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

git clone https://github.com/stefanciprian/rd.git

Install the required dependencies

pip install -r requirements.txt

Run the Jupyter Notebook

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.