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| title: README | |
| emoji: 📉 | |
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| # 📊 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. | |
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| ## 📢 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. | |
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| ## 🔑 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 | |
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| ## 🔄 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. | |
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| ## ⏱️ 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. | |
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| ## 🎯 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 | |
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| ## 🔍 Further Information | |
| Detailed structure descriptions, usage examples, and dataset-specific notes can be found in the individual dataset cards. | |