## Why

The annotation tool currently creates labeled datasets but has no way to train models on them or get predictions back. Adding a Python ML backend closes the loop: annotations become training data, models produce predictions, and predictions guide further annotation — creating an active learning cycle for candlestick pattern recognition.

## What Changes

- Add a Python service (`services/ml/`) alongside the existing Next.js app, using FastAPI for the REST API
- Implement TA-Lib-based candlestick pattern recognition to auto-generate annotations programmatically
- Build a configurable ML training pipeline (feature engineering → annotation ingestion → training → evaluation) with MLflow tracking and DVC for data versioning
- Support multiple model types: RandomForest and XGBoost initially, with architecture ready for LSTM/GRU and transformer-based models later
- Serve trained models via a FastAPI inference API that accepts OHLCV candles and returns pattern predictions with confidence scores
- Add Next.js API proxy routes (`/api/predict`, `/api/predict/batch`, `/api/model/info`) to connect the frontend to the Python backend
- Add prediction visualization layer on the chart (distinct from human annotations) with confidence filtering and disagreement detection
- Add a prediction controls panel for toggling predictions, filtering by label/confidence, and viewing per-class model metrics
- Implement a feedback loop: users can confirm, correct, or dismiss model predictions as new annotations

## Capabilities

### New Capabilities

- `feature-engineering`: TA-Lib indicator computation and candle feature extraction from raw OHLCV data, producing enriched datasets for training and inference
- `annotation-ingestion`: Converting span annotations (human and programmatic) into labeled training datasets with BIO or windowed encoding, including TA-Lib CDL* pattern auto-labeling
- `ml-training`: Configurable model training pipeline with temporal splits, class balancing, MLflow experiment tracking, artifact logging, and model registry integration
- `ml-inference`: REST API serving trained models — accepts OHLCV candles, runs preprocessing, returns predictions with confidence scores and model metadata
- `prediction-ui`: Frontend prediction layer with chart visualization, controls panel, confidence filtering, disagreement detection, and feedback loop for active learning

### Modified Capabilities

- `backend-api`: New proxy routes (`/api/predict`, `/api/predict/batch`, `/api/model/info`) added to forward requests to the Python inference service
- `span-annotation`: Span export format consumed by the ML pipeline for training; prediction-confirmed spans can be saved as new annotations

## Impact

- **New dependencies**: Python 3.11+, FastAPI, uvicorn, scikit-learn, XGBoost, TA-Lib (C library + Python wrapper), MLflow, DVC, pandas, numpy, joblib
- **New service**: Python FastAPI service running on port 8001, needs to be added to docker-compose
- **Data flow**: Annotation JSON/CSV exports feed into Python pipeline; inference results flow back to the frontend via Next.js proxy routes
- **Infrastructure**: MLflow tracking server (port 5000), DVC remote storage for dataset versioning
- **Existing code changes**: New API routes in Next.js, new React components for prediction panel, chart overlay modifications for prediction rendering
- **Config**: Pipeline YAML config (`config/pipeline.yaml`) controls all ML stages; env vars for inference API URL and feature flags