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apogee

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Time Series Forecasting
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Apogée: Crypto Market Candlestick Dataset

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

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

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

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:

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