Local-First Data Engine

A local-first data engine gives quant researchers control over the evidence layer behind their research. In Corrai, the data engine is designed for long-tail needs such as local quant research database, point-in-time market data workflow, reproducible backtesting data lineage, crypto OHLCV data lake, and alternative data validation for quant research.

The goal is not to collect as much data as possible. The goal is to make each dataset auditable enough that a backtest result can be trusted or rejected for the right reason.

What local-first means

Local-first means the core research artifacts live in an environment controlled by the researcher or team. Raw market data, derived features, run metadata, and validation evidence are not treated as temporary notebook state. They are part of the research record.

This is useful when the team works with:

• proprietary factors or private transforms
• licensed datasets with usage restrictions
• exchange data that needs normalization
• alternative data with publication lag
• experimental strategy code that should not leave the research machine

Local-first is not a fallback mode. It is a product stance: the evidence layer should remain inspectable and portable.

Dataset identity

A backtest without data identity is difficult to interpret. Corrai's data engine is built around explicit dataset metadata:

• source and provider
• market, instrument, field, and frequency
• schema version and normalization rules
• ingestion timestamp and source timestamp
• availability timestamp for point-in-time use
• missing-data policy and staleness state
• content fingerprint for reproducibility

Those details matter because small data changes can change a strategy verdict. A different funding-rate feed, a revised OHLCV candle, a missing delisting, or a backward-filled alternative data field can turn a weak signal into an impressive but false backtest.

Point-in-time availability

Point-in-time data is one of the most important phrases in quant SEO because it captures a real failure mode. The question is not only "what value describes that date?" The question is "when could a live system have known that value?"

For example, a daily on-chain metric stamped with Monday may not be available until Tuesday after indexing. A fundamentals field may be revised later. A crypto exchange candle may be restated after outage recovery. If the backtest uses the final corrected value on the date it describes, it may trade with information from the future.

Corrai treats availability semantics as part of the dataset, not as a footnote. For more detail, see Point-in-Time Data Lineage.

Staleness and coverage checks

Data quality failures are often quiet. A feed can stop updating, a symbol can disappear, a field can change units, or a vendor can revise history. A local-first data engine should surface these conditions before the strategy result is interpreted.

Corrai's data workflow is designed to support:

• feed staleness checks
• canonical OHLCV coverage checks
• schema drift detection
• symbol mapping review
• market calendar and session alignment
• reproducible feature materialization

The output is a stronger evidence chain. If a strategy fails, the team can tell whether it failed because the idea was weak or because the input data was not fit for inference.

Connection to the Judge

The Judge cannot validate what the data layer cannot describe. Data lineage, availability time, and version identity flow into every evidence package. That makes the data engine foundational for evidence-based alpha validation, AI quant research, and backtest overfitting control.

For the broader workstation architecture, see AI Quant Research Workstation.