Update README.md

README.md

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-# Traders Lab
+# 📊 Traders-Lab — Open Financial Time Series Data
 
-🚧 **Note (from April 30. 2025) : This organization is still being set up. No datasets are available yet, but they will be uploaded soon – expected within the comming month (May 2025). Stay tuned!**
+Traders-Lab publishes **public financial time series datasets** with a strong focus on **high-quality intraday data accumulation** over extended periods of time.
 
-**Traders-lab** is a collection of free, structured, and up-to-date financial datasets for training machine learning models in algorithmic trading.
+The primary goal is not short-term freshness, but **long-term continuity and gap-free historical depth**, especially for minute-level data.
 
-## 🧠 Motivation
+---
+
+## 📢 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
+
+---
 
-Finding clean and openly licensed trading data can be hard. This project focuses on making such datasets available in different key formats:
+## 🔄 Update Philosophy
 
-- ✅ **Daily candles** over several years
-- ✅ **Hour-level candles** for at least 2 years (growing over time)
-- ✅ **Minute-level candles** for recent history
+The primary guarantee is **data continuity**, not update frequency.
 
-By providing both, I aim to support models that can learn across **multiple timescales** — understanding how coarse and fine views of the market relate.
+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
+
+---
 
-## 🛠️ Features
+## 🔍 Further Information
 
-- Consistently structured datasets for reproducibility
-- Regular updates to keep training data current
+Detailed structure descriptions, usage examples, and dataset-specific notes can be found in the individual dataset cards.