Algo Wheel Broker Execution Quality Comparison

Purpose and Scope

Use this skill to compare broker execution quality after fills and convert the comparison into a controlled target allocation for a broker wheel. The reference implementation calculates signed Implementation Shortfall (IS) in basis points, includes explicit fees, ranks brokers using decision-notional weights, preserves a minimum canary allocation for non-leading brokers, and returns the sample count and notional coverage behind each rank.

It measures the executed quantity only. Perold's implementation shortfall is the difference between a paper portfolio filled instantly at the decision price and the real portfolio, and it includes the opportunity cost of quantity that was never executed. BrokerExecution carries no unfilled residual, so the score here is the execution-cost component alone. A broker that fills the easy part of an order and cancels the rest scores better on this metric than one that completed the order. Pair the score with a fill-rate or completion measure before it drives allocations — see Common Pitfalls.

This is a TCA and allocation component, not a complete best-execution program. It does not select the arrival-price methodology, prove statistical significance, replace venue selection controls, or route live orders itself.

When to Use

Use it when:

• the trading system captures an arrival or decision price before routing;
• fills, quantities, broker identity, and explicit fees are available in a

consistent currency and time window;

• order assignment to brokers within a comparable segment is randomised, so

the resulting scores are not confounded by which orders each broker received;

• fill rate and unfilled residual are measured separately alongside this score;
• broker performance should influence future flow without fully starving

underperformers;

• the desk can review allocation changes and retain the supporting TCA data.

Use the same benchmark definition across brokers and compare like-for-like orders by side, instrument, venue, urgency, size, and market regime whenever possible. A single unsegmented average can hide systematic routing bias.

When NOT to Use

Do not use this skill when decision prices are missing, stale, or generated after routing, or when fees are not comparable across brokers. Do not use it as the sole evidence for regulatory best execution, a live risk limit, a broker credit check, or a market-impact model.

Do not use it as a full implementation-shortfall measure, or as the only input to a FINRA Rule 5310 regular and rigorous review: that review must also weigh likelihood of execution, speed, and size of execution, none of which this score contains.

Do not rank brokers from a single fill or from an unbounded historical window, and do not rank brokers whose orders were chosen by a trader rather than by the wheel — discretionary assignment reintroduces exactly the selection bias the wheel exists to remove. Choose a review window and minimum observation policy appropriate to the desk and configure it through min_observations and min_notional.

Prerequisites

• Python 3.10+.
• A validated execution record containing broker, side, decision price, fill

price, quantity, and explicit fees.

• Decision price and fee currency aligned with the notional calculation, or a

documented FX conversion before evaluation.

• A randomised assignment mechanism that allocates comparable orders to brokers

according to the published target weights.

• A defined measurement window, order universe, and handling policy for partial

fills, cancels, rejects, and venue differences.

• A documented data-sufficiency policy (minimum executions and notional per

broker) before any allocation change.

• An approval process for changing wheel allocations and a route-level rollback

or kill-switch mechanism.

• Monitoring for sample count, notional coverage, fill rate, IS distribution,

and allocation drift.

Inputs and Outputs

BrokerExecution is immutable and represents one completed execution. Prices and quantity must be finite and strictly positive; fees may be positive or negative when rebates are real and documented. rank_brokers returns an ordered list of BrokerScore records — score, execution count, decision notional, and promotion eligibility — for the audit trail. evaluate_brokers returns a mapping from broker ID to target flow share; shares sum to 1.0 within ALLOCATION_TOLERANCE for a non-empty broker set.

The reference evaluator uses:

• Buy slippage: (fill_price - decision_price) / decision_price * 10000;
• Sell slippage: (decision_price - fill_price) / decision_price * 10000;
• Fee bps: fees_usd / (decision_price * quantity) * 10000;
• IS: slippage bps plus fee bps.

Positive IS is a cost; negative IS is price improvement. Broker averages are weighted by decision notional rather than by execution count.

Allocation rules:

• the best-ranked broker that satisfies min_observations and min_notional

leads and receives the residual flow;

• every other observed broker receives min_allocation, including a

better-scoring broker that failed the sufficiency policy;

• the leader must receive at least min_allocation, otherwise the

configuration is rejected rather than inverting the ranking;

• if no broker satisfies the sufficiency policy, the wheel promotes nobody and

returns equal weights with a warning.

Workflow

1. Define the benchmark: document the decision-price timestamp, price

source, side convention, fee definition, currency conversion, and review window before collecting results.

1. Randomise assignment: route comparable orders to brokers by the

published weights using a randomising engine, not trader discretion, and record the assignment. Without this the next window's comparison measures order selection rather than broker skill.

1. Validate execution records: reject missing broker IDs, unsupported sides,

non-finite values, non-positive prices, and non-positive quantities. Do not turn invalid records into zero-cost executions.

1. Segment the universe: compare orders with similar instrument, size,

urgency, venue, and market conditions; record exclusions and partial-fill treatment.

1. Calculate IS: calculate signed slippage and explicit fee bps using the

same decision-notional denominator for buys and sells.

1. Measure what the score omits: compute fill rate, cancelled residual, and

reject rate per broker over the same window. A broker whose IS improved while its fill rate fell has not improved.

1. Aggregate and rank: call rank_brokers for the notional-weighted average

IS, execution count, and notional coverage per broker; ties break deterministically by broker ID.

1. Apply the sufficiency policy: set min_observations and min_notional

so a broker cannot lead the wheel on a thin sample. If nothing qualifies, accept the equal-weight no-promotion result and keep gathering data.

1. Assign target flow: give the leading eligible broker the residual

allocation and each other observed broker the configured minimum canary share. Reject a configuration whose canary floors leave the leader with less than the floor itself.

1. Approve and deploy: review the proposed change, version the allocation

snapshot together with the BrokerScore evidence, deploy it atomically, and monitor post-deployment performance.

1. Re-evaluate: repeat on a bounded schedule and after material changes to

broker algo, fee schedule, venue mix, or market regime. FINRA members conducting a regular and rigorous review under Rule 5310 must do so at least quarterly.

Common Pitfalls

• Ranking on executed-fill cost alone, which rewards a broker for cancelling

the hard residual. Two brokers with identical IS and 95% versus 60% fill rates are not equivalent, and this score cannot tell them apart.

• Letting traders choose which orders go to which broker and then comparing the

results. The wheel's comparison is only valid over randomised assignment.

• Promoting a broker to the leading share on one lucky small fill. Configure

min_observations and min_notional instead of trusting the point estimate.

• Using decision_price / fill_price for sell slippage, which changes the

denominator and overstates a 100-to-99 sell shortfall as 101.01 bps instead of 100 bps before fees.

• Averaging rounded per-trade bps or weighting every fill equally when order

sizes differ materially.

• Returning zero for a zero or invalid decision price and allowing bad data to

win the ranking.

• Comparing brokers with different fee currencies, venue mix, order urgency,

or client flow without segmentation or adjustment.

• Letting the canary floor exceed the available flow, or setting a floor so

high that the leading broker is routed less flow than the brokers it beat.

• Allowing input order to decide ties and therefore change allocations

nondeterministically.

• Assuming allocation shares are exactly representable in binary floating

point. Four brokers at a 10% floor sum to 0.9999999999999999 unless the leader absorbs the residual; compare against ALLOCATION_TOLERANCE.

• Changing allocations without retaining the TCA window, data snapshot,

ranking, approval, and rollback reference.

• Building RTS 27 or RTS 28 reports from this output. Both MiFID II reporting

obligations have been deleted — see references/standards.md.

Verification

Run:

python -m unittest discover -s skills/algo-wheel-broker-execution-quality-comparison/scripts

The tests cover buy and sell IS, price improvement, notional weighting, canary allocation, deterministic ties, single-broker behavior, empty input, invalid data, notional overflow, allocation configuration errors, the leader-floor invariant, float-exact allocation sums, BrokerScore evidence, and the data-sufficiency gate including the equal-weight fallback. Add production integration tests for the execution ledger, fee/FX normalization, fill-rate measurement, allocation publication, and rollback before connecting the result to live routing.

Related Skills

• implementation-shortfall-minimization
• post-trade-execution-quality-scorecard
• best-execution-record-keeping-global
• broker-failover-secondary-account-routing

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: HimanshuJ16
• Source: HimanshuJ16/Algo-Trading-Skills
• License: Apache-2.0
• Homepage: https://skills.himanshujangir.com

Install and usage instructions live in the source repository linked above.