🦞 Project Proposal:
In response to Binance's 'Building the Future of Crypto AI' challenge, I share my practical project: EcoClaw.
Many people give up on AI agents within a few days because API Tokens are 'too costly' or they 'place orders randomly'.
My EcoClaw addresses these two major pain points: reducing costs through a minimalist structure and ensuring trading discipline through strict SOPs.
🧠 Part One: The Cost-Saving Logic Behind EcoClaw
To let AI survive on Binance in the long term, it's not about telling it what to do, but rather restricting it from 'doing what it shouldn't':
Reject ineffective visual consumption: Do not let OpenClaw 'look' at screenshots for technical analysis, but comprehensively switch to Binance API for data retrieval.
Writing programs to replace mental calculations: When it comes to math, I force AI to write Python scripts to execute, avoiding LLM hallucinations that lead to 'garbage in, garbage out,' reversing PnL and quickly rolling down to zero.
Memory Isolation: Spot trading, contracts, strategy development, and order placement are assigned to different modules (Multi-Claw) to prevent memory pollution and not waste tokens on daydreaming.
Memory isolation is extremely important; otherwise, it will look at the 5000 U in my pocket and think it can operate more aggressively, resulting in a disaster.
⚙️ Part Two: EcoClaw's Fully Automated Practical Workflow
After saving tokens, I have given EcoClaw a highly autonomous operation discipline that emphasizes risk control:
💡 Strategy Automation and Documentation: I do not provide rigid strategies but allow AI to generate trading strategies based on Binance's recent data, and it must be forced to write them in Markdown format.
This ensures that every entry and exit has a traceable basis.
🛡️ Ironclad Risk Control (5% Circuit Breaker Mechanism): Let AI play freely in and out, but set a daily lower limit, absolutely not to lose more than 5% of the total capital.
Once triggered, EcoClaw will automatically switch the Binance API to 'simulated trading' mode, continuing the logic without harming the principal.
And the history of trading can still serve as a basis for iteration.
🔄 7-Day Evolution Cycle: Every seven days, EcoClaw will summarize the work content of this week's live and simulated trading, proposing a 'trading strategy improvement plan.'
As the owner, I can review and decide to 'adopt' or 'maintain the original plan.'
💰 14-Day Profit Sharing and Recharging: Every 14 days is a settlement cycle. If there are profits (PnL), it will automatically settle and remind to deposit the profit into spot trading.
And actively ask the owner: 'Please use the profit to buy API Tokens to recharge my brain!' (It earns its own feeding money 🦞).
🌅 Part Three: A Day in the Life of the EcoClaw Assistant
[06:00] Daily Report (Output): Timely sending of yesterday's review, including total PnL, win rate, and the cryptocurrencies to focus on today.
[22:44] Trigger Action (Trigger & Action): Market volatility, strategy execution continuously stops loss.
[22:54] Risk Control Thoughts: 'Today's loss has reached a total of 5.1%, exceeding the 5% red line. To protect the principal, I will close the crypto live trading API, and all subsequent signals will be sent to the simulated trading for testing.'
[22:54] Action: Call code to close the live trading interface and switch to simulation mode.
Which 'brain' (Model) is everyone currently connecting to while driving the AI trading assistant?
To be able to write code smartly, strictly adhere to 5% risk control, and not waste money, feel free to share your cost-effective model configurations in the comments!