Trade Strategy and Execution

A good investment idea can be wiped out by poor execution. This module links why a trade is made to how urgently and with which tools it should be executed, compares benchmarks and algorithm families, contrasts trading across equity, bond, derivative and currency markets, and shows how to measure costs with implementation shortfall and judge whether a firm's trading process serves its clients.

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What you will learn

1. Relate the motivation behind a trade to its urgency and to the inputs that shape the trading strategy: order size, security liquidity, market conditions and the trader's risk aversion.

Short-lived alpha and unwanted risk call for urgency; long-horizon alpha allows patience.
Index-fund cash flows target the closing price because the benchmark is valued at the close.
Order size as a share of ADV, spread, volatility and market conditions drive expected cost.
Trading faster raises market impact but lowers execution risk; trading slower does the reverse.
Higher risk aversion means a faster, front-loaded schedule, not a slower one.

2. Compare pre-trade, intraday and post-trade execution benchmarks and select the class of trading algorithm that fits an order.

Arrival price measures the full cost of working an order; VWAP hides costs when the trader is a large part of volume.
Closing price is the natural benchmark for index funds; decision price captures delay before the order reaches the desk.
Scheduled algorithms (VWAP, TWAP, POV) suit small, non-urgent orders in liquid securities.
Arrival price algorithms front-load; liquidity-seeking and dark algorithms suit large orders in illiquid names.
POV algorithms accelerate when volume spikes, so they need price limits.

3. Contrast how trades are implemented in equity, fixed-income, exchange-traded and OTC derivative, and spot currency markets.

Equities: order-driven, fragmented, highly electronic; algorithms and smart routers for most orders.
Bonds: quote-driven OTC; RFQs to several dealers, portfolio trades, evaluated pricing; many bonds rarely trade.
Futures: central order book and clearing; cheap and quick exposure changes with basis and roll risk.
OTC swaps: dealer RFQs, often on regulated platforms, with central clearing and daily margin.
Spot FX: dealer market, RFQ, streaming prices or algorithms; benchmark fixes concentrate flow.

4. Measure trade costs with implementation shortfall and other benchmarks, evaluate execution quality, and assess the governance of a firm's trading process.

IS = delay + trading + opportunity costs + fees, divided by the paper portfolio value S x Pd.
Delay and trading costs use executed shares; opportunity cost uses unexecuted shares and the decision price.
Market-adjusted IS subtracts beta x market return to isolate the trader's contribution.
Compare realized costs with pre-trade estimates and review patterns over many orders, not single trades.
Best execution is a documented, monitored process covering price, cost, speed, likelihood and conflicts.

Module at a glance

The motivation sets the urgency: short-lived alpha and risk cuts trade fast, index cash flows target the close, long-horizon alpha can wait.
The trader's dilemma: faster means more market impact but less execution risk; a more risk-averse trader trades faster.
Arrival price shows the full cost of working an order; VWAP is fine for small orders but flatters large ones.
Scheduled algorithms suit small, patient orders; arrival price algorithms suit urgent ones; liquidity-seeking and dark algorithms suit large orders in illiquid names.
Bonds and spot FX are dealer markets traded by RFQ; futures change exposure quickly and cheaply with basis risk.
IS = delay + trading + opportunity costs + fees, divided by shares ordered x decision price.
Best execution is a documented, measured and reviewed process that manages conflicts and controls automated trading risk.

This free module has 4 lessons, 16 practice questions with full explanations and 1 essay case with a model answer.

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