📊 Traders-Lab — Open Financial Time Series Data

Traders-Lab publishes public financial time series datasets with a strong focus on high-quality intraday data accumulation over extended periods of time.

The primary goal is not short-term freshness, but long-term continuity and gap-free historical depth, especially for minute-level data.

📢 Announcement

A major update will be released today (December 17. 2025) after the US market close.

With this release, the long-running “Preliminary” phase will be officially concluded. A new dataset named TroveLedger will mark the transition to a stable and consolidated dataset line.

Earlier Preliminary datasets will remain available temporarily to allow a smooth transition.

🔑 Core Focus: Accumulated Minute-Level Data

High-quality minute-resolution OHLC data over long time spans is difficult to obtain from free sources.

Typical public data access (e.g. via yfinance) provides:

• Daily candles: often spanning decades
• Hourly candles: approximately one year into the past
• Minute candles: typically limited to the most recent 7 days

This makes freshly downloaded minute data unsuitable for training models that rely on historical intraday patterns.

The key value of the datasets published here lies in continuous accumulation:

• Minute-level data is collected day by day
• Over time, this results in months of gap-free minute data
• This provides a fundamentally different foundation for training and evaluation than repeatedly downloading short rolling windows

🔄 Update Philosophy

The primary guarantee is data continuity, not update frequency.

Specifically:

• Daily updates are not guaranteed
• The absence of gaps in accumulated minute data is the main objective
• Updates are performed on trading days whenever possible

All data updates are designed to extend existing time series, not to replace them.

⏱️ Update Rotation & Data Freshness

To balance data quality, processing time, and responsible use of public data sources:

• Minute data is updated most frequently to ensure continuity
• Hourly and daily data follow a rotation-based update schedule
• Hourly and daily datasets are guaranteed to be no older than one week

This approach significantly reduces unnecessary repeated requests while remaining fully sufficient for training purposes.

In real-world usage, models are typically deployed using live data feeds from the target trading platform, which naturally provide up-to-date market data.

🎯 Intended Use

The datasets are intended for:

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

🔍 Further Information

Detailed structure descriptions, usage examples, and dataset-specific notes can be found in the individual dataset cards.