---
pretty_name: Bonds Daily Price
language:
- en
license: other
task_categories:
- time-series-forecasting
- tabular-regression
tags:
- finance
- quantitative-trading
- backtesting
- algorithmic-trading
- bonds
- fixed-income
- daily
- ohlcv
- market-data
- time-series
size_categories:
- 1M<n<10M
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: 1415096
---

# Bonds Daily Price

This dataset includes daily rate data for various bonds.

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

**1,415,096 rows** over 209 symbols, 7 columns, covering **1969-04-30 to 2026-07-31**. Refreshed monthly.

## Strategies Built on This Data

590 papers in the [Papers With Backtest catalogue](https://paperswithbacktest.com/strategies) declare this dataset as an input. 560 of them have been coded and run over their own full history. The median replicated Sharpe ratio is **+0.48**, and **59%** 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 |
| :--- | ---: | ---: | ---: |
| [Forecasting Bond Risk Premia using Stationary Yield Factors](https://paperswithbacktest.com/strategies/forecasting-bond-risk-premia-using-stationary-yield-factors) | +1.06 | 6.4 | 36 years |
| [Statistical and Economic Benefits of Whitening Residuals in Bond Yields](https://paperswithbacktest.com/strategies/statistical-and-economic-benefits-of-whitening-residuals-in-bond-yields) | +0.90 | 4.9 | 29 years |
| [Explaining low annuity demand: an optimal portfolio application to Japan](https://paperswithbacktest.com/strategies/explaining-low-annuity-demand-an-optimal-portfolio-application-to-japan) | +1.60 | 9.6 | 36 years |
| [Inflation and Stock Returns at B3](https://paperswithbacktest.com/strategies/inflation-and-stock-returns-at-b) | +1.49 | 8.8 | 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.

<!-- MEASURED:END -->

## Why It Matters

This dataset powers fixed-income and cross-asset trading workflows by:

- **Rate momentum and carry:** Daily history of global bond benchmarks supports duration timing and curve-trading signals.
- **Cross-asset hedging:** Bond moves provide defensive context for equities, credit, FX, and commodities risk overlays.
- **Long history:** 209 sovereign curves, the oldest starting in 1969, which is enough span to test a rate signal across several full cycles.

## Load It

**Installation/Upgrade:**

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

**Load the Dataset:** Optionally pass instrument tickers (e.g., `US10Y`).

```python
from pwb_toolbox import datasets as pwb_ds

df = pwb_ds.load_dataset("Bonds-Daily-Price", symbols=["US10Y"])
print(df.iloc[0, :])
```

**Example Output:**

```
symbol         US10Y
date      1985-10-25
open          10.186
high          10.186
low           10.186
close         10.186
volume             0
```

## Columns

| Column Name | Description |
| :--- | :--- |
| **symbol** | Bond or rate instrument ticker. |
| **date** | Trading date (YYYY-MM-DD). |
| **open** | Opening rate level for the session. |
| **high** | Highest rate level reached during the session. |
| **low** | Lowest rate level reached during the session. |
| **close** | Closing rate level for the session. |
| **volume** | Always zero. Rate benchmarks are quotes rather than traded instruments, so nothing is reported here. |

## What This Data Does Not Cover

**These are yields, not prices.** The 209 series are sovereign benchmark rates in percent, so `close` for `US10Y` is a yield near 4, not a bond price near 100. A total-return series has to be built from the yield and the duration; a momentum signal computed straight off `close` has the sign of a rate move, which is the opposite of the bond return it is usually taken for.

## 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/bonds-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.