# Stock-pile dataset

**URL:** </static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fstock-pile-dataset%2F190699%3E%5C
**Category:** Research\
**Created:** [October 17, 2026, 8:38am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fstock-pile-dataset%2F190699 "2026-10-17T08:38:57Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![helloadhavan](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/helloadhavan/32/61071_2.png) [@helloadhavan](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fhelloadhavan)%5C
**Post date:** [October 17, 2026, 8:38am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fstock-pile-dataset%2F190699%2F1 "2026-10-17T08:38:57Z")

</div>

Hey everyone! 👋

I’d like to introduce [**Stock-pile**](https://huggingface.co/datasets/helloadhavan/Stock-pile), a comprehensive financial dataset I built that covers historical daily and weekly stock market records for over 750 top world companies.

The data spans from each company’s founding/IPO date all the way up to today, sourced via `yfinance` and Nasdaq. I initially created this dataset to train my own toy stock predictor model and am now sharing it with the community!

* * *

## Dataset Overview

- **Coverage:** 750+ major world companies by market capitalization
- **Size:** Over 5 Million rows and over 100 MB compressed parquet
- **Time Horizon:** Full historical coverage (IPO / Founding to Present)
- **Frequencies:** Daily and Weekly aggregate records
- **Sources:** Yahoo Finance (`yfinance`) & Nasdaq

it is also the biggest dataset i’ve created (in terms of rows) and the smallest dataset i’ve created in terms of compressed parquet size

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## Data Schema

The dataset includes standard OHLCV (Open, High, Low, Close, Volume) metrics alongside dividend and split histories:

| **Column Name** | **Data Type** | **Description** |
| --- | --- | --- |
| `date` | `timestamp` | Trade date |
| `frequency` | `string` | Frequency interval (`daily` or `weekly`) |
| `open` | `float64` | Opening price |
| `high` | `float64` | Highest price during the period |
| `low` | `float64` | Lowest price during the period |
| `close` | `float64` | Closing price |
| `volume` | `int64` | Total trading volume |
| `Dividends` | `float64` | Total dividends paid |
| `Stock splits` | `float32` | Split ratio/amount |

## Dataset sample

| # | Date timestamp | Open float64 | High float64 | Low float64 | Close float64 | Volume float64 | Dividends float64 | Stock Splits float64 | Ticker string |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | 2000-01-04 | 518,700 | 555,450 | 508,200 | 540,750 | 441,709 | 10,500.01 | 0 | 000660.KS |
| 1 | 2000-01-05 | 507,150 | 523,950 | 477,750 | 485,100 | 353,061 | 0 | 0 | 000660.KS |
| 2 | 2000-01-06 | 493,500 | 499,800 | 454,650 | 459,900 | 310,909 | 0 | 0 | 000660.KS |
| 3 | 2000-01-07 | 445,200 | 470,400 | 436,800 | 455,700 | 499,628 | 0 | 0 | 000660.KS |
| 4 | 2000-01-10 | 462,000 | 468,300 | 451,500 | 451,500 | 382,932 | 0 | 0 | 000660.KS |

Rows: **5,385,871** Dataset Viewer Preview

## Quickstart

you can load the dataset via `datasets`

```python
import datasets

dataset = datasets.load_dataset("helloadhavan/Stock-pile")
print(dataset)

```

Also! What kind of stock prediction model are you planning to train with this dataset?