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Modeling Transaction Cost Analysis

ASecurity

Builds TCA frameworks with implementation shortfall, VWAP comparison, and market impact estimation across asset classes. Use when conducting TCA, measuring execution quality, or analyzing trading costs.

22 stars
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Added 9/20/2026
datagoexpressperformance

Security Analysis

A100/100

Scanned 9/20/2026

Install to Claude Code

$npx -y skills add lev-os/agents --skill modeling-transaction-cost-analysis --agent claude-code

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SKILL.md
---
name: modeling-transaction-cost-analysis
description: Builds TCA frameworks with implementation shortfall, VWAP comparison, and market impact estimation across asset classes. Use when conducting TCA, measuring execution quality, or analyzing trading costs.
tags:
  - modeling
  - public-markets-and-trading
  - trading
metadata:
  author: casemark
  practice_areas:
    - Trading
    - Market Making
    - Execution
  document_types:
    - Financial Model
  skill_modes:
    - Modeling
    - Forecasting
---
# Modeling Transaction Cost Analysis

Builds TCA frameworks with implementation shortfall, VWAP comparison, and market impact estimation across asset classes.

## When To Use

- Evaluating execution quality for a completed trade or batch of orders against decision-price, VWAP, TWAP, or arrival-price benchmarks
- Estimating pre-trade market impact and optimal execution horizon for a proposed block or portfolio transition
- Comparing broker/algo performance across venues, time periods, or order-routing strategies
- Building or refining an ongoing TCA reporting framework for a trading desk, fund, or transition manager
- Responding to best-execution obligations under MiFID II, SEC Rule 606, or equivalent regulatory regimes [VERIFY jurisdiction-specific rules]

## Inputs To Gather

- **Order/execution data**: timestamps (order entry, first fill, last fill), side, quantity, limit price, filled quantity, average fill price, venue/broker tags
- **Market data**: consolidated bid/ask/mid at decision time, arrival time, and fill times; intraday VWAP and TWAP series; daily ADV and volatility for each instrument
- **Benchmark selection**: confirm which benchmarks are relevant (implementation shortfall, interval VWAP, close price, participation-weighted price)
- **Asset class specifics**: equity tick data differs from FX spot/forward, listed derivatives, or fixed-income RFQ workflows — confirm instrument universe
- **Cost components to isolate**: explicit costs (commissions, exchange fees, taxes, clearing) vs. implicit costs (spread, market impact, delay/timing cost, opportunity cost)
- **Grouping dimensions**: by broker, algorithm, trader, strategy, market-cap bucket, volatility regime, or time-of-day

## Workflow

1. **Normalize execution records** — align timestamps to a common clock, reconcile partial fills, and tag each execution with venue and algo identifiers. Remove or flag cancels, amendments, and erroneous prints.

2. **Calculate explicit costs** — sum commissions, SEC fees, stamp duties [VERIFY applicable fee schedules], clearing charges, and any exchange rebates/credits per order.

3. **Compute implementation shortfall (IS)** — decompose total IS into:
   - **Delay cost**: mid-price movement from decision time to order-entry time
   - **Market impact**: price movement from order entry to volume-weighted average fill price
   - **Timing cost**: price drift during the execution window attributable to market movement rather than the order itself
   - **Opportunity cost**: unfilled portion valued at closing price minus decision price
   - Express each component in basis points and in absolute currency.

4. **Run VWAP/TWAP comparison** — calculate the interval VWAP (or TWAP) over the execution window using consolidated market data; report slippage as fill price minus benchmark in bps. Flag orders where participation rate exceeded a threshold (e.g., >15% of interval volume) since benchmark validity erodes at high participation.

5. **Estimate market impact** — apply a square-root market impact model (e.g., σ × √(Q/ADV) × coefficient) calibrated to the asset class. Compare predicted impact to realized impact. If the desk has historical TCA data, fit coefficients empirically; otherwise use published coefficients [VERIFY source — common references: Almgren-Chriss, Kissell-Glantz, ITG/Virtu models].

6. **Segment and aggregate** — group results by the dimensions specified (broker, algo, trader, volatility regime, market-cap tier). Compute mean, median, and standard deviation of slippage within each group. Highlight statistically significant differences across groups.

7. **Sensitivity and regime analysis** — test how results shift under different benchmark windows, volatility bands, and participation-rate thresholds. Identify whether poor execution clusters in specific market conditions (e.g., high-vol opens, illiquid close auctions).

8. **Compile TCA report** — structure the output with an executive summary, per-benchmark scorecards, broker/algo league tables, outlier trade detail, and methodology notes.

## Output

- **Executive summary**: total explicit and implicit costs in bps and currency; headline IS and VWAP slippage across the analysis period
- **Benchmark scorecards**: IS decomposition table (delay, impact, timing, opportunity) and VWAP/TWAP slippage by instrument or portfolio segment
- **Market impact analysis**: predicted vs. realized impact scatter plot data; model fit statistics and residual analysis
- **Broker/algo league tables**: ranked performance by mean slippage, with sample size, standard deviation, and confidence intervals
- **Outlier register**: trades exceeding a defined slippage threshold with root-cause annotations (large block, illiquid name, news event, algo malfunction)
- **Methodology appendix**: benchmark definitions, model parameters, data sources, any exclusions or adjustments applied

## Quality Checks

- Confirm all timestamps are synchronized and in the same timezone; cross-check against exchange calendars for holidays and half-days
- Validate that VWAP denominators use the correct volume source (consolidated tape vs. primary exchange) [VERIFY per market convention]
- Ensure IS components sum to total IS within rounding tolerance
- Check that market impact model coefficients are appropriate for the asset class and liquidity tier — equity large-cap coefficients should not be applied to small-cap or credit instruments
- Verify that participation rate calculations exclude auction volume where appropriate
- Flag any instrument where ADV data is stale or missing — mark those rows with [VERIFY]
- Cross-reference explicit cost totals against broker confirmations or clearing statements before finalizing

Attribution

lev-oslev-os
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3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

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