metadata

license: mit
language: en

Bitcoin Price Prediction Dataset (60d History -> 4d Forecast)

This dataset is designed for fine-tuning Large Language Models (LLMs) on a time-series forecasting task. Each sample contains 60 days of historical financial and social media data to predict the next 4 days of Bitcoin's closing price.

Dataset Description

The dataset was generated by combining two primary sources:

Financial Data: Historical daily prices for Bitcoin (BTC), Gold (GC=F), Crude Oil (CL=F), S&P 500 (^GSPC), and the US Dollar Index (DX-Y.NYB) were fetched from Yahoo Finance.
Social Media Data: The Bitcoin Tweets Dataset was used to extract daily tweet volume and sample tweet texts.

Features Included in the Prompt:

60-Day Historical BTC Prices: The core time-series data.
Technical Indicators: 14-day RSI, 50-day EMA, and 200-day EMA for Bitcoin.
Macroeconomic Context: Daily closing prices of Gold, Oil, S&P 500, and the US Dollar Index.
Social Media Sentiment: A sample of 4 tweets for the most recent day in the historical window.

Data Fields

The dataset is structured for instruction fine-tuning and contains three columns:

instruction: A string containing the comma-separated closing prices of Bitcoin for the last 60 days.
input: A detailed text block containing all the contextual information (technical indicators, macro data, sample tweets) for the most recent day of the historical period.
output: A string containing the comma-separated closing prices of Bitcoin for the next 4 days (the prediction target).

Splits

train: The training split (train_dataset.json).
test: The validation/test split (val_dataset.json).

Intended Use

This dataset is intended to be used with the SFTTrainer from the TRL library or similar frameworks to fine-tune models like Llama 3 for complex, multi-modal time-series forecasting tasks.

Example prompt structure:

{{
  "instruction": "45123.45, 45321.89, ... (60 days of prices)",
  "input": "Based on the historical data from the last 60 days, predict the Bitcoin closing prices for the next 4 days. The prediction period starts on 2023-11-20 (Weekday). The most recent day's technical analysis (2023-11-19): ...",
  "output": "46123.78, 46050.12, 46300.50, 45987.32"
}}