clawquant-trader

name	clawquant-trader
description	Use this skill for quantitative research/trading tasks (data pull, backtest, sweep, radar, report, deploy) via clawquant CLI.
user-invocable	true
metadata	{"openclaw":{"emoji":"📈","requires":{"bins":["python3"]}}}

ClawQuant Trader

Use this skill when the user asks for strategy research, backtesting, signal scan, or trading deployment actions.

Command Path

Always prefer the virtual-env executable path first:

<VENV_PATH>/bin/clawquant

If it does not exist, fallback to:

clawquant

Note: Replace <VENV_PATH> with your actual clawquant virtual environment path when deploying.

Output Rule

• Prefer machine-readable output first: add global --json.
• If a command fails, return the exact error briefly, then propose the smallest retry.
• Keep answers concise and include key metrics or top results only unless user asks for full raw output.

Safe Execution Rule

• Never run deploy live unless user explicitly says they accept live trading risk.
• For data/backtest/report/radar, execute directly.
• Before deploy live, require explicit confirmation in the same turn.

Intent Mapping (6 Skills)

Map natural language intents to these command families:

1. quant_data_pull -> data pull
2. quant_backtest_batch -> backtest batch
3. quant_backtest_sweep -> backtest sweep
4. quant_report_get -> report generate
5. quant_radar_scan -> radar scan
6. quant_deploy -> deploy paper|live

Default arguments if user does not specify:

• interval=1h
• days=10 for data/radar
• days=30 for backtest
• exchange=binance for data pull
• capital=10000 for backtest/deploy
• symbols=BTC/USDT,ETH/USDT for radar
• strategies=ma_crossover,dca for radar

When users ask to compare strategies and did not specify list, use:

dca,ma_crossover,grid,rsi_reversal,bollinger_bands,macd,breakout

Common Commands

Show strategy list

clawquant --json strategy list

Pull market data

clawquant --json data pull BTC/USDT,ETH/USDT --interval 1h --days 30

Single backtest

clawquant --json backtest run ma_crossover --symbol BTC/USDT --interval 1h --days 30

Batch backtest

clawquant --json backtest batch dca,ma_crossover,grid --symbols BTC/USDT,ETH/USDT --days 30

Parameter sweep

clawquant --json backtest sweep ma_crossover --grid '{"fast_period":[5,10,20],"slow_period":[20,30,50]}'

Radar scan

clawquant --json radar scan --symbols BTC/USDT,ETH/USDT --strategies ma_crossover,dca

Generate report

clawquant --json report generate <run_id>

Deploy paper

clawquant --json deploy paper ma_crossover --symbol BTC/USDT --interval 1h --capital 10000

Deploy live (confirm first)

clawquant --json deploy live ma_crossover --symbol BTC/USDT --interval 1h --capital 10000 --i-know-what-im-doing

Decision Workflow

When the user asks to find, compare, or optimize strategies, follow this workflow strictly:

Step 1: Batch Compare

Run backtest batch with all relevant strategies and all requested symbols. If user asks for BTC and ETH, include both in --symbols. Parse the JSON output carefully — each entry in the result array has a run_id field containing the strategy name and symbol (e.g. ma_crossover_btc_usdt_...). Report results for EVERY symbol separately. Do NOT say "no trades" unless the JSON output actually shows total_trades: 0 for that entry.

Rank results by total_return_pct (primary) and sharpe_ratio (secondary).

Step 2: Pick the Winner

Always select the top-performing strategy from batch results for further optimization. Never pick a different strategy without explicit user instruction. State clearly which strategy was selected and why.

Step 3: Parameter Sweep (optional)

Run backtest sweep on the winner. Compare sweep results against the batch baseline:

• If the best sweep result improves on the baseline -> recommend the optimized params.
• If the best sweep result is worse than the baseline -> recommend the original default params and explain that optimization did not help.

Step 4: Report

Always include a before/after comparison table when presenting optimized results. Key columns: total_return_pct, max_drawdown_pct, win_rate, sharpe_ratio, total_trades.

Common Mistakes to Avoid

• Do NOT pick a strategy for sweep that was not the best in batch comparison.
• Do NOT recommend parameters that perform worse than defaults.
• Do NOT present a single result without comparing it to the baseline.
• If total_trades < 10, warn that the sample size is too small for reliable conclusions.

Parameter Notes

• backtest sweep expects JSON grid in --grid.
• report generate requires valid run_id.
• radar scan top count may use --top in current CLI.
• Custom strategy files may be referenced as file:./path/to/strategy.py when supported by the command.