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Testing 'Deep Seek' to automate trading strategies revealed its potential and areas needing refinement. The analysis compared Deep Seek with ChatGPT, focusing on their ability to generate profitable trading strategies. While Deep Seek showed promise with day trading strategies, ChatGPT had fewer coding errors and better library integration. The findings indicated that although both tools could generate strategies, neither could produce profitable ones straight out of the box, emphasizing the need for thorough testing and validation of AI-generated strategies.
Comparing profitability of strategies generated by Deep Seek and ChatGPT.
Deep Seek revealed more coding errors than ChatGPT in generated strategies.
The comparison between Deep Seek and ChatGPT highlights vital gaps in AI's ability to generate market-ready trading code. Successful automated systems require precise coding and optimal library integration, which clearly separates advanced AI tools like ChatGPT from nascent technologies. For instance, the reliance on correct library naming conventions significantly impacted Deep Seek's performance, reflecting broader themes in AI-driven finance, where traditional quantitative principles must adapt.
The video emphasizes using these systems through platforms like Ninja Trader.
Analyzing the importance of proper library use was critical in both AI evaluations.
It plays a central role in testing the strategies generated by the AI tools in the video.
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