SCID file

What it is

A SCID file is Sierra Chart's native binary tick-storage format — the .scid extension. Each file contains the per-tick history for one instrument: every trade, with its timestamp, price, size, and bid/ask side, written sequentially as the data feed delivers it. The platform reads SCID files to build bars at any timeframe (one-minute, range, tick, volume, renko, etc.) — the bars are derived; the SCID is the raw source of truth.

The format is compact and append-only. Each tick is a fixed-size record: a SCDateTime timestamp, four float values for OHLC (used to encode tick-level price and size depending on the record type), and accessory fields for bid/ask volume and trade count. New ticks are appended at the end as they arrive; historical ticks are never rewritten. This makes the file both efficient to grow and predictable to parse from any offset.

SCID files live in the configured Data folder of the Sierra Chart installation — typically C:\SierraChart\Data\ — one file per symbol. A single instrument's full multi-year tick history can fit in a few hundred megabytes; a high-volume contract like ES front-month accumulates a couple of GB over its active life.

Why it matters

SCID is what makes Sierra Chart's offline analytical capability so deep. Once you have a SCID file, every study, every chart, every replay session can run against the full tick-resolution history without re-downloading data from the feed. Most professional Sierra Chart users archive SCID files for instruments they trade so they can backtest, replay, and reconfigure indefinitely.

The format is also a well-known stable interface for external tools. Sierra Chart's documentation describes the SCID structure publicly, and third-party tools can parse SCID files directly without going through the platform — useful for ingesting tick data into Python, R, custom backtesters, or external analytical pipelines.

For SCS specifically, the SCID format matters because the Trading Journal app reads SCID files directly to reconstruct intra-trade price action. When a trade has an entry timestamp and an exit timestamp, the Journal extracts every tick between them from the symbol's SCID file and computes the per-tick P&L curve, MFE (maximum favorable excursion), and MAE (maximum adverse excursion). This is what enables tick-precise post-trade analytics.

How it appears on Sierra Chart

SCID files are managed transparently by the platform — you rarely need to interact with them directly. Sierra Chart downloads historical ticks on demand the first time a chart is opened on a new symbol, then appends real-time ticks as they arrive. The Data Folder Manager (under file or settings menus, depending on platform version) provides utilities to inspect, compact, or relocate the data folder.

For ACSIL developers, tick-level data is exposed through the bar arrays (sc.Close, sc.Volume, etc.) at whatever bar resolution the chart is set to, plus through tick-specific helpers if you need finer-than-bar access. The SCID file itself is the underlying store but you read it through the platform's API rather than directly.

Common patterns / pitfalls

• Back up the Data folder — losing SCID files means re-downloading from the data feed, which is rate-limited and often capped by the vendor at recent history only.
• Symbol consistency — different data feeds use different symbol prefixes (e.g., Rithmic vs CQG). Switching feeds creates new SCID files for the same instrument under a different filename.
• File size grows monotonically — SCID files do not shrink. Old data can be archived off but not compacted in place.
• Don't edit SCID files manually — they are append-only by design. Manual edits risk corrupting the platform's read offsets.
• External readers exist — the SCID format is documented; tools like Sierra Chart's own DTC protocol, third-party Python parsers, and journaling apps (including the SCS Trading Journal) read it directly.

Related SCS studies

The Trading Journal (the Electron app, not an ACSIL study) reads SCID files to reconstruct intra-trade tick data for every recorded trade — this is how it computes per-second equity curves, MFE, MAE, and the time-to-MFE statistics that drive its post-trade analytics.