Crypto Fear & Greed signals (crypto sentiment regimes)
Daily crypto-sentiment signals from the alternative.me Crypto Fear and Greed Index (2018-02 ->, 0–100): 7-day and 30-day momentum, 30-day change volatility, 3-sigma anomaly flags, naive-drift forecasts, extreme-fear (<=25) and extreme-greed (>=75) regime flags, a 90-day sentiment z-score, and the provider's own classification. The crypto-sentiment companion to fng-signals (CNN stock-market Fear & Greed) — crypto runs its own sentiment cycle, often leading equities. Keyless JSON API; commercial redistribution not explicitly granted (marked unclear). Global market, rows labeled WLD. Raw series: alternative.me (tobieskrambs GmbH).
Quality
Attribution
Crypto Fear and Greed Index by alternative.me (tobieskrambs GmbH); signals by Frontier Data Hub
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Observation date derived from the API's timestamp field (Unix seconds, UTC; YYYY-MM-DD, daily). |
| country | string | |
| country_code | string | |
| series_id | string | CFG: the Crypto Fear and Greed Index from alternative.me. |
| series_label | string | Crypto Fear and Greed Index (alternative.me): aggregates volatility, market momentum and volume, social media, surveys, Bitcoin dominance, and Google Trends into one gauge; 0 = extreme fear, 100 = extreme greed. |
| value | float | Index value on the 0–100 scale from the API's value field: 0 = extreme fear, 100 = extreme greed. Extreme fear has historically signalled buying opportunities for contrarians; extreme greed warns the market may be overheating. |
| sentiment_score | float | |
| sentiment_label | string | The API's value_classification field: one of Extreme Fear, Fear, Neutral, Greed, Extreme Greed. |
| momentum_3m | float | |
| momentum_7d | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| extreme_fear_flag | integer | |
| extreme_greed_flag | integer | |
| sentiment_z_90d | float |
Sample rows
| date | country | country_code | series_id | series_label | value | sentiment_score | sentiment_label | momentum_3m | momentum_7d | volatility_30d | anomaly_flag | forecast_1m | extreme_fear_flag | extreme_greed_flag | sentiment_z_90d |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2018-02-01 | World | WLD | CFG | Crypto Fear and Greed Index (alternative.me, 0-100) | 30 | 0.3 | Fear | — | — | — | 0 | — | 0 | 0 | — |
| 2018-02-02 | World | WLD | CFG | Crypto Fear and Greed Index (alternative.me, 0-100) | 15 | 0.15 | Extreme Fear | — | — | — | 0 | — | 1 | 0 | — |
| 2018-02-03 | World | WLD | CFG | Crypto Fear and Greed Index (alternative.me, 0-100) | 40 | 0.4 | Fear | — | — | — | 0 | — | 0 | 0 | — |
| 2018-02-04 | World | WLD | CFG | Crypto Fear and Greed Index (alternative.me, 0-100) | 24 | 0.24 | Extreme Fear | — | — | — | 0 | — | 1 | 0 | — |
| 2018-02-05 | World | WLD | CFG | Crypto Fear and Greed Index (alternative.me, 0-100) | 11 | 0.11 | Extreme Fear | — | — | — | 0 | — | 1 | 0 | — |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
Use with an LLM
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/crypto_fear_greed_signals/crypto_fear_greed_index_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/crypto_fear_greed_signals/crypto_fear_greed_index_signals").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/crypto_fear_greed_signals/crypto_fear_greed_index_signals
Tip: fetch /llms.txt for the full machine-readable catalog.