The video “Agentic AI Trading For Beginners” introduces how to combine agentic AI with trading on platforms like Hyperlquid and Poly Market, guiding beginners through data collection, model training, and setting up autonomous trading agents that adapt strategies in real-time for improved profitability. It emphasizes experimentation and learning, encouraging viewers to use these tools as a fun side hustle while highlighting the growing accessibility and potential of AI-driven trading.
The video titled “Agentic AI Trading For Beginners” serves as an introductory guide to combining agentic AI with trading in markets such as stocks, cryptocurrencies, and prediction markets. The creator explains that this crossover is a great way to learn both AI and finance. They focus on two main platforms, Hyperlquid and Poly Market, recommending these for beginners due to their ease of use and lower barriers compared to fiat currency trading platforms. The video also mentions the recent entry of platforms like Robinhood into this space, signaling growing accessibility.
A key part of the process is data collection, which is essential for training AI models. The creator discusses using APIs from platforms like Hyperlquid to gather historical and real-time data, as well as exploring traditional datasets from sources like Kaggle. They emphasize the importance of tailoring financial models to specific goals and risk tolerances, noting that the AI model should adapt based on the user’s balance and profit targets. The approach involves using a hybrid AI model, leveraging tools like Codex, Claw Opus, and others to analyze data and create trading strategies.
The video demonstrates setting up an autonomous trading agent using Codex on Hyperlquid. The creator shows how to initialize the account, deposit funds, and use a beginner.md file to provide context for the AI. Codex is then used to build a trading framework, execute test trades, and collect relevant market data such as order books and candlestick charts. The AI agent performs backtesting and generates multiple trading hypotheses, selecting strategies based on recent market trends and data analysis.
A standout feature highlighted is the agentic AI’s ability to monitor and adjust trading strategies autonomously in real-time. The AI agent runs continuously, checking market conditions every two minutes and modifying its approach based on new data without human intervention. This dynamic adaptability allowed the agent to switch from a bearish short strategy to a bullish long strategy as market conditions changed, ultimately achieving a small profit during the demonstration. This showcases the potential of agentic AI to manage trades more effectively than static models.
In conclusion, the video provides a practical introduction to agentic AI trading, emphasizing experimentation, learning, and gradual improvement. The creator encourages viewers to explore the tools and platforms discussed, join their Discord community for support, and consider AI trading as a fun side hustle rather than a full-time job. The video successfully demonstrates that with the right setup, beginners can start autonomous AI trading, collect and analyze data, and adapt strategies dynamically to meet financial goals.