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option-pricing-models

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Simple python/streamlit web app for European option pricing using Black-Scholes model, Monte Carlo simulation and Binomial model. Spot prices for the underlying are fetched from Yahoo Finance API.

파일 탐색기

최종 버전 다운로드 (.zip)

- Binomial_Model.gif
- Black_Scholes_Model.gif
- Monte_Carlo_Option_Pricing.gif
- __init__.py
- base.py
- BinomialTreeModel.py
- BlackScholesModel.py
- MonteCarloSimulation.py
- ticker.py
.DS_Store
.gitignore
app.yaml
Dockerfile
option_pricing.py
option_pricing_test.py
README.md
Requirements.txt
streamlit_app.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/just-krivi/option-pricing-models

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd option-pricing-models

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Docker

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.

docker build -t options-pricing:latest .

Dockerfile을 기반으로 실행 가능한 이미지를 빌드합니다.

docker image ls

이 명령어를 터미널에 그대로 입력해 실행하세요.

docker run -p 8080:8080 options-pricing:latest

빌드된 이미지를 실제 컨테이너로 실행합니다.

✅ 터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r Requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# Option Pricing Models

## Introduction
This repository contains a simple web app for calculating European option prices using three different methods:

1. Black-Scholes model
2. Monte Carlo simulation
3. Binomial model

The app is implemented in Python 3.9 and uses the Streamlit library for visualization.

## Option Pricing Methods

### 1. Black-Scholes Model
A mathematical model used to calculate the theoretical price of European-style options, based on factors like current stock price, strike price, time to expiration, risk-free rate, and volatility.

### 2. Monte Carlo Simulation
A probabilistic method that uses random sampling to estimate option prices by simulating multiple possible price paths of the underlying asset.

### 3. Binomial Model
A discrete-time model that represents the evolution of the underlying asset's price as a binomial tree, allowing for the calculation of option prices at different time steps.

## Features

- Fetches latest stock price data from Yahoo Finance API using pandas-datareader
- Caches data using requests-cache to avoid duplicate API calls
- Allows users to input various parameters:
  - Strike price
  - Risk-free rate (%)
  - Sigma (Volatility) (%)
  - Exercise date
- Calculates option prices based on user inputs
- Provides a user-friendly interface for testing different scenarios

## Project Structure

- `demo/`: Contains GIF files demonstrating the Streamlit app
- `option_pricing/`: Python package containing model implementations
- `streamlit_app.py`: Main script for the Streamlit web app
- `Requirements.txt`: List of required Python packages
- `Dockerfile`: Configuration for running the app in a Docker container

## How to Run the App

### Using Docker Locally
The easiest way to run the app is using Docker. Make sure you have Docker installed on your machine before proceeding.

1. Navigate to the repository directory in your terminal.

2. Build the Docker image:
   ```
   docker build -t options-pricing:latest .
   ```

3. Verify the image was built successfully:
   ```
   docker image ls
   ```

4. Run the Docker container:
   ```
   docker run -p 8080:8080 options-pricing:latest
   ```

5. Access the app in your web browser at:
   ```
   http://0.0.0.0:8080/
   ```

### Using Google Cloud

To deploy the Docker container to Google Cloud Platform (GCP), follow these steps:

1. Prerequisites:
   - Have a Google account
   - Create a project on Google Cloud Console
   - Set up billing for your project (be aware of GCP's pricing structure)
   - Install and set up Google Cloud SDK

2. Verify and set your GCP project:
   - Check the current project:
     ```
     gcloud config get-value project
     ```
   - Set a different project if needed:
     ```
     gcloud config set project YOUR_PROJECT_NAME
     ```

3. Deploy the application:
   - Run the following command (uses the app.yaml file in your project):
     ```
     gcloud app deploy
     ```
   - Select the nearest server location when prompted
   - Wait for the deployment process to complete

4. Access your web app:
   - After deployment, you'll receive a URL for your app
   - The URL format will be: https://YOUR_PROJECT_NAME.REGION.r.appspot.com/
   - You can also find this URL in the Google Cloud Console

Note: Ensure you understand GCP's pricing before deploying to avoid unexpected charges.

## Streamlit Web App Demonstrations

### Black-Scholes Model
![black-scholes-demo](https://raw.githubusercontent.com/just-krivi/option-pricing-models/master/media/Black_Scholes_Model.gif)

### Monte Carlo Option Pricing
![monte-carlo-demo](https://raw.githubusercontent.com/just-krivi/option-pricing-models/master/media/Monte_Carlo_Option_Pricing.gif)

### Binomial Model
![binomial-tree-demo](https://raw.githubusercontent.com/just-krivi/option-pricing-models/master/media/Binomial_Model.gif)

By following these instructions, you can easily set up and explore the option pricing models using the Streamlit web app. Feel free to experiment with different parameters and see how they affect the calculated option prices.