---
pretty_name: Commodities Daily Price
language:
- en
license: other
task_categories:
- time-series-forecasting
- tabular-regression
tags:
- finance
- quantitative-trading
- backtesting
- algorithmic-trading
- commodities
- futures
- daily
- ohlcv
- market-data
- time-series
size_categories:
- 100K<n<1M
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
  splits:
  - name: train
    num_examples: 508406
---

# Commodities Daily Price

This dataset includes daily price data for various commodities.

**508,406 rows** over 63 symbols, 7 columns, covering **1927-12-30 to 2026-07-31**. Refreshed monthly.

## Strategies Built on This Data

259 papers in the [Papers With Backtest catalogue](https://paperswithbacktest.com/strategies) declare this dataset as an input. 231 of them have been coded and run over their own full history. The median replicated Sharpe ratio is **+0.39**, and **53%** 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 |
| :--- | ---: | ---: | ---: |
| [Financialization in Commodity Markets](https://paperswithbacktest.com/strategies/financialization-in-commodity-markets) | +0.98 | 5.8 | 35 years |
| [Long-Run Reversal in Commodity Returns: Insights from Seven Centuries of Evidence](https://paperswithbacktest.com/strategies/long-run-reversal-in-commodity-returns-insights-from-seven-centuries-of-evidence) | +0.63 | 3.7 | 35 years |
| [Return and Risk in Commodity Futures Programs](https://paperswithbacktest.com/strategies/return-and-risk-in-commodity-futures-programs) | +0.53 | 3.2 | 36 years |
| [Optimal Trading of a Basket of Futures Contracts](https://paperswithbacktest.com/strategies/optimal-trading-of-a-basket-of-futures-contracts) | +0.54 | 3.2 | 35 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 enables systematic commodity trading and hedging strategies by:

- **Cross-asset signals:** Commodity trends inform inflation hedges and macro overlays for equity, FX, and rates portfolios.
- **Mean-reversion and momentum:** Daily OHLCV history supports both breakout and carry-style strategies across energy, metals, and agriculture.
- **Risk budgeting:** Realised volatility across energy, metals, grains and softs is what position sizing and stop placement are calibrated against.

## Load It

**Installation/Upgrade:**

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

**Load the Dataset:** Pass tickers such as `CL1` or `GC1` to focus on specific contracts.

```python
from pwb_toolbox import datasets as pwb_ds

df = pwb_ds.load_dataset("Commodities-Daily-Price", symbols=["CL1"])
print(df.iloc[0, :])
```

**Example Output:**

```
symbol           CL1
date      1985-02-15
open           27.85
high           28.01
low            27.55
close           27.8
volume             0
```

## Columns

| Column Name | Description |
| :--- | :--- |
| **symbol** | Commodity ticker (e.g., energy, metals, agriculture). |
| **date** | Trading date (YYYY-MM-DD). |
| **open** | Opening price for the trading day. |
| **high** | Highest traded price during the session. |
| **low** | Lowest traded price during the session. |
| **close** | Closing settlement or reference price. |
| **volume** | Zero for 61 of the 63 symbols. The generic continuous contracts carry no volume of their own. |

## What This Data Does Not Cover

**Half the file is not a front-month future.** Of the 63 symbols, 32 are generic continuous front-month contracts, the ones with a `1` suffix such as `CL1`, `GC1` and `W1`. The rest are spot prices, physical benchmarks and indices, and three of them are not commodities at all: `INDU:IDX`, `NDX:IDX` and `FUTU:US` are equity series. `INDU:IDX` is also what makes the file start in 1927; no commodity series here begins before 1983.

**Continuous series carry the roll.** A generic front-month contract is stitched from successive expiries, so its long-run return includes the roll and is not the return of holding the physical.

## 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/commodities-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.