---
dataset_info:
  features:
  - name: timestamp
    dtype: string
  - name: ticker
    dtype: string
  - name: open
    dtype: float64
  - name: high
    dtype: float64
  - name: low
    dtype: float64
  - name: close
    dtype: float64
  - name: volume
    dtype: int64
  - name: source
    dtype: string
  - name: retreived
    dtype: string
  splits:
  - name: train
    num_bytes: 32020428
    num_examples: 270846
  download_size: 12429410
  dataset_size: 32020428
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: gpl-3.0
task_categories:
- time-series-forecasting
pretty_name: BigStock
tags:
- timeseries
- economics
---
# BigStock

This dataset contains historical data for a sample of 79 stock symbols.
The data spans a period of 720+ days, at one-hour intervals.
All values are in USD.
It includes full candlestick data (open, close, low, high) as well as volume data.
The data (not scraping/processing code) is licensed under GPL v3.

## Other Datasets
This a preview of the full BigStock dataset.
I may also release a multivariate forecasting model trained on the data, likely based on Tiny Time Mixers or a custom architecture I have been evaluating.

## Uses

The data is usable for the following cluster of tasks:
- Time series forecasting
- Financial modeling
- Reinforcement Learning

## Limitations

While its breadth is very large, the data is comparatively very short-term (~2yrs).
Controlling for biases in economic data is inherently a difficult problem, because we only have one market to sample from!
Therefore, models trained on this data may be biased towards recent market trends.

This dataset may have missing, incorrect, or unreliable data. 
You are encouraged to validate it before use. 

## Dataset Creation

The source data in this sample was provided purely from the Yahoo Finance website.
Data was collected using the [yfinance](https://github.com/ranaroussi/yfinance/) library. 

The collection and processing was done on hardware owned and operated entirely by me.