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title: README
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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.