Bitcoin price analysis

Getting started

• If using venv

Initialise

python3 venv -m ~/.venv

Activate

source ~/.venv/bin/activate

• Generate model

python3 gen.py

• Run model

python3 main.py

NOTE: the first time downloading the csv data you will need to do some manual formatting. Maybe i'll automate this step one day.

Research

[] Gradient boosting machines (XGBoost, LightGBM or CatBoost), these models are good for regression tasks, predicting future prices, or understanding feature importance.

[] Deep learning (LSTM or GRUs), specialised for sequential data. Great for capturing long-term dependencies and complex patterns in price data. Particularly useful with large datasets.

[] Recurrent neural nets (temporal data, RNNs and variations). Designed for handling time series data and sequential dependencies effectively.

[] GANS (Generative Adversarial Networks) can be used to simulate and generate synthetic price data for testing trading strategies and risk analysis.

[] Bayesian machine learning - can help in probabalistic modeling, providing a range of possible outcomes and quantifying uncertainty in predictions. Especially good for volatile assets like Bitcoin.

Resources

• machine-learning-for-trading.