Recently, something called ClawdBot (OpenClaw) has completely taken off in AI circles—it's not just an ordinary chatbot, but a "personal assistant" that can directly operate your computer. As a quantitative trading enthusiast, the first thought that popped into my head was: Can this thing help me with quant trading?
Today, let's run a real test by combining ClawdBot with FMZ Quant Platform to see how much work this "AI personal assistant" can actually handle in strategy development.
Step 1: Installation and First Meeting
The installation process was simpler than expected. I tried it on macOS—basically one command line and following the official guide got it done.
After launching, a clean Dashboard interface appeared—this is the main battlefield for communicating with AI. It supports multiple channels including Lark, Slack, and WhatsApp, with both text and voice options, quite flexible. Looking at AI's first reply, my quantitative personal assistant was officially on duty.
Challenge Begins: Let's Have AI Write a Strategy
Without hesitation, I went straight to the challenge. I gave ClawdBot its first task: "Write a dual moving average quantitative strategy." I chose this because the logic is clean: buy when the short-term moving average crosses above the long-term one (golden cross), sell when it crosses below (death cross). Classic, clear, perfect for testing AI's coding ability.
Once the instruction was sent, this "digital colleague" got to work. I watched it automatically open a browser, head straight to FMZ Quant Platform, find the strategy editor, and then—start writing code.
The whole process was surprisingly smooth. It clearly understood FMZ's API, with clean code structure: first set parameters (5-day short-term, 20-day long-term, 0.01 per trade), then write utility functions for getting moving averages, initialize charts in the main function, use loops to continuously fetch K-lines, calculate indicators, execute trades, and even added visualization as a bonus. After finishing, it even included a detailed logic explanation—definitely beginner-friendly.
Backtesting and First Debugging
Writing code isn't enough—you have to put it to the test. I told it: "Use Binance exchange, BTC/USDT trading pair, daily data, run a backtest."
AI switched to the backtest page, configured parameters, clicked run—and got an error! Just as I was thinking "guess I'll have to do this myself," it located the error on its own, proactively fixed a syntax issue, and resubmitted.
This time it ran successfully, but the results were... a bit dismal. However, it didn't stop there—it immediately generated a backtest report: return rate, maximum drawdown, win rate, analysis of typical losing trades—data at a glance. The report also initially identified various areas for optimization.
Strategy Optimization: Multi-Step Coordinated Operations
The backtest results weren't ideal, so let's have AI optimize it. I proposed a complex requirement: change the exchange and trading pair, while adjusting and optimizing moving average period parameters to see if performance could improve. This task involves coordinated operations across multiple steps.
After AI started executing, it first proposed specific optimization ideas based on the previous backtest results, then returned to the strategy editing page for modifications: changed the exchange to OKX, trading pair to ETH/USDT, and moving average periods from 5 and 20 to 10 and 50. What surprised me more was that it proactively added ATR volatility filtering and dynamic stop-loss logic—things I hadn't explicitly requested.
Then it returned to the editor and implemented the changes step by step. After modifications, it automatically re-ran the backtest—this time the return curve was much smoother, achieving positive returns. Finally, it generated a comparison analysis between old and new versions and provided suggestions for further optimization, such as considering increased position sizing, parameter and logic optimization, multi-asset portfolios, etc. These suggestions were all quite pertinent, showing that AI truly understands the basic logic of quantitative trading.
Wrapping Up: Let AI Organize Development Notes
Finally, a practical task: "Organize today's process from strategy writing to optimization debugging into reusable development documentation." It quickly organized a clearly structured note including: strategy version iteration records, reasoning for each modification, backtest data comparison, and potential next optimization directions—directly a qualified technical log.
Post-Use Impressions
After the complete test, the role ClawdBot plays in the quantitative workflow is that of a qualified quantitative trading assistant. While it can't replace your experience and judgment, it can efficiently execute many standardized operations.
Its advantages include:
- Quick idea implementation: From strategy description to runnable code in just a few minutes
- High debugging efficiency: Automatically locates errors, compares version differences, generates data reports
- Knowledge base support: Familiar with common strategy patterns, risk control methods, and platform APIs
- Tireless: Can simultaneously backtest multiple parameter combinations or monitor multiple strategy statuses
But there are also clear limitations:
- Depends on clear instructions: Vague requirements lead to output deviations
- Superficial market understanding: It understands code logic but not market sentiment and macro changes
- Requires human review: After each important modification, logic reasonableness still needs human verification
In actual use, the smoothest mode is: you grasp direction and risk control, it handles execution and debugging. For example, when you think "maybe adding volatility filtering would be better," it can immediately implement this idea and verify the effect; when you find the strategy loses money during sideways markets, it can quickly test multiple parameter combinations to find more robust configurations.
This collaboration model is particularly suitable for high-repetition tasks like strategy prototyping, quick backtest verification, and parameter tuning. It lets quantitative developers focus more on core logic and market insights, rather than getting bogged down in code debugging and data organization.
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