https://github.com/stefanciprian/rd
R & D
https://github.com/stefanciprian/rd
arima bayesian fgn flsm higuchi hurst-exponent jupyter-notebook linear-regression lorenz markov pearson python random-forest
Last synced: 2 months ago
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R & D
- Host: GitHub
- URL: https://github.com/stefanciprian/rd
- Owner: stefanciprian
- Created: 2024-10-13T12:30:07.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-10-23T19:13:23.000Z (8 months ago)
- Last Synced: 2024-10-24T05:39:30.868Z (7 months ago)
- Topics: arima, bayesian, fgn, flsm, higuchi, hurst-exponent, jupyter-notebook, linear-regression, lorenz, markov, pearson, python, random-forest
- Language: Jupyter Notebook
- Homepage:
- Size: 2.09 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# R & D
This project focuses on analyzing stock prices, trading volumes, VIX using Python. The analysis is performed using Jupyter Notebooks, providing a comprehensive approach to examining financial data. The project can be used to extract insights from stock market trends by processing and visualizing price and volume data.
## Project Overview
This repository contains Python code for:
- Reading stock prices and trading volume data from CSV files or using yfinance
- Cleaning and preprocessing the data
- Calculating statistical summaries (e.g., moving averages, returns, etc.)
- Visualizing stock price movements and trading volume trends
## Installation
To run the notebooks, follow these steps:
### Clone the repository
```bash
git clone https://github.com/stefanciprian/rd.git
```
### Install the required dependencies
```bash
pip install -r requirements.txt
```
### Run the Jupyter Notebook
```bash
jupyter notebook prices_and_volumes.ipynb
```
## Files
- **prices_and_volumes.ipynb**: The main notebook file containing code for analyzing stock prices and volumes.
- **vix.ipynb**: Notebook with VIX tests.
## Features
- **Load Data**: Load historical stock data from CSV/yfinance.
- **ARIMA**: Implement AutoRegressive Integrated Moving Average for time series forecasting.
- **Bayesian**: Apply Bayesian methods for statistical analysis.
- **Chua's Circuit - Chaotic Algorithm**: Explore chaotic dynamics with Chua's Circuit.
- **Duffing Oscillator - Chaotic Algorithm**: Model chaotic behavior using the Duffing oscillator.
- **Fractional Gaussian Noise (fGn)**: Analyze data with fractional Gaussian noise characteristics.
- **Fractional Lévy Stable Motion (FLSM)**: Model data using fractional Lévy stable motion.
- **Fuzzy Logic**: Apply fuzzy logic for reasoning under uncertainty.
- **Heatmap**: Visualize data correlations and distributions with heatmaps.
- **Hénon Map - Chaotic Algorithm**: Study chaotic patterns with the Hénon map.
- **Higuchi Fractal Dimension**: Calculate the Higuchi fractal dimension for time series.
- **Hurst Exponent**: Estimate the Hurst exponent to assess long-term memory of time series.
- **Ikeda Map - Chaotic Algorithm**: Use the Ikeda map to explore complex chaotic systems.
- **Julia Sets - Chaotic Algorithm**: Visualize fractal structures through Julia sets.
- **Kalman Filter - Linear Quadratic Estimation**: Apply Kalman filtering for state estimation in linear systems.
- **Linear Regression**: Perform linear regression analysis on stock data.
- **Lorenz System**: Analyze the Lorenz system for chaotic behavior.
- **Markov**: Implement Markov models for predictive analysis.
- **Mackey-Glass Equation - Chaotic Algorithm**: Model time series using the Mackey-Glass equation for chaotic behavior.
- **Pearson**: Calculate Pearson correlation coefficients for data relationships.
- **Random Forest**: Use random forest algorithms for classification and regression tasks.
- **Rössler Attractor - Chaotic Algorithm**: Study chaotic dynamics with the Rössler attractor.
- **Standard Map (Chirikov-Taylor Map) - Chaotic Algorithm**: Model chaotic behavior with the standard map (Chirikov-Taylor map).
- **VIX Time Series with Highlighted Trend Changes**: Analyze VIX time series data and highlight significant trend changes for better insights.
- **Monte Carlo Shuffled Projection for VIX**: Use Monte Carlo simulations to create shuffled projections of VIX data, allowing for uncertainty quantification.
- **Monte Carlo Prediction of VIX**: Implement Monte Carlo methods to predict future VIX values based on historical data patterns.
- **MCMC Projections for VIX**: Apply Markov Chain Monte Carlo (MCMC) techniques to generate projections for VIX, enhancing predictive accuracy.
- **Markov Regime Switching Model**: Utilize a Markov regime switching model to identify different market regimes and their impact on VIX.
- **Perturbation Analysis and Future Projections for VIX**: Conduct perturbation analysis to assess the effects of small changes in parameters on future VIX projections.
- **Synthetic Control Method for VIX**: Implement the synthetic control method to estimate the causal effect of interventions on VIX.
- **Projections Using Latin Hypercube Sampling (LHS)**: Use Latin Hypercube Sampling to create projections for VIX, ensuring a more comprehensive exploration of uncertainty.
- **VIX Projections Using Geometric Brownian Motion (GBM)**: Model VIX projections using Geometric Brownian Motion to capture the stochastic nature of financial markets.
- **Recurrence Quantification Analysis (RQA)**: Perform Recurrence Quantification Analysis to study the dynamics of VIX time series and identify patterns.
- **VIX Forecast Using Chaos Neural Network**: Leverage chaos neural networks to forecast VIX, utilizing complex patterns in historical data.
## How to Use
After loading your stock data, the notebook will guide you through various analytical steps such as calculating statistics, applying technical indicators, and visualizing results.
## Contributing
Feel free to contribute to this project by submitting a pull request or reporting an issue.