Vendor acquisition
Downloads instrument listings and historical per-contract futures data for configured markets and timeframes.
Research-grade continuous futures data from fragmented contract history.
The Data Management Software transforms raw third-party per-contract futures files into clean, session-aware, roll-adjusted continuous futures contracts suitable for systematic research, backtesting, feature engineering, and downstream execution workflows.
Product positioning
More than downloading data: normalize, sessionize, validate, roll, and reconstruct.
The data layer preserves auditability at the raw contract and roll-event levels while producing stable symbol-level histories for quantitative research. Its core value is the conversion of vendor files into coherent futures time series with explicit lineage, roll evidence, and quality controls.
Core capabilities
A data engineering foundation built for systematic futures research.
Database normalization
Stores contract-level OHLCV and open-interest records plus roll metadata in structured MySQL tables.
Roll-event logic
Uses proprietary session-aware logic to determine back-to-front contract transition events.
Continuous construction
Applies cumulative additive roll deltas to generate gap-adjusted continuous futures bars.
Quality checks
Separates structural quarantine issues from statistical flags so research does not silently ingest unsafe bars.
Usability evidence
Generates conservative research-usable ranges and roll invariant checks for downstream workflows.
Data flow
The output is stable symbol-level history with retained contract-level auditability.
Continuous futures bars are generated from raw contract rows and roll markers rather than opaque pre-stitched files. The result supports research workflows that need reliable history, interpretable price units, and a reviewable construction trail.
01 Vendor filesDownload listings and historical contract archives.
02 Raw tablesNormalize OHLCV and open-interest records.
03 Roll eventsCompute session-aware transitions and deltas.
04 Adjusted barsApply cumulative additive adjustment.
05 Research rangesPublish usable date ranges and QC evidence.
Why it matters
The data methodology reduces false confidence before research begins.
Session consistency
Daily and intraday bars are harmonized around a consistent exchange-day interpretation.
Roll auditability
Raw bars, roll rows, roll deltas, and stamped roll markers remain separable for review.
Research safety
Quality policy identifies duplicate timestamps, invalid OHLC bounds, negative volume, extreme gaps, and domain-specific exceptions.
Timeframe flexibility
Normalized minute and daily records support research-specific aggregation without losing source lineage.
Public disclosure boundary
This page summarizes data methodology at a product level. It does not disclose vendor credentials, database passwords, private schemas beyond conceptual roles, proprietary roll formulas beyond public positioning, or production data stores.