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Quant · Sample or your CSV

Daily ETF validation

Run defined rules through a real calculation and compare benchmark, costs and drawdown.

See what you will get

A disappointing result is still a finding.

Synthetic teaching sample · not a real company / fund
A disappointing result is still a finding.
Valuation dateStrategy 60/40Benchmark 50/50Event
2026-10-2610,000.0010,000.00Signal; no position
2026-10-279,990.009,990.00Next-day entry; cost 10 each
2026-10-2810,589.4010,489.50Value existing asset units
2026-10-299,930.069,940.05Not month end; no rebalance
Net cumulative return−0.6994%−0.5995%Includes initial friction
Maximum drawdown6.2264%5.2381%Includes initial point

Synthetic teaching sample synthetic-4d-v1, not real fund performance. ETF_A total-return indices: 100, 110, 121, 108.9; ETF_B: 100 throughout. The strategy misses the first rise and pays 10 bps per side. Synthetic month end is October 30, not the last CSV row. These are reproducible expected values; actual host runs have separate evidence.

Keep the records in your workspace

Experiment report, equity records, costs and limits

report.mdequity.csvallocations.csvevents.csvrun-manifest.json

Each run writes to a new reports/{run-id}/, preserving inputs and previous reports. Status distinguishes completed, needs-input and failed.

What to prepare

Confirmed rules and confirmation record, prices.csv, a data manifest and complete valuation calendar. Requires a Python 3.12 environment.

File preparation & data definitions →

Start with this request

Use the ETF validation Skill to check confirmed rules, CSV and data manifest first. Use the four-day teaching sample to check fields, dates and total-return definitions. If valid, run the fixed tool and preserve benchmark, costs, drawdown and records. Otherwise stop and explain missing inputs.

First ask your host to inspect the method version, inputs, output location and dependencies in read-only mode. Downloading, discovering a Skill and completing its sample are separate steps.

Its limits

Four days test timing and arithmetic, not long-term effectiveness. Version one supports fixed-weight rebalancing and bounded simple momentum. No fabricated missing values, unconfirmed rules or same-close lookahead. A 63-interval lookback needs 64 complete observations.

No network retrieval, account connection or result upload by default. Model handling depends on your host and service settings; this is not a promise of fully offline processing.

Continue your work

A disappointing result is still a finding. Return to the rules and record new questions.

Strategy rule specification →

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