Model trade execution costs to correct optimistic backtest fills.
npx skills add https://github.com/loanntc/Paave --skill trade-execution-cost
Please help me install this Agent Skill. Skill: trade-execution-cost Source: https://github.com/loanntc/Paave/tree/main/skills/execution-model Command: npx skills add https://github.com/loanntc/Paave --skill trade-execution-cost
This Skill reduces overly optimistic backtest results by modeling slippage, market impact, execution delay, and transaction-cost drag so your simulated fills better resemble real execution.
Use it when you are running strategy backtests and want to quantify how costs and liquidity assumptions (ADV, volatility, participation rate) change annual returns, Sharpe ratio, and drawdowns.
Apply the trade-execution-cost skill to your backtest configuration to compute execution-price slippage, apply T+1 delay assumptions, and estimate total transaction-cost drag.
High-intent search queries and answers about installing and using this skill.
You can model slippage and market impact in backtesting by applying fixed, linear, or square-root impact assumptions to estimate realistic fill prices and transaction-cost drag.
Simulating VWAP and TWAP execution in a backtest involves applying intraday slicing logic with participation constraints to compute consistent fill-price adjustments and execution delays.
Yes, the slippage model supports backtest-only execution simulations across both equities and crypto, applying liquidity regime parameters to estimate market impact and execution delay effects.
Transaction costs affect Sharpe ratio and drawdowns by adding slippage, spread, and commission drag to simulated fills, reducing optimistic returns to reflect real execution friction.
Configuring commission and delay bars requires setting execution parameters like participation constraints and T+1 delay assumptions to calculate accurate fill-price adjustments and net performance impact.
Use a square-root impact model over fixed slippage when modeling large orders sensitive to liquidity regime parameters like ADV and volatility, capturing non-linear market impact effects on realized prices.
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