finance-analysis-project 0.1.0

Tools for real-time market data analysis and financial modeling

Financial Data Analysis Package

Overview

This project provides a Python package for analyzing financial data, including tools for:

• Fetching and processing market data.
• Building correlation networks.
• Performing PCA and detecting market regimes.
• Calculating portfolio risk metrics.

Additionally, an interactive web application is available for exploring the data.

1. Data Scaffolding:

   • Fetch historical market data for multiple securities using real-time data providers
   • Process and clean financial data for analysis
   • Calculate daily returns and other key metrics
   • Handle missing data and outliers in financial time series

2. Data Navigation:

   • Build correlation networks to visualize relationships between securities.
   • Identify connected components and query paths in the network.

3. Data Analysis:

   • Perform PCA to extract principal factors driving market movements.
   • Detect market regimes using Hidden Markov Models (HMM).
   • Calculate portfolio risk metrics (VaR, CVaR, volatility).

4. Visualization:

   • Visualize correlation networks with interactive stock selection
   • Dynamic dashboard for exploring financial data relationships
   • Interactive PCA component visualization with clustering
   • Market regime timeline visualization with color-coded periods
   • Risk metrics visualization with comparative analysis tools

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Installation

From PyPI

Install the package directly from PyPI:

pip install financial-analysis-project

From Source

Clone the repository and install the package:

git clone https://github.com/hsph-bst236/midterm-project-bytewise236.git
cd midterm-project-bytewise236
pip install -r requirements.txt

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Usage

1. Data Scaffolding

Fetch Market Data

from financial_analysis_project import RealTimeMarketData

# Initialize the market data handler
market_data = RealTimeMarketData(source="yahoo")

# Add tickers to track
market_data.add_tickers(['AAPL', 'MSFT', 'GOOGL'])

# Fetch historical data
from datetime import datetime, timedelta
end_date = datetime.now()
start_date = end_date - timedelta(days=365)  # 1 year of data

# Get historical price data
price_data = market_data.fetch_market_data(
    start_date=start_date,
    end_date=end_date,
    fields=['Close'],
    frequency='daily'
)

# Calculate returns
daily_returns = market_data.calculate_returns(price_data, method='simple')

2. Data Navigation

Build Correlation Network

from financial_analysis_project import build_correlation_network, connected_component, path_query

# Build a correlation network with threshold 0.5
G = build_correlation_network(daily_returns, threshold=0.5)

# Find connected components
components = connected_component(G)

# Check if there's a path between two stocks
is_connected = path_query(G, 'AAPL', 'MSFT')

3. Data Analysis

Perform PCA

from financial_analysis_project import perform_pca
principal_components, explained_variance_ratios, factor_loadings = perform_pca(daily_returns)

Detect Market Regimes

from financial_analysis_project import detect_market_regimes
regimes, transition_probabilities = detect_market_regimes(daily_returns, n_regimes=3)

Calculate Risk Metrics

from financial_analysis_project import calculate_risk_metrics
weights = [0.2, 0.3, 0.5]
risk_metrics = calculate_risk_metrics(daily_returns, weights)
print(risk_metrics)

4. Interactive Visualization

The interactive dashboard provides a comprehensive visual interface to explore stock market data:

# Run the interactive dashboard
python app.py

Dashboard features include:

• Network visualization with adjustable correlation thresholds
• Interactive stock selection and information display
• Real-time data integration and historical charts
• PCA analysis visualization and factor loading exploration
• Market regime detection with color-coded timeline
• Risk metrics comparison with portfolio analysis tools

To generate static visualizations for use with GitHub Pages:

# Generate visualization files
python DA_visualization_demo.py

# Build static site
python build_static.py

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Interactive Website to Explore Data

Explore the data interactively through our web application. The app allows you to:

• Visualize stock correlation networks and discover relationships
• Explore PCA results and factor loadings
• Detect and analyze market regimes
• Compare risk metrics across different portfolios
• Monitor real-time stock data and historical patterns

Click here to access the web

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Contributions

Bella Qian: Data Scaffolding + Interactive Visualization

Suhwan Bong: Data Analysis + publishing the package on PyPI

Yibin Xiong: Data Navigation