Stateful Strategy Optimization

This README provides an overview of the stateful strategy optimization process, which uses Quantiacs libraries to implement and backtest a trading strategy with specific exit conditions. The strategy is optimized using multiple parameters to find the best performing combination.

How to Run the Strategy

In an Online Environment

The strategy can be executed in an online environment using Jupiter or JupiterLab on the Quantiacs personal dashboard. To do this, clone the template in your personal account.

In a Local Environment

To run the strategy locally, you need to install the Quantiacs Toolbox.

Strategy Overview

This strategy uses a stateful approach to manage long and short positions with specific exit conditions. It operates on NASDAQ-100 stocks and employs various indicators to make trading decisions.

Key Features:

• Universe: NASDAQ-100 stocks
• Trading Logic: Positions are adjusted based on calculated signals, with conditional exits for taking profit, stopping loss, and day counting for short positions.
• Indicators Used: Simple Moving Average (SMA), Rate of Change (RoC), Average True Range (ATR), etc.
• State Management: Utilizes the Quantiacs state management system to maintain and update strategy state across different days.

Strategy Components:

1. Strategy Function:
   • Define the strategy function which computes the weights (positions) based on signals and applies exit conditions.
   • The strategy adjusts weights according to the trading logic and exits conditions.
   • Conditional exits are applied to manage risks and capture profits.
2. Data Loading and Preparation:
   • Load stock data using qndata.stocks.load_ndx_data or another function for a different dataset.
3. Optimize Strategy
   • Run the optimization with defined parameter ranges.
4. Analyze Results
   • Extract and visualize the optimization results.
5. Backtesting:
   • Use the multipass backtester to evaluate the strategy performance over historical data.
   • Analyze the results and visualize performance metrics.

Recommendations for Competitive Submissions:

• Limit the amount of exit functions to reduce computational demand.
• Limit the amount of parameters and use larger steps in ranges to avoid overfitting and reduce computational demand.
• Compare notebook statistics with the submission statistics to make sure there are no unintended interactions such as forward-looking.