Algorithmic Trading Backend

A comprehensive Python-based financial analysis tool that provides data models for stocks and options, including pricing, statistical analysis, and visualization capabilities.

Features

Data Models

• Base Data: Historical price data for any ticker
• Stock Data: Extended analysis including log returns and volatility calculations
• Option Data: Options pricing with Black-Scholes model and Greeks calculation
  • Support for both Call and Put options
  • Automatic expiration date matching (finds closest available date)
  • Real-time risk-free rate fetching from treasury data

Analysis Capabilities

• Historical stock price data via Yahoo Finance API
• Log returns and annualized volatility calculations
• Option pricing using Black-Scholes model
• Greeks calculation (Delta, Gamma, Vega, Theta, Rho)
• Statistical metrics and price analysis

Visualizations

• Price charts with customizable timeframes
• Returns distribution histograms
• Volatility analysis with 30-day rolling windows
• Cumulative returns tracking
• Comprehensive summary dashboard combining all metrics

Requirements

• Python 3.x
• yfinance
• pandas
• numpy
• matplotlib
• scipy

Installation

pip install yfinance pandas numpy matplotlib scipy

Usage

Run the main script to execute all three test cases (Base Data, Stock Data, and Option Data):

python main.py

The script will automatically:

1. Load base price data for AAPL and display a price chart
2. Load stock data with volatility analysis and display a comprehensive dashboard
3. Load option data with Greeks calculation and pricing

Customizing Parameters

Modify the test configuration in main.py:

ticker = "AAPL"
start_date = datetime(2024, 1, 1)
end_date = datetime(2025, 10, 31)

Loading Different Data Types

from data.data_factory import DataLoader
from datetime import datetime

# Load base data
base_data = DataLoader.load_data(
    ticker="AAPL",
    start_date=datetime(2024, 1, 1),
    end_date=datetime(2024, 12, 31),
    data_type="base"
)

# Load stock data with volatility
stock_data = DataLoader.load_data(
    ticker="AAPL",
    start_date=datetime(2024, 1, 1),
    end_date=datetime(2024, 12, 31),
    data_type="stock"
)

# Load option data (automatically finds closest expiration)
option_data = DataLoader.load_data(
    ticker="AAPL",
    start_date=datetime(2024, 1, 1),
    end_date=datetime(2024, 12, 31),
    data_type="option",
    option_type="call",  # or "put"
    expiration_date="2025-01-17"  # or None for first available
)

Project Structure

algo_backend/
├── main.py                 # Entry point - runs 3 test cases with visualizations
├── data/                   # Data models and loaders
│   ├── base_data.py       # BaseData class - basic price data model
│   ├── stock_data.py      # StockData class - extends BaseData with returns/volatility
│   ├── option_data.py     # OptionData class - option pricing and Greeks
│   ├── data_factory.py    # DataLoader factory for creating data objects
│   └── download_data.py   # Yahoo Finance API integration
├── graph/                  # Visualization modules
│   └── graph_model.py     # GraphModel class - all charting functionality
└── README.md              # This file

Data Models

BaseData

• Basic price data container
• Properties: get_prices, get_current_price, get_ticker, get_type

StockData (extends BaseData)

• Adds log returns and volatility calculations
• Properties: get_log_returns, get_volatility, get_prices_stats()
• Automatically calculates annualized volatility (252 trading days)

OptionData (extends StockData)

• Black-Scholes option pricing model
• Greeks calculation: Delta, Gamma, Vega, Theta, Rho
• Properties: strike_price, expiration_date, risk_free_rate, time_to_maturity
• Methods: get_option_price(), get_greeks_dict(), get_option_info()

Example Output

Statistical Metrics

• Current price
• Price mean and standard deviation
• Return mean and standard deviation
• Annualized volatility
• Number of data points

Option Metrics

• Strike price
• Expiration date and time to maturity
• Risk-free rate (fetched from ^IRX - 13 Week Treasury Bill)
• Option price (Black-Scholes)
• All Greeks (Delta, Gamma, Vega, Theta, Rho)

Visualizations

• Multi-panel dashboard with price, returns, cumulative returns, and volatility
• Individual charts for detailed analysis
• Customizable figure sizes and time periods

Code Style

• Indentation: 4 spaces (no tabs)
• Python version: 3.x
• Type hints: Using dataclasses for data models