tahamajs/bitcoin-prediction-context-dataset_short_term_10_more_for_each_date

tags:

financial-forecasting

time-series

instruction-tuning

bitcoin

finance license: apache-2.0

Granular Short-Term Bitcoin Price Prediction Dataset This dataset is designed for fine-tuning language models on a highly granular, short-term Bitcoin price forecasting task. The goal is to predict the next 3 days of closing prices based on a 10-day price history and a detailed context of recent news, social media buzz, and wider market indicators.

The key feature of this dataset is its granularity. For days with a high volume of news or social media activity, multiple training samples are generated. Each sample contains a unique chunk of up to 10 news articles paired sequentially with a chunk of up to 3 social media posts, allowing the model to learn from diverse, specific sets of information for the same prediction window. A maximum of 20 samples are generated per day to maintain balance.

Dataset Description The core task is to predict a 3-day future price sequence based on a 10-day historical price sequence and a rich, multi-faceted context. The dataset is structured for instruction fine-tuning, with instruction, input, and output fields.

The context provided in the input field is a snapshot of the market, including:

Recent News: A chunk of up to 10 news articles published in the 7 days prior to the prediction date.

Social Media Buzz: A chunk of up to 3 social media posts from the prediction date.

Wider Market Context: The daily closing price and RSI for Gold (GC=F) and Crude Oil (CL=F).

Data Sources Market Data: Daily closing prices, volume, and technical indicators for BTC-USD, Gold Futures (GC=F), and WTI Crude Oil Futures (CL=F) were fetched using the yfinance library.

News Articles: Sourced from the edaschau/bitcoin_news dataset on Hugging Face.

Social Media Posts: Sourced from the gauravduttakiit/bitcoin-tweets-16m-tweets-with-sentiment-tagged dataset on Kaggle.

How to Use The dataset is provided in JSON format and can be loaded directly using the datasets library.

from datasets import load_dataset

Load the dataset from the Hugging Face Hub

repoid = "tahamajs/bitcoin-short-term-granular-dataset" # <-- Replace with your repo ID dataset = loaddataset(repo_id)

Access the training split

train_data = dataset['train']

Print the first sample

print(train_data[0])

Data Structure The dataset is split into train.json and validation.json. Each entry is a JSON object with the following fields:

instruction: (string) - A directive for the language model. It includes the task description and a list of the previous 10 days of Bitcoin closing prices.

input: (string) - The context the model should use to make its prediction. This multi-line string is formatted with clear headings:

[Recent News (Sample of up to 10)]: A string containing up to 10 news articles, separated by ||.

[Social Media Buzz (Sample of up to 3)]: A string containing up to 3 social media posts, separated by ||.

[Wider Market Context for YYYY-MM-DD]: Contains the closing prices and RSI for Gold and Crude Oil for the current day.

output: (string) - The ground truth for the prediction. This is a comma-separated string of the next 3 days of Bitcoin's closing prices.

Example Data Point { "instruction": "Based on the following news, social media buzz, market context, and historical prices, predict the next 3 daily closing prices for Bitcoin. The historical prices are: 47496.81, 47178.12, 48937.09, 49150.54, 48657.49, 47653.07, 47289.08, 46444.47, 47128.01, 47743.31.", "input": "[Recent News (Sample of up to 10)]\nBitcoin could become 'unwound' in a risk-off environment, warns Guggenheim's Scott Minerd: Guggenheim's Scott Minerd said that bitcoin could face a major correction as risk assets become 'unwound.' || Bitcoin price hits $47K, but derivatives data shows traders are not so bullish: BTC price is on the verge of flipping a key S&R level to support, but derivatives data shows pro traders are not yet convinced that the local top has been set.\n\n[Social Media Buzz (Sample of up to 3)]\n#Bitcoin is looking strong today! Breaking through resistance. #BTC #Crypto || Anyone else feeling bullish on BTC right now? The charts are looking good. || Just added more #Bitcoin to my portfolio. Long term hodl!\n\n[Wider Market Context for 2021-12-28]\nGold Price: 1810.90, Gold RSI: 62.15\nOil Price: 75.98, Oil RSI: 64.32", "output": "46428.41, 47179.53, 46210.28" }

Citation If you use this dataset in your research, please consider citing it:

@misc{yourname2025bitcoingranular, author = {taha majlesi}, title = {Granular Short-Term Bitcoin Price Prediction Dataset}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/datasets/tahamajs/bitcoin-short-term-granular-dataset}}, }

Disclaimer: This dataset is intended for research and educational purposes only. Financial markets are highly volatile, and past performance is not indicative of future results. Do not use models trained on this data for actual financial trading.---

For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1

Doc / guide: https://huggingface.co/docs/hub/datasets-cards

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