This repository is dedicated to the analytical study of Bitcoin (BTC) price behavior across different time intervals. The project aims to statistically analyze price movements using various machine learning techniques, including neural networks, and traditional trading indicators. This is an ongoing project and is subject to changes.
- To understand the statistical properties of BTC price movements.
- To evaluate the effectiveness of various trading indicators like MA, ADX, RSI, etc.
- To experiment with different neural network architectures and training approaches for predicting price movements.
- Statistical analysis of BTC price movements.
- Implementation of Simple Moving Average (SMA) strategy for baseline comparison.
- Extensive work with neural networks, including different architectures and training approaches.
- Use of various trading indicators like MA, ADX, RSI, etc., for feature engineering in neural network models.
- 5min_NN_lag_rsi_adx.ipynb: Neural network-based analysis with 5-minute intervals, using lag, RSI, and ADX indicators.
- BTC_1h_NN.ipynb: Neural network-based analysis with 1-hour intervals.
- BTC_1min_1h.ipynb: Analysis focusing on 1-minute and 1-hour intervals.
- BTC_DAYLY_NN.ipynb: Neural network-based analysis with daily intervals.
- SMA_back_test.py: Backtesting script using Simple Moving Average (SMA) strategy as a baseline.
- backtest.py: General backtesting script.
- get_data.py: Script to retrieve Bitcoin price data.
This project is for educational and research purposes only. Use the trading strategies and analyses at your own risk.