📊 Traders-Lab — Accumulated Financial Time Series

Traders-Lab publishes public financial time series datasets with a deliberate focus on long-term accumulation, structural consistency, and historical depth, rather than short-term freshness.

The organization exists to build and maintain datasets that grow quietly and continuously over time — forming a reliable archival foundation for research, modeling, and long-horizon analysis.

🧭 Core Principle: Accumulation over Freshness

High-quality intraday market data is readily available only in short rolling windows from most public sources.

Typical access patterns provide:

• Daily candles over long historical ranges
• Hourly candles with limited depth
• Minute-level data restricted to a few recent days

Such data is unsuitable for workflows that depend on historical intraday structure, regime shifts, or long-term pattern persistence.

Traders-Lab addresses this limitation by accumulating minute-level OHLC data incrementally, day by day.
Over time, this approach produces months and eventually years of gap-free intraday history — something that cannot be reconstructed retroactively.

🧱 Data Philosophy

The datasets published here follow a small set of strict principles:

• Continuity over update frequency
  Updates extend existing time series rather than replacing them.

• Structure over convenience
  Data is kept uniform across markets and timeframes.

• Archival integrity
  Once recorded, historical data is preserved as part of a growing ledger.

• Responsible sourcing
  Public data sources are used conservatively, avoiding unnecessary repeated requests.

Freshness is treated as a secondary concern; continuity is the primary guarantee.

⏱️ Update Rotation & Granularity

To preserve long-term continuity while keeping data collection sustainable:

• Minute-level data is updated most frequently to minimize the risk of gaps
• Hourly and daily data follow a relaxed, rotation-based schedule
• Non-minute data is maintained to remain reasonably recent, without aiming for real-time freshness

Update timing and frequency are intentionally flexible and may vary over time as data sources, markets, and operational constraints change.

In practical applications, models trained on these datasets are expected to consume live data from their execution environment, not from the archive itself.

🎯 Intended Use

The datasets are designed for:

• machine learning on financial time series
• intraday and swing trading research
• feature engineering on accumulated OHLC data
• backtesting strategies that benefit from dense intraday history

They are not designed to provide trading signals, indicators, opinions, or market commentary.

🗂️ Primary Dataset Line: TroveLedger

The principles outlined above are realized in TroveLedger, the primary dataset line maintained by Traders-Lab.

TroveLedger is a structured, continuously expanding collection of market indices and exchanges, unified by:

• consistent OHLC schemas
• multiple time resolutions
• long-term intraday accumulation

Each market is added deliberately and preserved as part of an expanding historical record.

Detailed market coverage, recent additions, and dataset-specific notes are documented in the TroveLedger dataset card.