Revise README.md to emphasize long-term data accumulation and clarify data philosophy

README.md

@@ -7,84 +7,86 @@ sdk: static
 pinned: false
 ---
 
-# 📊 Traders-Lab — Open Financial Time Series Data
+# 📊 Traders-Lab — Accumulated Financial Time Series
 
-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 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 primary goal is not short-term freshness, but **long-term continuity and gap-free historical depth**, especially for minute-level data.
+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.
 
 ---
 
-## 📢 Announcement
+## 🧭 Core Principle: Accumulation over Freshness
 
-**A major update will be released today (December 17. 2025) after the US market close.**
+High-quality intraday market data is readily available only in short rolling windows from most public sources.
 
-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.
+Typical access patterns provide:
+- **Daily candles** over long historical ranges
+- **Hourly candles** with limited depth
+- **Minute-level data** restricted to a few recent days
 
-Earlier *Preliminary* datasets will remain available temporarily to allow a smooth transition.
+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.
 
 ---
 
-## 🔑 Core Focus: Accumulated Minute-Level Data
+## 🧱 Data Philosophy
 
-High-quality **minute-resolution OHLC data over long time spans** is difficult to obtain from free sources.
+The datasets published here follow a small set of strict principles:
 
-Typical public data access (e.g. via yfinance) provides:
+- **Continuity over update frequency**  
+  Updates extend existing time series rather than replacing them.
 
-* **Daily candles:** often spanning decades
-* **Hourly candles:** approximately one year into the past
-* **Minute candles:** typically limited to the most recent 7 days
+- **Structure over convenience**  
+  Data is kept uniform across markets and timeframes.
 
-This makes freshly downloaded minute data unsuitable for training models that rely on **historical intraday patterns**.
+- **Archival integrity**  
+  Once recorded, historical data is preserved as part of a growing ledger.
 
-The key value of the datasets published here lies in **continuous accumulation**:
+- **Responsible sourcing**  
+  Public data sources are used conservatively, avoiding unnecessary repeated requests.
 
-* 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
+Freshness is treated as a *secondary concern*; continuity is the primary guarantee.
 
 ---
 
-## 🔄 Update Philosophy
+## ⏱️ Update Rotation & Granularity
 
-The primary guarantee is **data continuity**, not update frequency.
+To preserve long-term continuity while keeping data collection sustainable:
 
-Specifically:
+- **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
 
-* 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
+Update timing and frequency are intentionally flexible and may vary over time as data sources, markets, and operational constraints change.
 
-All data updates are designed to **extend existing time series**, not to replace them.
+In practical applications, models trained on these datasets are expected to consume **live data from their execution environment**, not from the archive itself.
 
 ---
 
-## ⏱️ Update Rotation & Data Freshness
-
-To balance data quality, processing time, and responsible use of public data sources:
+## 🎯 Intended Use
 
-* **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**
+The datasets are designed for:
 
-This approach significantly reduces unnecessary repeated requests while remaining fully sufficient for training purposes.
+- 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
 
-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.
+They are **not** designed to provide trading signals, indicators, opinions, or market commentary.
 
 ---
 
-## 🎯 Intended Use
-
-The datasets are intended for:
+## 🗂️ Primary Dataset Line: TroveLedger
 
-* 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
+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
 
-## 🔍 Further Information
+Each market is added deliberately and preserved as part of an expanding historical record.
 
-Detailed structure descriptions, usage examples, and dataset-specific notes can be found in the individual dataset cards.
+Detailed market coverage, recent additions, and dataset-specific notes are documented in the TroveLedger dataset card.