feat(ui): implement disagreement detection, prediction summary, loading states, and update documentation
- Add disagreement detection logic comparing human annotations vs predictions - Display prediction summary in PredictionPanel (agreements/disagreements) - Wire up 'Show only disagreements' filter toggle - Add loading overlay during prediction fetching - Update docker-compose.yml with healthchecks for all services - Update DEPLOYMENT.md with comprehensive ML service setup instructions - Update README.md with ML pipeline overview and architecture diagrams - Update CLAUDE_DESCRIPTION.md with v3.0.0 ML integration details Remaining tasks (11.2, 11.4, 11.5) deferred - core functionality complete
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Marko Djordjevic
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README.md
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README.md
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@ -4,14 +4,28 @@ A web-based tool for manually annotating candlestick charts with pattern labels
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## Overview
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Candle Annotator provides a TradingView-like charting interface that allows traders and researchers to:
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Candle Annotator is a complete machine learning platform for candlestick pattern recognition, combining:
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**Annotation Tools** - TradingView-like charting interface for creating labeled training data:
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- Upload historical OHLC (Open, High, Low, Close) candle data from CSV files
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- Visualize candlestick charts with interactive zoom and pan
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- Annotate patterns with span labels (e.g., "Bullish Engulfing", "Doji", "Hammer")
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- Mark breakout patterns (Break Up, Break Down) directly on candles
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- Draw custom trend lines with two-click interaction
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- Delete annotations with a dedicated tool
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- Export all annotations as CSV for ML training pipelines
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- Export annotations for ML training
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**ML Pipeline** - Python-based training and inference system:
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- Feature engineering with TA-Lib indicators (RSI, MACD, Bollinger Bands, etc.)
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- Automated pattern detection using TA-Lib CDL* functions
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- Train RandomForest and XGBoost models with MLflow experiment tracking
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- FastAPI inference service for real-time predictions
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- Integration with Next.js UI for prediction visualization
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**Active Learning Loop** - Close the feedback cycle:
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- Model predictions displayed as overlays on the chart
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- Disagreement detection between human annotations and model predictions
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- One-click feedback to confirm, correct, or dismiss predictions as new training data
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- Continuous improvement through iterative annotation and retraining
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## Features
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@ -59,8 +73,117 @@ Candle Annotator provides a TradingView-like charting interface that allows trad
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- **Docker Deployment**: One-command deployment with persistent data volume
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- **Health Check**: Built-in /api/health endpoint for monitoring
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### ML Pipeline Features (Optional)
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The integrated ML pipeline provides:
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#### Feature Engineering
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- **TA-Lib Indicators**: Automatic computation of 150+ technical indicators (RSI, MACD, Bollinger Bands, ATR, Stochastic, etc.)
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- **Candle Features**: Body size, wick ratios, gap detection, price ranges
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- **Custom Features**: Plugin system for domain-specific feature functions
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- **NaN Handling**: Automatic warmup period detection and cleanup
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#### Annotation Ingestion
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- **Windowed Classification**: Extract fixed-size windows around each pattern for classification models
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- **BIO Sequence Labeling**: Begin-Inside-Outside encoding for sequence models (future LSTM/GRU support)
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- **Programmatic Labels**: TA-Lib CDL* pattern functions for auto-labeling (23+ candlestick patterns)
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- **Label Merging**: Human-priority, programmatic-priority, or both strategies
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- **Dataset Statistics**: Class distribution, label counts, human/programmatic agreement metrics
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#### Model Training
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- **Model Types**: RandomForest and XGBoost with class balancing
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- **Temporal Splitting**: Train/val/test splits that respect time series order (no data leakage)
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- **MLflow Integration**: Automatic experiment tracking, hyperparameter logging, artifact storage
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- **Model Registry**: Versioned model storage with stage management (Production, Staging, Archived)
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- **Evaluation Metrics**: Accuracy, F1 (macro/weighted), per-class precision/recall/F1
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- **Visualization**: Confusion matrix, feature importance plots, classification reports
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#### Inference Service
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- **FastAPI REST API**: High-performance inference with automatic OpenAPI docs
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- **Preprocessing Parity**: Loads pipeline config from MLflow to ensure training/inference consistency
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- **Batch Processing**: Efficient prediction for large time ranges
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- **Span Grouping**: Consecutive predictions merged into labeled spans with confidence scores
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- **Model Metadata**: Endpoint to query model version, metrics, and label configuration
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#### Prediction UI
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- **Chart Overlay**: Predictions rendered as histogram series with label-specific colors
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- **Confidence Filtering**: Slider to hide low-confidence predictions
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- **Label Filtering**: Toggle visibility per pattern type with per-class F1 scores
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- **Disagreement Detection**: Automatic comparison of human vs model predictions
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- **Prediction Summary**: Counts for total predictions, agreements, disagreements
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- **Active Learning Feedback**: Click predictions to convert them to annotations (future feature)
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## Architecture
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### System Components
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Web Browser │
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│ ┌───────────────────────────────────────────────────────────┐ │
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│ │ Next.js Frontend (React 19, Tailwind, lightweight-charts)│ │
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│ │ - Annotation tools │ │
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│ │ - Prediction visualization │ │
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│ └──────────────────┬──────────────────────────────────────────┘ │
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└─────────────────────┼──────────────────────────────────────────────┘
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│ HTTP
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▼
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┌─────────────────────────────────────────────────────────────────┐
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│ Next.js API Routes (TypeScript) │
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│ - /api/candles, /api/annotations, /api/span-annotations │
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│ - /api/predict (proxy) │
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│ - /api/model/info (proxy) │
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│ └───────────┬─────────────────────────────────────┬─────────── │
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│ │ SQLite (annotations) │ HTTP │
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│ ▼ ▼ │
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│ ┌──────────────────┐ ┌──────────────────────┐ │
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│ │ SQLite Database │ │ ML Inference API │ │
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│ │ (Drizzle ORM) │ │ (FastAPI, Python) │ │
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│ └──────────────────┘ └──────────┬───────────┘ │
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└────────────────────────────────────────────────────┼──────────────┘
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│
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┌──────────────────────────────┴───────────────┐
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│ │
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┌───────────▼─────────┐ ┌──────────────▼────────┐
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│ MLflow Server │ │ PostgreSQL │
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│ (Experiments, │ │ (Training run │
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│ Model Registry) │ │ metadata) │
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└─────────────────────┘ └───────────────────────┘
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```
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### ML Pipeline Workflow
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```
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1. Annotate Data (Web UI)
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↓
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2. Export Annotations (JSON)
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↓
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3. Feature Engineering (TA-Lib)
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├─ Raw OHLCV → Enriched CSV (with indicators)
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↓
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4. Annotation Ingestion
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├─ Annotations + Enriched CSV → Labeled Dataset
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├─ Optional: TA-Lib CDL* auto-labeling
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↓
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5. Model Training
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├─ Temporal train/val/test split
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├─ RandomForest or XGBoost training
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├─ MLflow experiment tracking
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├─ Model registration
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↓
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6. Inference Service
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├─ Load model from MLflow registry
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├─ Serve predictions via FastAPI
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↓
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7. Prediction Visualization (Web UI)
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├─ Display predictions on chart
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├─ Detect disagreements
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├─ Feedback loop: predictions → new annotations → retrain
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```
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## Tech Stack
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### Frontend & Web Service
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- **Frontend**: Next.js 16 (App Router), React 19, TypeScript
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- **Styling**: Tailwind CSS 3, shadcn/ui components
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- **Charting**: lightweight-charts 4.x (TradingView)
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@ -70,6 +193,17 @@ Candle Annotator provides a TradingView-like charting interface that allows trad
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- **ORM**: Drizzle ORM
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- **CSV Parsing**: papaparse
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### ML Pipeline (Python)
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- **API Framework**: FastAPI with uvicorn
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- **ML Libraries**: scikit-learn (RandomForest), XGBoost
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- **Feature Engineering**: TA-Lib (Technical Analysis Library)
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- **Data Processing**: pandas, numpy
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- **Experiment Tracking**: MLflow (model registry, artifact storage)
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- **Data Versioning**: DVC (Data Version Control)
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- **Database**: PostgreSQL 16 (training run metadata)
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- **Model Persistence**: joblib
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- **Validation**: Pydantic
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## Getting Started
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### Docker Quickstart (Recommended)
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