# Clawquant Trader > Use this skill for quantitative research/trading tasks (data pull, backtest, sweep, radar, report, deploy) via clawquant CLI. - Skill: `tomevault-io/clawquant-trader` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/clawquant-trader` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/clawquant-trader/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/clawquant-trader --- # 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: `/bin/clawquant` If it does not exist, fallback to: `clawquant` > **Note:** Replace `` 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 ```bash clawquant --json strategy list ``` ### Pull market data ```bash clawquant --json data pull BTC/USDT,ETH/USDT --interval 1h --days 30 ``` ### Single backtest ```bash clawquant --json backtest run ma_crossover --symbol BTC/USDT --interval 1h --days 30 ``` ### Batch backtest ```bash clawquant --json backtest batch dca,ma_crossover,grid --symbols BTC/USDT,ETH/USDT --days 30 ``` ### Parameter sweep ```bash clawquant --json backtest sweep ma_crossover --grid '{"fast_period":[5,10,20],"slow_period":[20,30,50]}' ``` ### Radar scan ```bash clawquant --json radar scan --symbols BTC/USDT,ETH/USDT --strategies ma_crossover,dca ``` ### Generate report ```bash clawquant --json report generate ``` ### Deploy paper ```bash clawquant --json deploy paper ma_crossover --symbol BTC/USDT --interval 1h --capital 10000 ``` ### Deploy live (confirm first) ```bash 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. --- > Source: [duolaAmengweb3/clawquant-trader](https://github.com/duolaAmengweb3/clawquant-trader) — distributed by [TomeVault](https://tomevault.io).