lowkey_backtest/pyproject.toml

[project]
name = "lowkey-backtest"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
    "numba>=0.63.1",
    "python-dotenv>=1.2.1",
    "requests>=2.32.5",
    "scikit-learn>=1.8.0",
    "ta>=0.11.0",
    "xgboost>=3.1.2",
]
Initial project setup with Python version 3.12, main script for backtesting trading strategies, and configuration files for project management. Added necessary dependencies and documentation structure. 2025-08-05 12:08:59 +08:00			`[project]`
			`name = "lowkey-backtest"`
			`version = "0.1.0"`
			`description = "Add your description here"`
			`readme = "README.md"`
			`requires-python = ">=3.12"`
Enhance backtesting functionality by adding date range parameters to load_data, improving ATR calculation, and refining trade logic with meta Supertrend signals. Update README with detailed usage instructions and requirements. Add CSV logging for trade results and performance metrics. Include ta library as a dependency in pyproject.toml. 2025-08-12 10:33:17 +08:00			`dependencies = [`
Add MVRV strategy backtesting module with feature engineering and on-chain data integration. Implement model training and evaluation pipeline, including probability threshold analysis. Update configuration for strategy parameters and enhance logging for trade results. Include instructions for running the backtest and preparing data. 2026-01-10 06:10:35 +08:00			`"numba>=0.63.1",`
			`"python-dotenv>=1.2.1",`
			`"requests>=2.32.5",`
			`"scikit-learn>=1.8.0",`
Enhance backtesting functionality by adding date range parameters to load_data, improving ATR calculation, and refining trade logic with meta Supertrend signals. Update README with detailed usage instructions and requirements. Add CSV logging for trade results and performance metrics. Include ta library as a dependency in pyproject.toml. 2025-08-12 10:33:17 +08:00			`"ta>=0.11.0",`
Add MVRV strategy backtesting module with feature engineering and on-chain data integration. Implement model training and evaluation pipeline, including probability threshold analysis. Update configuration for strategy parameters and enhance logging for trade results. Include instructions for running the backtest and preparing data. 2026-01-10 06:10:35 +08:00			`"xgboost>=3.1.2",`
Enhance backtesting functionality by adding date range parameters to load_data, improving ATR calculation, and refining trade logic with meta Supertrend signals. Update README with detailed usage instructions and requirements. Add CSV logging for trade results and performance metrics. Include ta library as a dependency in pyproject.toml. 2025-08-12 10:33:17 +08:00			`]`