My custom library for data science, trading and ML projects
Project description
EMR-Py
Documentation
--
Current Implementation Status
- ✅ Logging utilities
- ✅ ML encoders
- ✅ Telegram trading bot
- ✅ Performance decorators
- 🚧 Time series visualization
- 📋 Planned: GCP utilities, trading indicators, backtesting tools
Library Structure (Tentative)
src/emrpy/
├── __init__.py
├── py.typed
├── decorators.py # General utilities (timer, memory profiling)
├── logging/ # Logging configuration utilities
│ ├── __init__.py
│ └── logger_config.py
├── data/ # data utilities
│ ├── __init__.py
│ └── data_loaders.py
├── ml/ # Machine Learning utilities
│ ├── __init__.py
│ └── encoders.py # Categorical encoding functions
├── visualization/ # Plotting and visualization functions
│ ├── __init__.py
│ └── timeseries.py # Time series plotting utilities
├── timeseries/ # Time series analysis tools
│ ├── __init__.py
│ ├── features.py # Feature engineering
│ └── analysis.py # Time series analysis functions
├── trading/ # Trading-specific utilities
│ ├── __init__.py
│ ├── indicators.py # Technical indicators
│ ├── backtesting.py # Backtesting utilities
│ └── telegrambot.py # Trading notifications via Telegram
├── gcp/ # Google Cloud Platform utilities
│ ├── __init__.py
│ ├── bigquery.py # BigQuery helpers
│ └── storage.py # Cloud Storage helpers
└── finance/ # Financial data utilities
├── __init__.py
└── data_processing.py # Financial data preprocessing
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