Free lesson · Crypto markets

Order-book depth and average execution price

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The best ask covers only the quantity resting there. Larger orders consume deeper, often more expensive levels.

Walk the visible asks until the desired base quantity is filled. Multiply each fill price by its quantity and divide total quote cost by filled base units.

Visible depth is a snapshot, not a promise. Cancellations, hidden liquidity, latency and fees change actual fills. Keep unfilled quantity explicit; do not divide cost by the requested quantity when fewer units fill.

Apply it in a strategy

• Estimate size-dependent execution costs for spot and cross-venue research.
• Record the input timestamp, executable quantity, currency and horizon. Reconcile the result with a cash-flow or state table.
• Stress this failure condition: Static depth assumes the book survives until arrival and ignores competing orders.

Research deliverable

Build and explain a order-book depth and average execution price worksheet. Estimate size-dependent execution costs for spot and cross-venue research.

Evidence boundary: Synthetic arithmetic and scenarios illustrate mechanics. They are not historical returns, a paper replication, or evidence of an executable edge. Research sources and their access limitations are recorded at the end of this module.

# Python 3.10+; standard library unless NumPy is imported below.
# Inputs and outputs use the units defined in this lesson. Synthetic teaching example.
def execution_average(fills):
    if not fills or any(q<=0 or p<=0 for q,p in fills): raise ValueError("Positive quantity/price fills required")
    quantity=sum(q for q,p in fills)
    cost=sum(q*p for q,p in fills)
    return quantity,cost,cost/quantity

print(execution_average([(.5,50000),(.3,50100)]))