Free lesson · On-chain markets

Minimum output and ordering risk

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Between signing and inclusion, other trades can change the pool. Minimum-output protection limits the executed outcome but does not guarantee that your transaction succeeds.

A slippage setting is usually relative to a quoted output, not a promise that the market price will stay within that percentage. Exact semantics depend on router and token behavior.

Transaction ordering creates economic exposure, including adverse execution and failed inclusion. Study observable outcomes and protective execution constraints; a public transaction is not necessarily executed in the order a user first saw it.

Apply it in a strategy

• Define a reproducible swap-protection policy and measure both successful fills and paid failures.
• Record the input timestamp, executable quantity, currency and horizon. Reconcile the result with a cash-flow or state table.
• Stress this failure condition: Loose output limits permit adverse fills; tight limits can raise failure costs. Neither solves all ordering or availability risks.

Research deliverable

Build and explain a minimum output and ordering risk worksheet. Define a reproducible swap-protection policy and measure both successful fills and paid failures.

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 minimum_output(quoted,slippage):
    if quoted<0 or not 0<=slippage<1: raise ValueError("Invalid output constraint")
    return quoted*(1-slippage)

print(minimum_output(1000,.005))