economic-surveys-data

name	economic-surveys-data
description	Use whenever a strategy needs US macroeconomic survey data — CPI/inflation, nonfarm payrolls and earnings (CES), producer prices (PPI), or job openings (JOLTS). Covers the point-in-time BLS Economic Surveys datasets, their release-date semantics, the field tables for each survey, and the non-seasonally-adjusted caveat and how to handle it.

US economic-survey data — point-in-time BLS Economic Surveys

For macro signals built on US economic releases (inflation, employment, producer prices, labor turnover), use the BLS Economic Surveys datasets. They are delivered point-in-time — each data point is stamped with its actual release date — so a backtest sees a number only once it was really published, exactly as live trading would. This is the property that makes a macro-timing strategy reproducible live; do not substitute a revised/latest-vintage macro series, which would leak data a live strategy could never see.

The four integrated surveys

Subscribe in Initialize with AddData<Class>("<TICKER>") and keep the returned symbol. The class lives in QuantConnect.DataSource.

Survey	Class	Ticker	Coverage starts	Released
Consumer Price Index	BLSEconomicSurveysCpi	"CPI"	Jan 2000	~monthly, 08:30 ET
Current Employment Statistics	BLSEconomicSurveysCes	"CES"	Jan 2000	~monthly, 08:30 ET
Producer Price Index	BLSEconomicSurveysPpi	"PPI"	Jan 2000	~monthly, 08:30 ET
Job Openings & Labor Turnover	BLSEconomicSurveysJolts	"JOLTS"	Nov 2007	~monthly, 10:00 ET

_cpi = AddData<BLSEconomicSurveysCpi>("CPI").Symbol;

The coverage start bounds your backtest — a CPI strategy cannot start before Jan 2000 on this data, and a signal that needs N years of history to warm up effectively begins that much later. If the backtest start precedes coverage, hold the defensive/cash leg until the data exists rather than idling on empty signals.

Accessing the data — live and history (verified by probe)

Live, in OnData, read the named property off the data point and key off EndTime:

public override void OnData(Slice slice)
{
    if (slice.ContainsKey(_cpi))
    {
        var point = slice[_cpi];
        var allItems = point.AllItems;       // headline CPI index level
        var releaseDate = point.EndTime;     // the real BLS release date (point-in-time)
    }
}

To seed a signal from history, prefer the typed (dataset-object) history overload — it returns the actual data-point objects, so you read the SAME .AllItems / .EndTime properties as in OnData:

foreach (var point in History<BLSEconomicSurveysCpi>(_cpi, 2000, Resolution.Daily))
{
    var level = point.AllItems;          // same property name as live
    var releaseDate = point.EndTime;     // real release date
}

Notes:

• These are monthly releases, so a count-based daily history request must be large — a few-day window returns nothing; request enough trading days to span the months you need.
• A history request returns points from before the start date, so seed once at startup rather than idling.
• The value each point carries describes the prior month (a CPI point released mid-May is April's CPI). Every named field is an index level or a count (not a rate of change) — compute MoM/YoY/etc. yourself.

Common fields — every survey

These base fields are present on every BLS Economic Surveys data point (CPI, CES, PPI, JOLTS).

Field	Description
EndTime	The actual BLS release date of this observation (point-in-time). Date every observation by this.
Time	LEAN timestamp — unreliable for these datasets; do not use it to date the observation (use EndTime).
Value	Generic BaseData value — not a specific named series here; read one of the named fields below instead.

CPI fields — BLSEconomicSurveysCpi

All series non-seasonally-adjusted, base period 1982-84=100.

Field	Description
AllItems	Headline All-Items CPI index (series CUUR0000SA0)
CoreCpi	All items less food and energy — Core CPI (CUUR0000SA0L1E)
Food	Food
FoodAtHome	Food at home
FoodAwayFromHome	Food away from home
Energy	Energy
Gasoline	Gasoline (all types)
Shelter	Shelter
RentOfPrimaryResidence	Rent of primary residence
MedicalCare	Medical care
Apparel	Apparel
EducationAndCommunication	Education and communication
NewVehicles	New vehicles
UsedCarsAndTrucks	Used cars and trucks
CollegeTuitionAndFees	College tuition and fees

CES (employment) fields — BLSEconomicSurveysCes

All series non-seasonally-adjusted, from the monthly Employment Situation report.

Field	Description
TotalNonfarm	Total nonfarm payrolls, all employees (thousands)
TotalPrivate	Total private, all employees (thousands)
Manufacturing	Manufacturing, all employees (thousands)
GoodsProducing	Goods-producing, all employees (thousands)
PrivateServiceProviding	Private service-providing, all employees (thousands)
Construction	Construction, all employees (thousands)
RetailTrade	Retail trade, all employees (thousands)
FinancialActivities	Financial activities, all employees (thousands)
EducationAndHealthServices	Education and health services, all employees (thousands)
LeisureAndHospitality	Leisure and hospitality, all employees (thousands)
MiningAndLogging	Mining and logging, all employees (thousands)
AverageHourlyEarnings	Average hourly earnings, total private (dollars)
AverageWeeklyHours	Average weekly hours, total private (hours)
AverageWeeklyEarnings	Average weekly earnings, total private (dollars)
ProductionHourlyEarnings	Average hourly earnings, production & nonsupervisory (dollars)
ProductionEmployees	Production & nonsupervisory employees, total private (thousands)

PPI fields — BLSEconomicSurveysPpi

All series non-seasonally-adjusted. Base date varies by series (noted below).

Field	Description
FinalDemand	Headline Final Demand PPI index, base Nov 2009=100 (WPUFD4)
CorePpi	Final demand less foods and energy — Core PPI, base Oct 2004=100
FinalDemandLessFoodEnergyTrade	Final demand less foods, energy, and trade services, base Aug 2013=100
FinalDemandGoods	Final demand goods, base Nov 2009=100
FinalDemandServices	Final demand services, base Nov 2009=100
FinalDemandConstruction	Final demand construction, base Nov 2009=100
FinalDemandGoodsLessFoods	Final demand goods less foods
AllCommodities	All commodities, base 1982=100
FarmProducts	Farm products, base 1982=100
ProcessedFoodsAndFeeds	Processed foods and feeds, base 1982=100
CrudePetroleum	Crude petroleum (domestic), base 1982=100

JOLTS fields — BLSEconomicSurveysJolts

All series non-seasonally-adjusted, total nonfarm.

Field	Description
JobOpenings	Job openings, level (thousands)
JobOpeningsRate	Job openings rate (percent)
Hires	Hires, level (thousands)
HiresRate	Hires rate (percent)
Quits	Quits, level (thousands)
QuitsRate	Quits rate (percent)
TotalSeparations	Total separations, level (thousands)
LayoffsAndDischarges	Layoffs and discharges, level (thousands)

The series are non-seasonally-adjusted (NSA)

Every series here is non-seasonally-adjusted (NSA). This is a property of the data to be aware of — not something to "fix" by default. Whether to seasonally adjust is the strategy spec's decision, not this skill's; use the NSA series as-is unless the spec calls for a seasonally-adjusted signal.

What the NSA property means for a signal:

• Year-over-year (12-month) change is seasonality-free on NSA — comparing the same calendar month across years cancels the seasonal pattern: yoy_t = level_t / level_{t-12} − 1.
• Month-over-month change on NSA carries a strong seasonal component (gasoline up every spring, post-holiday drops every January), mixing the seasonal calendar with the underlying trend.

Only if the spec calls for a seasonally-adjusted signal, build it point-in-time from the NSA series (no point-in-time SA series is available, so you must construct it using only past data): for the calendar month c of date t, subtract the expanding-window mean of that same calendar month's values observed strictly before t (excluding the current one). Never use a centered/two-sided filter or any data dated at or after t.