OptionsTraderAI-AI for options trading analysis

Example

Creating an Iron Condor strategy based on volatility levels and market conditions using Python scripts and real-time data feeds.

Scenario

A trader is expecting low volatility in the market and wants to profit from time decay. OptionsTraderAI helps set up the Iron Condor by calculating optimal strike prices and expiration dates using statistical models, backtesting the strategy over historical data, and visualizing the profit-loss graph.

Example

Using libraries like Zipline Reloaded and Optopsy to backtest an algorithm that executes options trades based on moving averages and momentum indicators.

Scenario

A user wants to automate an options strategy that buys calls when the stock price crosses the 50-day moving average and sells when it drops below the 200-day moving average. OptionsTraderAI enables the user to write the algorithm, backtest it using historical data, and evaluate the strategy's performance metrics such as Sharpe ratio, maximum drawdown, and win/loss ratio.

Example

Using Riskfolio-Lib to optimize a portfolio of options and underlying assets for risk-adjusted returns based on the user's risk tolerance.

Scenario

A portfolio manager wants to diversify their portfolio by adding options positions. They use OptionsTraderAI to run Monte Carlo simulations on potential portfolios, calculate VaR (Value at Risk), and apply Modern Portfolio Theory (MPT) to recommend the best asset allocation for maximizing returns at a given risk level.