Free lesson · On-chain markets
Inclusion, finality and conditional loss scenarios
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Start with the idea
Seeing a transaction in a block is not always the same as having irreversible economic settlement. The required assurance depends on the chain and receiving system.
Symbols, units & horizon
- p_rev: assumed reversal/non-final-settlement probability over the specified waiting interval
- X_unhedged: USD loss conditional on reversal with the other leg already committed
- E[L]: modeled USD expected loss
When and why to use this
Document confirmation policies and cross-system settlement dependencies in a research risk memo.
Seeing a transaction in a block is not always the same as having irreversible economic settlement. The required assurance depends on the chain and receiving system.
Record confirmation or finality policy rather than assuming a universal number of blocks. A reorganization can invalidate an observation or reorder actions; a bridge can impose additional waiting beyond the origin chain.
Use explicit scenario probabilities to reason about exposure while waiting. This elementary expected-loss example does not estimate chain security and must never be presented as a measured failure probability.
Inclusion, finality and conditional loss scenarios
- Specify what irreversible action the other leg took before finality.
- Estimate or explicitly assume the loss if the recorded settlement reverses.
- Weight that conditional loss by a clearly labeled scenario probability and also report the full conditional loss.
If a deliberately assumed reversal scenario has probability .001 and the unmatched committed leg loses $5,000 when it occurs, expected loss is $5; the tail remains $5,000.
Apply it in a strategy
- Document confirmation policies and cross-system settlement dependencies in a research risk memo.
- Record the input timestamp, executable quantity, currency and horizon. Reconcile the result with a cash-flow or state table.
- Stress this failure condition: Reversal probabilities are chain- and condition-dependent; a chosen scenario is not an empirical security estimate.
Research deliverable
Build and explain a inclusion, finality and conditional loss scenarios worksheet. Document confirmation policies and cross-system settlement dependencies in a research risk memo.
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 implementation
Self-contained teaching example. Python 3.10+; dependencies and input conventions are shown in the code and notation. Run in your own Python environment.
# Python 3.10+; standard library unless NumPy is imported below.
# Inputs and outputs use the units defined in this lesson. Synthetic teaching example.
def settlement_scenario(probability,conditional_loss):
if not 0<=probability<=1 or conditional_loss<0: raise ValueError("Invalid settlement scenario")
return probability*conditional_loss
print(settlement_scenario(.001,5000))Continue learning
On-Chain Markets: Data, Oracles and Execution — all lessons- Raw token amounts and economic event types
- Transaction gas in a reporting currency
- Minimum output and ordering risk
- Time-weighted reference prices
- Stale oracle health versus executable collateral value
- Bridge fragmentation and capital lock-up
- Inclusion, finality and conditional loss scenarios
- A reproducible on-chain economics study
Quantitative finance and development glossary · Python resources and libraries · Research sources and limitations