Introducing Optimal Execution and Block Trade Pricing: A General Framework for after-work quants

Takeaway. Block trades need a price that embeds illiquidity—and optimal liquidation can be characterised with a clean Hamiltonian toolkit instead of one-off recipes.

Optimal Execution and Block Trade Pricing: A General Framework (Olivier Guéant, 2012) ties optimal execution and negotiated block pricing into one coherent story.

Fit for learners: Hamiltonian characterization of optimal liquidation and closed-form block-trade illiquidity pricing.

This Hub note is a plain-language intro. Not investment advice. See also Almgren–Chriss and Alfonsi–Fruth–Schied.

The problem

A large position is not "the mid times shares." Crossing or negotiating a block changes the economics. Learners often confuse temporary impact, permanent impact, and the premium counterparties charge for immediacy.

The idea / core intuition

• Optimal liquidation is framed so the hard control problem has a Hamiltonian characterisation—useful scaffolding across models.
• Block-trade pricing asks: what illiquidity adjustment makes both sides willing to deal off-book (in the model)?
• The framework connects continuous trading schedules and discrete block decisions.
• Intuition: urgency, risk aversion, and liquidity all show up as terms you can name—not magic constants.

Read the PDF for mathematics. This note stays at vocabulary level on purpose.

Why after-work Python learners care

You will meet "block" and "slippage" in every desk story. Having a research frame beats memorising blog rules of thumb.

Tiny evening exercise: write a function block_premium(size, urgency, liquidity) that returns a made-up premium (e.g. linear in size/liquidity × urgency)—plot premium vs size for two liquidity levels. Label it toy, not fair value.

One-evening reading checklist

1. Separate optimal execution vs block pricing goals in two bullets.
2. Note where Hamiltonian language appears (no need to derive tonight).
3. List three drivers of illiquidity premium in your own words.
4. Link back to Almgren–Chriss mean-variance intuition.

Download PDF

Download PDF

Landing: arXiv:1210.6372

Important disclaimer

Not investment advice. This Hub note is educational only. The paper is research—not a licence to trade, quote, or execute with real capital.

What this is not

• Not CEF / subsidy framing
• Not Quant or Macro course CTA
• Not a broker / signal-group pitch

Next step

Single course CTA: SoloQuant Academy's Introduction to Python Programming—get comfortable with functions, files, and plots first; then these microstructure PDFs become much less scary.