---
pretty_name: All Daily Turing Risk Index
language:
- en
license: other
task_categories:
- time-series-forecasting
- tabular-regression
tags:
- finance
- quantitative-trading
- backtesting
- algorithmic-trading
- macroeconomics
- daily
size_categories:
- 10K<n<100K
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: datetime
    dtype: string
  - name: turing_risk
    dtype: float64
  - name: market_risk
    dtype: float64
  - name: business_risk
    dtype: float64
  - name: political_risk
    dtype: float64
  - name: hs
    dtype: float64
  - name: ma
    dtype: float64
  - name: ewma
    dtype: float64
  - name: garch
    dtype: float64
  - name: ensemble
    dtype: float64
  - name: vix
    dtype: float64
  - name: systemic
    dtype: float64
  - name: turbulence
    dtype: float64
  - name: recession_6
    dtype: float64
  - name: recession_12
    dtype: float64
  - name: recession_24
    dtype: float64
  - name: cape
    dtype: float64
  - name: naiim_neg
    dtype: float64
  - name: aaii_neg
    dtype: float64
  - name: ads_business_neg
    dtype: float64
  - name: nonman_outlook_neg
    dtype: float64
  - name: man_phil_neg
    dtype: float64
  - name: man_tex_neg
    dtype: float64
  - name: man_ny_neg
    dtype: float64
  - name: cfnai_fneg
    dtype: float64
  - name: zew_sent_neg
    dtype: float64
  - name: atlanta_unc
    dtype: float64
  - name: building_index_neg
    dtype: float64
  - name: consumer_index_neg
    dtype: float64
  - name: industry_index_neg
    dtype: float64
  - name: main_index_neg
    dtype: float64
  - name: retail_index_neg
    dtype: float64
  - name: services_index_neg
    dtype: float64
  - name: mics_ics_neg
    dtype: float64
  - name: mics_icc_neg
    dtype: float64
  - name: mics_ice_neg
    dtype: float64
  - name: news_sent_neg
    dtype: float64
  - name: term_spread
    dtype: float64
  - name: credit_spread
    dtype: float64
  - name: corp_bond_distress
    dtype: float64
  - name: misery_index
    dtype: float64
  - name: housing_afford_neg
    dtype: float64
  - name: new_trucks
    dtype: float64
  - name: new_homes
    dtype: float64
  - name: cfsec_neg
    dtype: float64
  - name: us_policy_unc_d
    dtype: float64
  - name: uk_policy_unc_d
    dtype: float64
  - name: china_policy_unc_m
    dtype: float64
  - name: us_market_unc_d
    dtype: float64
  - name: us_policy_vol_m
    dtype: float64
  - name: global_policy_unc_m
    dtype: float64
  - name: us_sovereign_unc_m
    dtype: float64
  - name: geo_unc_d
    dtype: float64
  - name: geo_unc_m
    dtype: float64
  - name: geo_equal_m
    dtype: float64
  - name: web_search_unc_m
    dtype: float64
  - name: thinktank_unc_m
    dtype: float64
  splits:
  - name: train
    num_examples: 24204
---

# 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:

```bash
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.

```python
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](https://paperswithbacktest.com/pricing). The same subscription covers the other datasets in this organisation.

## Elsewhere

- [Dataset page and coverage charts](https://paperswithbacktest.com/datasets/all-daily-turingriskindex)
- [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.