It is now easy to ask AI to write a quant strategy.
Users can request a moving average strategy, RSI strategy, grid strategy, or even backtest code.
But being able to generate code does not mean the strategy can make money.
What AI Is Good At
AI can help:
- Explain strategy ideas
- Generate code structure
- Write backtest scripts
- Adjust parameters
- Organize review reports
This lowers development barriers.
What AI Cannot Solve Automatically
AI cannot automatically answer:
- Does the strategy really have an edge?
- Is the data reliable?
- Is the backtest biased?
- What about live slippage and fees?
- Can the user keep executing through losses?
- When should the strategy be stopped?
These still require judgment and validation.
The Biggest Risk
The biggest risk is treating AI-generated code as a reliable strategy.
AI can produce code that looks correct, while the strategy logic may be weak and the backtest unrealistic.
If users go live immediately, they bear the trial-and-error cost.
How Ordinary Users Should Use AI
A better approach:
- Use AI to learn concepts
- Use AI to assist tool building
- Validate with small positions
- Do not treat AI output as certainty
- Build alerts and risk rules first
The Value of AlphaPony
AlphaPony, the AI investment assistant under CZCC, emphasizes alerts, risk identification, and trading assistance rather than asking users to blindly trust AI-generated strategies.
Conclusion
AI lowers development cost. It does not lower market risk.
Ordinary users can use AI for efficiency, but they cannot skip validation, risk control, and live trial and error.
This article is for educational and informational purposes only and does not constitute investment advice. Crypto assets are highly volatile. Please make decisions based on your own risk tolerance.