---
license: apache-2.0
viewer: false
task_categories:
- time-series-forecasting
tags:
- finance
pretty_name: Apogée
size_categories:
- 10B<n<100B
---
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<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 100 crypto pairs, sourced from Binance. It is stored in an optimized format that allows for high-performance training and multi-scale aggregation.

### Assets Included:
- **BTC/USDT** (Bitcoin)
- **ETH/USDT** (Ethereum)
- **SOL/USDT** (XRP)
- **XRP/USDT** (Binance Coin)
- **DOGE/USDT** (Solana)
- **LTC/USDT** (Dogecoin)
- **BNB/USDT** (Cardano)
- **ADA/USDT** (Tron)
- **AVAX/USDT** (Chainlink)
- **LINK/USDT** (Avalanche)

and [many more](https://huggingface.co/datasets/duonlabs/apogee/raw/main/metadata.csv)

### Data Properties:
Total dataset size:  
- **~2.188 billion candles**  
- **~43.76 billion 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**: 960 (tokens)
- **Aggregation**: 1m, 5m, 30m, 2h, 8h, 1d

On a consumer-grade laptop with an SSD:
- **254.79 batches/sec**
- **8,153.39 samples/sec**
- **3,921,782.52 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)