OpenOptionsTrading

Python program to choose the best Option trading stratgey based on risk/reward criteria.

Algorithms for Options Trading

This project utilizes the Black-Scholes model and market data from Yahoo Finance (yfinance) to analyze different options trading strategies. Strategies include low-risk short wait, low-risk long wait, and high-risk short wait options strategies.

Here is a table with risk, reward, time, and a generated title based on the cell values:

Risk	Reward	Time
Low	Good	Short
Low	Better	Long
High	Best	Short

Remember to handle edge cases and input validation appropriately when integrating this function into your trading system.

Installation

Ensure you have Python 3.6+ installed. Clone this repository and install the required dependencies:

git clone <repository-url>
cd <repository-directory>
pip install -r requirements.txt

Dependencies include yfinance, numpy, pandas, and scipy.

Usage

Import the necessary libraries and define the functions from the project:

import yfinance as yf
import numpy as np
import pandas as pd
from scipy.stats import norm

# Define the functions here...

Running the Strategies

To execute a strategy, first set your parameters:

underlying_symbol = "MSFT"
expiry_date = "2024-04-26"
risk_free_rate = 0.02

Then, run the desired strategy function:

# Low risk, short wait strategy
low_risk_short_options, low_risk_short_return = low_risk_short_wait(underlying_symbol, expiry_date, risk_free_rate)
print("Low Risk, Short Wait Options:", low_risk_short_options)
print("Expected Return:", low_risk_short_return)

# For low risk long wait and high risk short wait, uncomment and run similar lines

Important Notes

This project uses real-time market data from Yahoo Finance. Ensure you are connected to the internet.
The expiry_date format should be YYYY-MM-DD.
Adjust risk_free_rate according to the current risk-free interest rate environment.

Contributing

Contributions are welcome. Please open an issue first to discuss what you would like to change or add.