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ETFs Daily Price
This dataset includes daily price data for various ETFs.
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 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 | +1.00 | 5.8 | 33 years |
| Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA) | +0.93 | 5.6 | 36 years |
| 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 | +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.
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:
pip install --upgrade pwb-toolbox
Load the Dataset:
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. The same subscription covers the other datasets in this organisation.
Elsewhere
- Dataset page and coverage charts
- The strategy catalogue, 3,806 papers and 4,837 replicated strategies
pwb-toolbox, the loader used in the snippet aboveawesome-systematic-trading, the replicated strategies with their measured Sharpe- Every dataset in this organisation
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.
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