hft-paper-trader

What This Skill Does

The hft-paper-trader skill is a robust, high-frequency paper trading framework designed for OpenClaw agents to simulate crypto market strategies. It functions as an autonomous trading engine that ingests real-time market data from the Binance public API to generate complex, multi-indicator signals. By calculating a confluence score between -10 and +10 based on RSI, MACD, EMA, Bollinger Bands, OBV, and StochRSI, the agent identifies high-probability entry points. The system incorporates a sophisticated Kelly Criterion position sizer, capped at 0.05% risk per trade, ensuring that portfolio exposure is managed conservatively even during high-volume periods. With the ability to execute over 300 trades daily, it provides a rigorous testing environment for trading logic, strategy backtesting, and autonomous agent development without risking actual capital.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/jamierossouw/hft-paper-trader

Ensure that your environment has sufficient write permissions, as the skill creates and maintains a local PORTFOLIO.json file and a LEDGER.csv for tracking trade performance.

Use Cases

• Strategy Backtesting: Validate new algorithmic trading hypotheses against historical or real-time data flows.
• Agent Training: Use the self-improvement loop in lessons.md to refine decision-making processes after realized losses.
• Market Simulation: Test how a portfolio handles high-frequency trade sequences under volatile crypto market conditions.
• Risk Management Prototyping: Experiment with drawdown controls and position sizing models.

Example Prompts

• "hft-paper-trader: scan the current watchlist for BTC and SOL, and execute any signals with a score higher than 7."
• "hft-paper-trader: provide a summary of today's performance from the ledger and calculate our current daily drawdown."
• "hft-paper-trader: re-evaluate the OBV divergence for ETH and adjust the current position size based on updated risk parameters."

Tips & Limitations

• Data Dependency: The skill relies on Binance public API data; ensure your network connection remains stable to avoid gaps in indicator calculation.
• Risk Management: While the 0.05% risk per trade is conservative, monitor the auto-pause trigger at 2% daily NAV to ensure your strategy isn't hemorrhaging capital during unexpected market regime shifts.
• Simulation Accuracy: Remember that this is a paper trading environment; actual live execution involves slippage, exchange fees, and liquidity constraints not fully modeled in basic simulation.
• Maintenance: Periodically clear or archive your LEDGER.csv to maintain performance and prevent file bloat during extended testing sessions.