From raw futures observations to one auditable model input.
The forecasting model only sees a compact pre-open signal. Reaching that signal requires a data pipeline that retrieves the correct futures contract, respects fixed New York timestamps, checks data freshness and preserves a reproducible audit trail.
The data pipeline defines what information reaches the model.
Graham Says measures Nasdaq-100 futures between the previous US cash-market close and 09:15 New York time. The engineering job is to make that measurement the same way every session rather than relying on a manually selected price.
Five steps turn market data into the model input.
Select the intended Nasdaq-100 futures contract and handle the roll explicitly.
Request the market observations needed around the two fixed endpoints.
Interpret timestamps in America/New_York and preserve the fixed observation window.
Check endpoint availability, freshness, price sanity and contract consistency.
Convert the two endpoint observations into the pre-market log return.
Once the two observations are fixed, the model input is fixed too.
What the pipeline guards against.
Reproducibility starts with the inputs.
A one-line regression can still produce an irreproducible result if the underlying futures observation was selected by hand, taken from the wrong contract, or measured at a different time. The market-data pipeline exists to make the input definition operational rather than aspirational.