feat: Phase 4c-bis — CNN image-based (analyse visuelle graphiques chandeliers)

## Nouveaux modules

### src/ml/cnn_image/
- chart_renderer.py : CandlestickImageRenderer — OHLCV → images 128×128 RGB (mplfinance)
  Fond #0d1117, bougies vertes/rouges, volume, sans axes, rendu en mémoire
  Fallback 2D si mplfinance absent
- cnn_image_model.py : CandlestickCNN — Conv2D 4-blocs (3→32→64→128→256) + AvgPool + Dense(3)
- cnn_image_strategy_model.py : CNNImageStrategyModel — même interface que MLStrategyModel

### src/strategies/cnn_image_driven/
- cnn_image_strategy.py : CNNImageDrivenStrategy(BaseStrategy), SL/TP ATR, seq_len=64

## Modifications

- ensemble_model.py : attach_cnn_image(), poids XGB=0.30/CNN1D=0.30/CNNImage=0.40
- trading.py : POST /train-cnn-image, GET /train-cnn-image/{id}, GET /cnn-image-models
- docker/requirements/api.txt : mplfinance>=0.12.10b0, Pillow>=10.0.0

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

docker/requirements/api.txt

@@ -29,3 +29,7 @@ joblib>=1.3.0
 
 # ML — Deep Learning (CNN pour patterns chandeliers)
 torch>=2.0.0
+
+# Visualisation — graphiques financiers et traitement d'images
+mplfinance>=0.12.10b0
+Pillow>=10.0.0