Agent skill · AI & Agents

longbridge-execution-model

Trade execution modelling framework (backtesting analysis only) via Longbridge — covers slippage models (linear / square-root market impact), VWAP/TWAP execution logic, market impact cost estimation (Kyle lambda), volume participation rate (POV) strategy. Helps quant traders build realistic execution assumptions in backtests. Triggers: "执行模型", "滑点模型", "VWAP执行", "TWAP执行", "市场冲击", "执行成本", "成交量参与率", "交易执行", "執行模型", "滑點模型", "VWAP執行", "TWAP執行", "市場冲擊", "執行成本", "交易執行", "execution model", "slippage model", "VWAP", "TWAP", "market impact", "execution cost", "volume participation rate", "Kyle lambda",

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill longbridge-execution-model --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 4 KB
Bundled scripts: none
Version: 1.0.0
Declared author: longbridge
Path: skills/analysis/longbridge-execution-model/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# longbridge-execution-model Trade execution modelling framework for backtesting — slippage, VWAP/TWAP, market impact, and volume participation. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use Trigger on prompts asking for: - Slippage or market impact modelling — *"帮我建一个滑点模型"*, *"square root market impact model"* - VWAP / TWAP execution strategy — *"VWAP执行逻辑"*, *"TWAP slice timing"* - Volume participation rate (POV) — *"成交量参与率策略"*, *"POV strategy"* - Kyle lambda or price impact estimation — *"Kyle lambda 估算"*, *"execution cost analysis"* > This skill is for **backtesting / analysis only** — no live order placement. ## Workflow 1. Identify the symbol and fetch intraday volume profile and tick data. 2. Compute average daily volume (ADV) and intraday volume curve. 3. Apply the requested execution model: - **Linear slippage**: `impact = k × (order_size / ADV)` - **Square-root impact**: `impact = σ × √(order_size / ADV)` - **Kyle lambda (λ)**: estimate from tick data as `ΔP / ΔQ` - **VWAP slice**: distribute order proportionally to historical volume curve - **TWAP slice**: divide order into equal time-weighted

What's inside
Steps it walks through
  1. When to use
  2. Workflow
  3. CLI
  4. Output structure
  5. Error handling
  6. MCP fallback
  7. Related skills
  8. File layout
Ships with 1 file
  • metadata.json
Commands it runs
longbridge kline <SYMBOL> --period 1m --count 200 --format json
Tick-by-tick trades — for Kyle lambda estimation
longbridge trades <SYMBOL> --count 100 --format json
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About this skill
What does the longbridge-execution-model skill do?

Trade execution modelling framework (backtesting analysis only) via Longbridge — covers slippage models (linear / square-root market impact), VWAP/TWAP execution logic, market impact cost estimation (Kyle lambda), volume participation rate (POV) strategy. Helps quant traders build realistic execution assumptions in backtests. Triggers: "执行模型", "滑点模型", "VWAP执行", "TWAP执行", "市场冲击", "执行成本", "成交量参与率", "交易执行", "執行模型", "滑點模型", "VWAP執行", "TWAP執行", "市場冲擊", "執行成本", "交易執行", "execution model", "slippage model", "VWAP", "TWAP", "market impact", "execution cost", "volume participation rate", "Kyle lambda",

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-execution-model --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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