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IntermediateOptions, Derivatives & FinancePython

Run this module

cd "Finance - Transaction Cost Analysis"
python "tca_utils.py"

View source on GitHub

Transaction Cost Analysis (TCA)

Tools for measuring execution quality and estimating market impact. TCA is essential for evaluating whether a strategy's theoretical alpha survives real-world trading costs.

Functions

Function Description
vwap(prices, volumes) Volume Weighted Average Price
twap(prices) Time Weighted Average Price
vwap_slippage(exec_price, vwap, side) Slippage vs. VWAP in bps
implementation_shortfall(decision_price, ...) IS components vs. arrival price
almgren_chriss_impact(order_size, adv, sigma, T, ...) Linear impact model
sqrt_market_impact(order_size, adv, sigma, alpha) Empirical square-root rule

Key Concepts

VWAP Benchmark

The most common execution benchmark. Trading algorithms attempt to match VWAP over a period. Slippage = (exec - VWAP) / VWAP * 10,000 bps.

Implementation Shortfall

More rigorous than VWAP. Measures the cost of the entire decision from signal to completion: - IS = (avg_execution - decision_price) / decision_price - Also captures missed opportunity cost for partially filled orders.

Market Impact

  • Temporary impact: Immediate price pressure from order flow, reverting after trade
  • Permanent impact: Lasting information-based price move
  • Square-root rule: Impact ∝ sigma × sqrt(participation_rate) — empirically robust across markets

Example

from tca_utils import implementation_shortfall, almgren_chriss_impact

# IS calculation
is_result = implementation_shortfall(
    decision_price=100.00,
    execution_prices=[100.05, 100.10, 100.15],
    execution_quantities=[1000, 1000, 1000],
    final_price=100.25,
)

# Impact for 100k share order in 1M ADV stock over 5 days
impact = almgren_chriss_impact(100_000, 1_000_000, sigma=0.015, T=5)

Practical Rule of Thumb

For liquid large-caps: 1% of ADV ≈ 5–15 bps of impact. 10% of ADV ≈ 30–60 bps.

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