This repository contains a Jupyter notebook that delves deep into financial analysis using Python. The notebook begins with an initial exploration of financial data and its basic visual representation. Subsequently, various technical indicators are implemented, followed by the introduction of several trading strategies.
The following technical indicators have been implemented in the notebook:
- MACD (Moving Average Convergence Divergence): A trend-following momentum indicator.
- ATR (Average True Range): Measures market volatility.
- Bollinger Bands: A volatility indicator.
- RSI (Relative Strength Index): Measures the speed and change of price movements.
- ADX (Average Directional Index): Used to determine the strength of a trend.
- Renko: Price movement visualization.
- CAGR (Compound Annual Growth Rate): Represents the geometric progression ratio.
- Volatility Measurement: A statistical measure of the dispersion of returns.
- Sharpe & Sortino Ratios: Measures the risk-adjusted performance.
- Maximum Drawdown & Calmar Ratio: Evaluates the risk of investment strategies.
The notebook discusses and evaluates the following trading strategies:
- Portfolio Rebalancing
- Resistance Breakout
- Renko & OBV
- Renko & MACD
- Ensure you have Jupyter Notebook or Jupyter Lab installed.
- Clone this repository.
- Navigate to the project directory and launch Jupyter.
- Open the
Yash_Kristal_shortened_output.ipynbnotebook. - Run the cells to see the analysis and results.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent