AI-Driven Triple Screen Trading System Experiment

TLDR: I experimented with the AI Trading Strategy Builder to develop and test a triple screen trading system. I upgraded to the GPT-3.5-turbo model and simplified the time interval. With the help of ChatGPT4, I generated pseudo code for the trading strategy and tested it under different market conditions. The results varied across assets and market conditions, highlighting the need for continuous testing and optimization in trading strategy development.

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As a trading enthusiast, I recently experimented with the AI strategy builder tool to create and test a triple screen trading system. I wanted to share my experience and results with the LinkedIn community, highlighting the importance of continuous testing, optimization, and adaptability in trading strategy development.

Experiment Setup:

I upgraded the OpenAI model to GPT-3.5-turbo, simplified the time interval using 1h timeframe, and defined trading rules for the triple screen trading system. Then, with the help of ChatGPT 4, I wrote the pseudo code for the strategy, which was use as prompt and subsequently tested using the AI strategy builder.

Triple screen function generated from pseudo code

Experiment Results:

The results from the Bull Market Test showed significant profitability for BTC/USDT and ETH/USDT, while other symbols showed insignificant returns. The overall strategy was profitable in the bull market.

On the other hand, the Bear Market Test showed mixed results, with potential profitability for BTC/USDT and MATIC/USDT, losing strategies for ETH/USDT and ADA/USDT, and insignificant returns for the overall strategy.

Interpretation of Results:

The experiment results demonstrated varying performance across different market conditions and cryptocurrencies. In the bull market, the triple screen trading system showed potential profitability for BTC/USDT and ETH/USDT, while the bear market results were mixed. These findings underline the importance of continuous testing, optimization, and adaptability when developing trading strategies, as well as the need for tailored approaches for different market conditions and assets.

Conclusion:

I encourage you to try the AI strategy builder tool for yourself to develop and test your own trading strategies. The tool can help streamline the strategy creation process, making it more efficient and accessible, even for those without extensive coding or trading experience. By experimenting with different strategies and continuously refining them, you can potentially improve your trading performance and adapt to ever-changing market conditions.