---
pretty_name: ETFs Daily Price
language:
- en
license: other
task_categories:
- time-series-forecasting
- tabular-regression
tags:
- finance
- quantitative-trading
- backtesting
- algorithmic-trading
- etf
- equities
- daily
- ohlcv
- market-data
- time-series
size_categories:
- 10M<n<100M
extra_gated_prompt: "This dataset is free to browse and gated for download. Approval is tied to a Papers With Backtest subscription, which also covers the other datasets in this organisation and the strategy catalogue at https://paperswithbacktest.com. Plans and what each one includes: https://paperswithbacktest.com/pricing"
dataset_info:
  features:
  - name: symbol
    dtype: string
  - name: date
    dtype: string
  - name: open
    dtype: float64
  - name: high
    dtype: float64
  - name: low
    dtype: float64
  - name: close
    dtype: float64
  - name: volume
    dtype: int64
  - name: adj_close
    dtype: float64
  splits:
  - name: train
    num_examples: 10813800
---

# ETFs Daily Price

This dataset includes daily price data for various ETFs.

<!-- MEASURED:START - rewritten by dataset_cards/refresh.py -->

**10,813,800 rows** over 5,802 symbols, 8 columns, covering **1978-01-03 to 2026-08-03**. Refreshed monthly.

## Strategies Built on This Data

502 papers in the [Papers With Backtest catalogue](https://paperswithbacktest.com/strategies) declare this dataset as an input. 476 of them have been coded and run over their own full history. The median replicated Sharpe ratio is **+0.55**, and **70%** clear a t-statistic of 1.96 on their own sample, against 48% across all 4,837 replications in the library.

Some of the strongest results that lean on it:

| Strategy | Sharpe | t-stat | Tested over |
| :--- | ---: | ---: | ---: |
| [Long-term Benefits from Investing in International Real Estate](https://paperswithbacktest.com/strategies/long-term-benefits-from-investing-in-international-real-estate) | +1.00 | 5.8 | 33 years |
| [Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA)](https://paperswithbacktest.com/strategies/growth-trend-timing-and-variations-lethargic-asset-allocation-laa-) | +0.93 | 5.6 | 36 years |
| [Mutual Fund Performance Persistence, Market Efficiency, Efficiency,and Breadth and Breadth and Breadth](https://paperswithbacktest.com/strategies/mutual-fund-performance-persistence-market-efficiency-efficiency-and-breadth-and-breadth-and-breadth) | +0.93 | 5.5 | 35 years |
| [Robo-advisory: from Investing Principles and Algorithms to Future Developments](https://paperswithbacktest.com/strategies/robo-advisory-from-investing-principles-and-algorithms-to-future-developments) | +0.90 | 5.4 | 36 years |

A Sharpe ratio quoted without its t-statistic hides how much of the library cannot be distinguished from zero, which is why both are shown. The figures are in-sample over each strategy's own window and carry no transaction costs.

<!-- MEASURED:END -->

## Why It Matters

This dataset supports ETF-focused and cross-asset strategies by:

- **Portfolio construction:** Broad ETF coverage enables factor-tilted allocations and tactical rotation systems.
- **Liquidity-aware backtests:** Daily OHLCV supports realistic slippage models for tradable instruments.
- **Hedging and benchmarking:** Use ETF prices as hedges or benchmarks against single-name strategies.

## Load It

**Installation/Upgrade:**

```bash
pip install --upgrade pwb-toolbox
```

**Load the Dataset:**

```python
from pwb_toolbox import datasets as pwb_ds

df = pwb_ds.load_dataset("ETFs-Daily-Price", symbols=["SPY"])
print(df.iloc[0, :])
```

**Example Output:**

```
symbol           SPY
date      1993-01-29
volume       1003200
close      24.313049
open       24.330342
high       24.330342
low        24.209295
```

## Columns

| Column Name | Description |
| :--- | :--- |
| **symbol** | ETF ticker. |
| **date** | Trading date (YYYY-MM-DD). |
| **volume** | Trading volume. |
| **close** | Closing price for the session. |
| **open** | Opening price for the session. |
| **high** | Highest traded price during the day. |
| **low** | Lowest traded price during the day. |
| **adj_close** | Closing price adjusted for splits and distributions. `load_dataset` rescales the other three prices by `adj_close / close` and returns it as `close`, so the snippet above prints adjusted prices and no separate column. Pass `adjust=False` for the raw ones. |

## Access

Browsing the card and the schema is open to anyone. Downloading the files needs an approved request, tied to a subscription: [what each plan includes](https://paperswithbacktest.com/pricing). The same subscription covers the other datasets in this organisation.

## Elsewhere

- [Dataset page and coverage charts](https://paperswithbacktest.com/datasets/etfs-daily-price)
- [The strategy catalogue](https://paperswithbacktest.com/strategies), 3,806 papers and 4,837 replicated strategies
- [`pwb-toolbox`](https://github.com/paperswithbacktest/pwb-toolbox), the loader used in the snippet above
- [`awesome-systematic-trading`](https://github.com/paperswithbacktest/awesome-systematic-trading), the replicated strategies with their measured Sharpe
- [Every dataset in this organisation](https://huggingface.co/datasets/paperswithbacktest)

Papers With Backtest publishes 32 datasets on the Hub and codes the papers that use them. Every strategy in the catalogue is run over its own full history before it is published, which is where the numbers above come from.