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

All Daily Turing Risk Index

Daily history of the Turing Risk Index and its market, business, and political risk components.

24,204 rows, 57 columns, covering 1960-01-01 to 2026-04-07. The most recent observation is 2026-04-07.

Why It Matters

This dataset is essential for systematic trading, risk management, and quantitative research focused on market uncertainty:

Macro Overlay: The aggregate index and sub-components (market, business, political risk) provide a consolidated view of systemic uncertainty for market timing and asset allocation.
Recession Modeling: Utilize the multi-horizon recession probabilities (recession_6, recession_12, recession_24) as leading indicators in economic and investment models.
Stress Testing: Combine with portfolio data to stress-test holdings against sudden surges in volatility, credit distress, or geopolitical uncertainty.

Load It

Installation/Upgrade: Ensure you have the latest version of the toolkit:

pip install pwb-toolbox

Access Token Setup: Before running the code, ensure your Hugging Face Access Token is set as an environment variable (HF_ACCESS_TOKEN) or defined in your session, and you are subscribed to the Backtester plan on Papers With Backtest.

from pwb_toolbox import datasets as pwb_ds

df = pwb_ds.load_dataset("All-Daily-TuringRiskIndex")
print(df.iloc[0,:])

Example Output:

datetime               1960-01-01
turing_risk             34.737167
market_risk              37.58584
business_risk           37.796684
political_risk          18.032432
hs                      47.265806
ma                      45.548835
ewma                    45.629371
garch                   13.879916
ensemble                43.940312
vix                     20.046106
systemic                 23.89812
turbulence              10.153223
recession_6             28.024086
recession_12            57.873915
recession_24            63.821508
cape                    50.948886
naiim_neg               36.501792
aaii_neg                48.418238
ads_business_neg        30.910628
nonman_outlook_neg      27.353725
man_phil_neg            42.388548
man_tex_neg             38.688323
man_ny_neg              35.776361
cfnai_fneg              52.835876
zew_sent_neg            41.137134
atlanta_unc             27.961265
building_index_neg      39.481653
consumer_index_neg      43.601625
industry_index_neg      32.466125
main_index_neg          30.484709
retail_index_neg        27.853436
services_index_neg      31.143671
mics_ics_neg             35.46754
mics_icc_neg            45.701794
mics_ice_neg            30.523532
news_sent_neg           41.411862
term_spread             57.643777
credit_spread           21.581057
corp_bond_distress      26.381857
misery_index            28.958701
housing_afford_neg      48.310476
new_trucks              51.962154
new_homes               31.686657
cfsec_neg               36.166639
us_policy_unc_d         19.269711
uk_policy_unc_d         19.455555
china_policy_unc_m      42.449101
us_market_unc_d         12.186028
us_policy_vol_m         23.570878
global_policy_unc_m     27.334687
us_sovereign_unc_m      16.975858
geo_unc_d               18.319971
geo_unc_m               19.829667
geo_equal_m             21.486922
web_search_unc_m        29.997329
thinktank_unc_m         25.182874

Columns

This dataset includes data with the following fields:

Column Name	Description
datetime	Observation date (UTC).
turing_risk	Headline Turing Risk Index (Aggregate Risk Score).
market_risk	Component sub-index for Market Uncertainty.
business_risk	Component sub-index for Business Cycle Uncertainty.
political_risk	Component sub-index for Political/Geopolitical Uncertainty.
hs, ma, ewma, garch, ensemble	Volatility and risk model outputs used in the composite index.
vix, systemic, turbulence	Market stress and systemic risk gauges.
recession_6, recession_12, recession_24	Recession probabilities at 6-month, 12-month, and 24-month horizons.
cape	Shiller Cyclically Adjusted P/E (CAPE) valuation measure.
naiim_neg, aaii_neg, ads_business_neg, nonman_outlook_neg, man_phil_neg, man_tex_neg, man_ny_neg, cfnai_fneg, zew_sent_neg, atlanta_unc	Survey and nowcast sentiment: investment manager and retail investor surveys, the ADS business conditions index, the regional Fed manufacturing and non-manufacturing outlooks, the Chicago Fed national activity index, ZEW sentiment and the Atlanta Fed uncertainty measure.
building_index_neg, consumer_index_neg, industry_index_neg, main_index_neg, retail_index_neg, services_index_neg	Business activity diffusion indexes by sector.
mics_ics_neg, mics_icc_neg, mics_ice_neg	University of Michigan consumer sentiment variants: the headline index, current conditions and expectations.
news_sent_neg	News sentiment component.
term_spread, credit_spread, corp_bond_distress, misery_index, housing_afford_neg, new_trucks, new_homes, cfsec_neg	Macro and credit conditions: the yield curve, credit spreads, corporate bond distress, the misery index, housing affordability, truck and home sales and financial conditions.
us_policy_unc_d, uk_policy_unc_d, china_policy_unc_m, us_market_unc_d, us_policy_vol_m, global_policy_unc_m, us_sovereign_unc_m, geo_unc_d, geo_unc_m, geo_equal_m, web_search_unc_m, thinktank_unc_m	Policy and geopolitical uncertainty across regions. The `\_d` suffix marks a daily series and `\_m` a monthly one.

Columns ending in \_neg are sign-flipped so that a higher value always means more risk, which is what lets the composite add them together.

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 above
awesome-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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