BUILD / 03 — PRODUCT OVERVIEW
POINT-IN-TIME SAFE MARKET-DATA INFRASTRUCTURE
QUANT MARKET DATA REPLAY
Point-in-time safe market-data infrastructure for quantitative research.
A market-data infrastructure that ingests raw exchange data, normalizes, validates, quarantines and reorders it, then serves point-in-time safe access with deterministic replay — eliminating lookahead bias by construction. Exact decimal arithmetic, nanosecond timestamps, SQLite WAL storage, sliding watermarks and deterministic tie-breaking.
9
PIPELINE STAGES
RAW → ANALYTICS
ns
TIMESTAMP PRECISION
NANOSECOND CLOCK
0
SILENT DATA DROPS
QUARANTINE MODEL
WAL
STORAGE MODE
SQLITE
SYSTEM INSTRUMENT / DATA REPLAY
VISUAL METAPHOR — TEMPORAL DATA STREAM / REPLAY ENVIRONMENT
INSTRUMENT / POINT-IN-TIME REPLAY
STRUCTURAL DEMONSTRATION — SYNTHETIC DETERMINISTIC SERIES
POINT-IN-TIME REPLAY LAB
LOOKAHEAD VS POINT-IN-TIME SAFE
The signature research instrument. Scrub the timestamp to see the information boundary — what was knowable at t, and what only existed after t.
POINT-IN-TIME SAFE VIEW (STRUCTURAL GUARANTEE)
A query at t can only read the solid candles. The ghosted region is information that did not yet exist — in a naive backtest it silently leaks in (lookahead); in point-in-time infrastructure it is structurally unreachable. Series is deterministic and synthetic.
9-STAGE PIPELINE — RAW DATA → ANALYTICS
THE BUILD AT A GLANCE
WHAT IT IS, WHAT IT SOLVES, HOW IT IS BUILT — THE FULL EVIDENCE CHAIN LIVES IN THE CASE STUDY
THE PROBLEM
Lookahead bias is the quiet killer of quantitative research. It enters through ordinary engineering decisions: a resampled candle that includes the future tail of its window, a join that matches on post-hoc knowledge, a timestamp normalized to a coarser clock.
Once contaminated, a backtest cannot be repaired — the result must be regenerated from clean data. Prevention is the only cure, and prevention has to happen in the data layer, not the strategy layer.
WHAT IT DOES
- Build an ingestion → normalization → validation → quarantine → reorder → storage pipeline that never silently drops or fabricates data
- Serve point-in-time safe reads with a sliding watermark
- Make replay deterministic: same query, same clock, same result — including tie-breaking
- Use exact decimal arithmetic and nanosecond timestamps so precision itself cannot introduce bias
RESEARCH QUESTION
Can market-data infrastructure guarantee that a consumer at time t can only access information that existed at time t — deterministically, and at full research throughput?
ARCHITECTURE
ARCHITECTURE — DATA PATH
ARCHITECTURE DIAGRAM — THE DOCUMENTED LAYERS, NOT A MEASUREMENT
- 01
INGESTION
Raw data acquisition
- 02
NORMALIZATION
Canonical representation
- 03
VALIDATION
Integrity gates
- 04
QUARANTINE
Failure containment
- 05
REORDER
Causal ordering
- 06
STORAGE
Point-in-time ledger
- 07
PIT ACCESS
Point-in-time safe reads
- 08
DETERMINISTIC REPLAY
Reproducible research
- 09
ANALYTICS
Downstream consumption
TECHNOLOGY
KEY EVIDENCE
CURRENT STATE
RELEASED
Core infrastructure implemented; 70-test suite passes CI on Python 3.10-3.12.
LAST VERIFIED 2026-09-17 — GitHub Actions CI: full unittest suite passed on Python 3.10-3.12 · BASELINE a72d803 · VIEW THE RUN →
GO DEEPER
THE FULL EVIDENCE CHAIN
Problem, hypothesis, architecture, methodology, experiments, results — and the failures and limitations beside them. The case study is the full write-up for this build.