()
- Add AttentionInfo dataclass to TradingSignal
- Boost confidence by 15% when high flow detected
- Add /api/attention/{symbol} endpoint for direct attention queries
- Add /api/attention/models endpoint to list loaded models
- Add attention field to SignalResponse API model

This completes the L0→L1 integration of the hierarchical ML architecture.
Attention models identify high-flow market moments to improve signal quality.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

2026-01-25 06:57:59 -06:00

Adrian Flores Cortes

475e913e3c

config: Deprecate MySQL and update PostgreSQL credentials

- PostgreSQL credentials updated to trading_user/trading_dev_2026
- MySQL section marked as deprecated (remote server no longer accessible)
- Table mappings updated to use market_data PostgreSQL schema
- Password redacted from deprecated MySQL config

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

2026-01-25 05:52:50 -06:00

rckrdmrd

75c4d07690

feat: Initial commit - ML Engine codebase

Hierarchical ML Pipeline for trading predictions:
- Level 0: Attention Models (volatility/flow classification)
- Level 1: Base Models (XGBoost per symbol/timeframe)
- Level 2: Metamodels (XGBoost Stacking + Neural Gating)

Key components:
- src/pipelines/hierarchical_pipeline.py - Main prediction pipeline
- src/models/ - All ML model classes
- src/training/ - Training utilities
- src/api/ - FastAPI endpoints
- scripts/ - Training and evaluation scripts
- config/ - YAML configurations

Note: Trained models (*.joblib, *.pt) are gitignored.
      Regenerate with training scripts.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

2026-01-18 04:27:40 -06:00