Update README.md

README.md

@@ -1,3 +1,109 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- time-series-forecasting
+tags:
+- finance
+pretty_name: Apogée
+size_categories:
+- 100M<n<1B
+---
+<div align="center">
+  <a href="https://www.duonlabs.com" target="_blank">
+    <img src="https://www.duonlabs.com/theme/images/duon_white.png" width="30%" alt="Duon Labs Logo" />
+  </a>
+</div>
+<h1 align="center" style="font-size: 3rem;">Apogée: Crypto Market Candlestick Dataset</h1>
+<hr>
+
+## Overview
+
+Most traders believe crypto is random, but deep learning scaling laws suggest otherwise. Apogée is an open-source research initiative exploring the **scaling laws of crypto market forecasting**. While financial markets are often assumed to be unpredictable, modern deep learning suggests that increasing data and compute could uncover measurable predictability.
+Our goal is to **quantify how many bits of future price movement can be inferred** from historical candlestick data.
+[More informations on Apogée](https://www.duonlabs.com/apogee)
+
+This dataset serves as the foundation for training large-scale deep learning models on historical crypto market data.
+
+## Dataset Description
+
+The dataset contains **1-minute interval candlestick data** for the top 10 cryptocurrencies by market capitalization, sourced from Binance. It is stored in an optimized format that allows for high-performance training and multi-scale aggregation.
+
+### Assets Included:
+- **BTCUSDT** (Bitcoin)
+- **ETHUSDT** (Ethereum)
+- **XRPUSDT** (XRP)
+- **BNBUSDT** (Binance Coin)
+- **SOLUSDT** (Solana)
+- **DOGEUSDT** (Dogecoin)
+- **ADAUSDT** (Cardano)
+- **TRXUSDT** (Tron)
+- **LINKUSDT** (Chainlink)
+- **AVAXUSDT** (Avalanche)
+
+### Data Properties:
+Total dataset size:  
+- **~33 million candles**  
+- **~660 million tokens** (after uint8 tokenization)
+
+## Storage Format
+
+The dataset is stored as **NumPy memory-mapped buffers** (`.npy`) to allow for **efficient streaming and real-time aggregation**. This approach enables high-speed data access without requiring the full dataset to be loaded into RAM.
+This efficiency allows **real-time lazy aggregation** to generate different timeframes on demand
+
+### Baseline implementation
+The official dataloader used in project apogee is available at: [https://github.com/duonlabs/apogee/blob/master/apogee/data/loading.py](https://github.com/duonlabs/apogee/blob/master/apogee/data/loading.py)
+
+We tested performances under:
+- **batch_size**: 32
+- **context_size**: 480 (tokens)
+- **Aggregation**: 1m, 5m, 30m, 2h, 8h, 1d
+
+On a consumer-grade laptop with an SSD:
+- **225.19 batches/sec**
+- **7,205.92 samples/sec**
+- **3,458,842.60 tokens/sec**
+
+## Applications
+
+This dataset is designed for training **deep learning models** on crypto price prediction, particularly in the context of **scaling law research**. Potential applications include:
+
+- **Autoregressive price forecasting** using models like **Transformers** or **State-Space Models** (SSMs).
+- **Analyzing predictability limits** in crypto markets.
+- **Developing trading algorithms** based on learned patterns in candlestick sequences.
+- **Exploring market efficiency** by testing if deep learning models can systematically extract information from past price movements.
+- **Scaling law analysis** to determine how predictive power improves with increased dataset size and model capacity.
+
+## Binance Data Disclaimer
+
+Please note that all our data services strictly follow the [Binance Terms of Use](https://www.binance.com/en/support/faq/how-to-download-historical-market-data-on-binance-5810ae42176b4770b880ce1f14932262)
+> Without written consent from Binance, the following commercial uses of Binance data are prohibited:
+> 
+> - Trading services that make use of Binance quotes or market bulletin board information.
+> - Data feeding or streaming services that make use of any market data of Binance.
+> - Any other websites/apps/services that charge for or otherwise profit from (including through advertising or referral fees) market data obtained from Binance.
+> 
+> You hereby understand and agree that Binance will not be liable for any losses or damages arising out of or relating to:
+> - (a) Any inaccuracy, defect, or omission of digital asset price data.
+> - (b) Any error or delay in the transmission of such data.
+> - (c) Interruption in any such data.
+> - (d) Regular or unscheduled maintenance carried out by Binance and service interruption and change resulting from such maintenance.
+> - (e) Any damages incurred by other users’ actions, omissions, or violation of these terms.
+> - (f) Any damage caused by illegal actions of third parties or actions without authorization by Binance.
+> - (g) Other exemptions mentioned in disclaimers and platform rules issued by Binance.
+
+## Citation & References
+
+If you use this dataset in your research, please cite the Apogée project:
+```
+@misc{apogee2025,
+  title={Apogée: Scaling Laws for Crypto Market Forecasting},
+  author={Duon Labs},
+  year={2025},
+  url={https://github.com/duonlabs/apogee}
+}
+```
+
+
+For more details, refer to:
+- **Apogée GitHub Repo**: [https://github.com/duonlabs/apogee](https://github.com/duonlabs/apogee)
+- **Apogée Community**: [https://t.me/DuonLabs](https://t.me/DuonLabs)