---
title: "US Bureau of Labor Statistics (BLS)"
description: "US Bureau of Labor Statistics (BLS) dataset from Bureau of Labor Statistics."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/bureau-of-labor-statistics/us-bureau-of-labor-statistics-(bls)
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/bureau-of-labor-statistics/us-bureau-of-labor-statistics-(bls).md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/14%20Datasets/09%20Bureau%20of%20Labor%20Statistics/01%20US%20Bureau%20of%20Labor%20Statistics%20%28BLS%29
modified: 2026-11-13
index: https://www.quantconnect.com/llms.txt
---

# US Bureau of Labor Statistics (BLS)

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Datasets > [Bureau of Labor Statistics](https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/bureau-of-labor-statistics.md) > US Bureau of Labor Statistics (BLS)

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

The US Bureau of Labor Statistics (BLS) tracks key U.S. macroeconomic indicators across four major surveys: Consumer Price Index (CPI), Current Employment Statistics (CES), Producer Price Index (PPI), and Job Openings and Labor Turnover Survey (JOLTS). The data covers 50 non-seasonally-adjusted series, starting in January 2000, and is delivered on a daily frequency (monthly releases with exact publication timestamps). This dataset is created by analyzing BLS release schedules for accurate publication dates and fetching series data from the [Public Data API](https://www.bls.gov/developers/api_signature_v2.htm).

For more information about the US Bureau of Labor Statistics (BLS) dataset, including CLI commands and pricing, see the [dataset listing](https://www.quantconnect.com/datasets/us-bureau-of-labor-statistics).

## About the Provider

The BLS was established in 1884 as a division of the Department of the Interior, with the goal of collecting and disseminating essential economic data about the U.S. labor market and price changes. BLS provides access to employment, inflation, compensation, and productivity data for researchers, policymakers, and financial professionals.

## Getting Started

The following snippet demonstrates how to request data from the BLS dataset:

```python
self.add_data(BLSEconomicSurveysCpi, "CPI")
self.add_data(BLSEconomicSurveysCes, "CES")
self.add_data(BLSEconomicSurveysPpi, "PPI")
self.add_data(BLSEconomicSurveysJolts, "JOLTS")
```

## Data Summary

The following table describes the dataset properties:

| Property | Value |
| --- | --- |
| Start Date | January 2000 (CPI/CES/PPI), November 2007 (JOLTS) |
| Data Density | Sparse |
| Survey Coverage | 50 non-seasonally-adjusted series across 4 surveys |
| Resolution | Daily |
| Timezone | New York |

The BLS database contains over 77 million series from over 60 different surveys, but not all of them are integrated into QuantConnect. The following table show the integrated surveys:

| Ticker | Survey | Release Time (ET) | Start Date |
| --- | --- | --- | --- |
| CPI | Consumer Price Index | 08:30 AM | Jan 2000 |
| CES | Current Employment Statistics | 08:30 AM | Jan 2000 |
| PPI | Producer Price Index | 08:30 AM | Jan 2000 |
| JOLTS | Job Openings and Labor Turnover | 10:00 AM | Nov 2007 |

## Requesting Data

To add BLS data to your algorithm, call the `add_data` method. Save a reference to the dataset `Symbol` so you can access the data later in your algorithm.

```python
class BLSDataAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2020, 1, 1)
        self.set_end_date(2024, 1, 1)
        self.set_cash(100000)
        self._cpi = self.add_data(BLSEconomicSurveysCpi, "CPI").symbol
        self._ces = self.add_data(BLSEconomicSurveysCes, "CES").symbol
        self._ppi = self.add_data(BLSEconomicSurveysPpi, "PPI").symbol
        self._jolts = self.add_data(BLSEconomicSurveysJolts, "JOLTS").symbol
```

## Accessing Data

To get the current BLS data, index the current [`Slice`](https://www.quantconnect.com/docs/v2/writing-algorithms/key-concepts/time-modeling/timeslices.md) with the dataset `Symbol`. `Slice` objects deliver unique events to your algorithm as they happen, but the `Slice` may not contain data for your dataset at every time step. To avoid issues, check if the `Slice` contains the data you want before you index it.

```python
def on_data(self, data: Slice) -> None:
    if self._cpi in data:
        cpi = data[self._cpi]
        all_items = cpi.all_items
        core = cpi.core_cpi
        energy = cpi.energy
```

## Historical Data

To get historical BLS data, call the `history` method with the dataset `Symbol`. If there is no data in the period you request, the history result is empty.

```python
# DataFrame
history_df = self.history(self._cpi, 100, Resolution.DAILY)

# Dataset objects
history_bars = self.history[BLSEconomicSurveysCpi](self._cpi, 100, Resolution.DAILY)
```

For more information about historical data, see [History Requests](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/history-requests.md).

## Remove Subscriptions

To remove your subscription to BLS data, call the `remove_security` method.

```python
self.remove_security(self._cpi)
```

## Example Applications

The BLS dataset provides unique alternative data that the US government releases. Example applications of this dataset include the following strategies:

- Monitoring month-over-month changes in nonfarm payrolls and sector employment to identify economic regime shifts.
- Tracking the inflation rate of gasoline to forecast the performance of electric vehicle (EV) manufacturers on the premise that rising gas prices will increase the revenue and stock prices of EV companies.
- Combining CPI, PPI, and CES data to build composite economic health indicators.

### Classic Algorithm Example

The following example algorithm rotates to an energy ETF when energy prices are rising faster than core CPI. Otherwise, it holds the market benchmark.

```python
from AlgorithmImports import *

class BLSEconomicSurveysAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2023, 1, 1)
        self.set_end_date(2026, 3, 1)
        self.set_cash(100000)
        # Add some Equities to trade.
        self._market = self.add_equity('SPY')
        self._energy = self.add_equity('XLE')
        # Add the BLS CPI survey data.
        self._cpi = self.add_data(BLSEconomicSurveysCpi, "CPI").symbol
        # Add a warm-up period so we have the most recent CPI survey on deployment.
        self._target = None
        self.set_warm_up(timedelta(60))

    def on_data(self, data: Slice):
        # Check for CPI data.
        if data.contains_key(self._cpi):
            cpi = data.get(BLSEconomicSurveysCpi, self._cpi)
            # Simple signal: if energy CPI is rising faster than core, rotate to the energy ETF.
            if cpi.energy is not None and cpi.core_cpi is not None and cpi.energy > cpi.core_cpi * 1.5:
                self._target = self._energy
            else:
                self._target = self._market
        # Rebalance whenever the target changes.
        if not self.is_warming_up and self._target and not self._target.invested:
            self.set_holdings(self._target, 1, True)
```

### Framework Algorithm Example

The following example algorithm rotates to energy stocks in the OEF ETF when energy prices are rising faster than core CPI. Otherwise, it holds an equal-weighted portfolio of the ETF's constituents.

```python
from AlgorithmImports import *

class BLSEconomicSurveysAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2023, 1, 1)
        self.set_end_date(2026, 3, 1)
        self.set_cash(100000)
        # Add the framework models.
        self.add_universe_selection(ETFConstituentsUniverseSelectionModel('OEF'))
        self.add_alpha(CpiAlphaModel(self))
        self.set_portfolio_construction(InsightWeightingPortfolioConstructionModel())

class MyAlphaModel(AlphaModel):

    def __init__(self, algorithm):
        # Set up a member to track the universe.
        self._universe = []
        # Add the BLS CPI survey data.
        self._cpi = algorithm.add_data(BLSEconomicSurveysCpi, "CPI").symbol

    def update(self, algorithm: QCAlgorithm, data: Slice) -> List[Insight]:
        # Get the latest CPI survey.
        if data.contains_key(self._cpi):
            cpi = data.get(BLSEconomicSurveysCpi, self._cpi)
            # Simple signal: if energy CPI is rising faster than core, rotate to the energy stocks.
            target_energy = cpi.energy is not None and cpi.core_cpi is not None and cpi.energy > cpi.core_cpi * 1.5:
            securities = []
            for security in self._universe:
                if target_energy:
                    if security.fundamentals.asset_classification.morningstar_sector_code == MorningstarSectorCode.ENERGY:
                        securities.append(security)
                else:
                    securities.append(security)
            # Emit monthly insights for the selected stocks.
            return [Insight.price(s, timedelta(35), InsightDirection.UP, weight=1/len(securities)) for s in securities]
        return []

    # As assets enter and leave the universe, update our self._universe member.
    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            self._universe.append(security)
        for security in changes.removed_securities:
            self._universe.remove(security)
```

## Data Point Attributes

The BLS dataset provides `BLSEconomicSurveysCpi`, `BLSEconomicSurveysCes`, `BLSEconomicSurveysPpi`, and `BLSEconomicSurveysJolts` objects.

### BLSEconomicSurveysCpi Attributes

`BLSEconomicSurveysCpi` objects have the following attributes:

*Members of `QuantConnect.DataSource.BLSEconomicSurveysCpi` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.DataSource.BLSEconomicSurveysCpi&language=python*

### BLSEconomicSurveysCes Attributes

`BLSEconomicSurveysCes` objects have the following attributes:

*Members of `QuantConnect.DataSource.BLSEconomicSurveysCes` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.DataSource.BLSEconomicSurveysCes&language=python*

### BLSEconomicSurveysPpi Attributes

`BLSEconomicSurveysPpi` objects have the following attributes:

*Members of `QuantConnect.DataSource.BLSEconomicSurveysPpi` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.DataSource.BLSEconomicSurveysPpi&language=python*

### BLSEconomicSurveysJolts Attributes

`BLSEconomicSurveysJolts` objects have the following attributes:

*Members of `QuantConnect.DataSource.BLSEconomicSurveysJolts` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.DataSource.BLSEconomicSurveysJolts&language=python*