📈 Turbo Options: High-Frequency Options Pricing Engine

Welcome to the High-Frequency Options Pricing Engine! This project is designed to provide a comprehensive and efficient solution for pricing options using various models, including Monte Carlo simulation and Finite Difference Methods (FDM).

🌟 Key Features

• Option Pricing Models: Implements Monte Carlo simulation and Finite Difference Methods (FDM) for accurate options pricing.
• Market Data Handling: Efficiently fetches and updates market data required for pricing.
• Math Utilities: Provides essential mathematical functions for financial calculations.
• Unit Testing: Includes a simple test framework to verify the correctness of the Option class.

🔧 Under the Hood

graph LR
A[Market Data Handling] --> B[Option Pricing]
B --> C[Monte Carlo Simulation]
B --> D[Finite Difference Methods]

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1. Market Data Handling: Fetches and updates market data, including underlying prices, interest rates, dividend yields, and implied volatilities.
2. Option Pricing: Prices options using Monte Carlo simulation and Finite Difference Methods (FDM).
3. Monte Carlo Simulation: Simulates many random paths for the underlying asset price, calculates the option payoff for each path, and takes the average.
4. Finite Difference Methods: Solves the Black-Scholes PDE using numerical methods to get the option price at each point in a grid.

🚀 Getting Started

Before getting started, please ensure you have the following:

• A C++ compiler (I tested with g++)
• CMake for building Google Test (if you want to use it)
• Basic understanding of options pricing and financial models

📥 Installation

Follow these steps to set up the project:

# Clone the repository
git clone https://github.com/yourusername/OptionsPricingEngine.git

# Navigate to the project directory
cd OptionsPricingEngine

# Create the bin directory for output binaries
mkdir -p bin

# Build the project
make

🔑 Market Data

The project requires market data to function properly. Create a data/market_data.txt file with the necessary market data:

interestRate 0.02
dividendYield 0.00
AAPL 150.0
volatility_AAPL 0.30

🎯 Usage

To run the options pricing engine, create an options.txt file with the options to be priced:

AAPL 155 1672531199 call
AAPL 145 1672531199 put

Run the executable with the options.txt file as an argument:

./bin/pricer data/options.txt

📊 Results

The output will display the prices of the options using different models:

Option: AAPL, Strike=155, Expiry=1672531199
Monte Carlo price: $4.12
FDM price: $4.09
Black-Scholes PDE price: $4.10
Option: AAPL, Strike=145, Expiry=1672531199
Monte Carlo price: $4.12
FDM price: $4.09
Black-Scholes PDE price: $4.10

🧪 Testing

To run the tests, build the test executable and run it:

make
./bin/test_option

The output will display the results of the tests:

Testing Intrinsic Value:
Call Option (160): Expected 10, Got 10
Call Option (140): Expected 0, Got 0
Put Option (140): Expected 10, Got 10
Put Option (160): Expected 0, Got 0
Testing Time Value:
Call Option: Expected 10, Got 10
Put Option: Expected 10, Got 10

🔮 Future Enhancements

• Automated market/options data scraping and sourcing pipeline
• Usage of GPU and parallelization, Boost.Math and Eigen
• Integration of additional pricing models

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options pricing is kind of fun! 🚀