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API Reference
Radar
CT

Get certificates time series

radar.ct.timeseries(CTTimeseriesParams**kwargs) -> CTTimeseriesResponse
GET/radar/ct/timeseries

Retrieves certificate volume over time.

Security
API Token

The preferred authorization scheme for interacting with the Cloudflare API. Create a token.

Example:Authorization: Bearer Sn3lZJTBX6kkg7OdcBUAxOO963GEIyGQqnFTOFYY
API Email + API Key

The previous authorization scheme for interacting with the Cloudflare API, used in conjunction with a Global API key.

Example:X-Auth-Email: user@example.com

The previous authorization scheme for interacting with the Cloudflare API. When possible, use API tokens instead of Global API keys.

Example:X-Auth-Key: 144c9defac04969c7bfad8efaa8ea194
Accepted Permissions (at least one required)
User Details WriteUser Details Read
ParametersExpand Collapse
agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]

Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to Aggregation intervals.

One of the following:
"15m"
"1h"
"1d"
"1w"
ca: Optional[SequenceNotStr[str]]

Filters results by certificate authority.

ca_owner: Optional[SequenceNotStr[str]]

Filters results by certificate authority owner.

date_end: Optional[SequenceNotStr[Union[str, datetime]]]

End of the date range (inclusive).

date_range: Optional[SequenceNotStr[str]]

Filters results by date range. For example, use 7d and 7dcontrol to compare this week with the previous week. Use this parameter or set specific start and end dates (dateStart and dateEnd parameters).

date_start: Optional[SequenceNotStr[Union[str, datetime]]]

Start of the date range.

duration: Optional[List[Literal["LTE_3D", "GT_3D_LTE_7D", "GT_7D_LTE_10D", 4 more]]]

Filters results by certificate duration.

One of the following:
"LTE_3D"
"GT_3D_LTE_7D"
"GT_7D_LTE_10D"
"GT_10D_LTE_47D"
"GT_47D_LTE_100D"
"GT_100D_LTE_200D"
"GT_200D"
entry_type: Optional[List[Literal["PRECERTIFICATE", "CERTIFICATE"]]]

Filters results by entry type (certificate vs. pre-certificate).

One of the following:
"PRECERTIFICATE"
"CERTIFICATE"
expiration_status: Optional[List[Literal["EXPIRED", "VALID"]]]

Filters results by expiration status (expired vs. valid).

One of the following:
"EXPIRED"
"VALID"
format: Optional[Literal["JSON", "CSV"]]

Format in which results will be returned.

One of the following:
"JSON"
"CSV"
has_ips: Optional[Iterable[bool]]

Filters results based on whether the certificates are bound to specific IP addresses.

has_wildcards: Optional[Iterable[bool]]

Filters results based on whether the certificates contain wildcard domains.

log: Optional[SequenceNotStr[str]]

Filters results by certificate log.

log_api: Optional[List[Literal["RFC6962", "STATIC"]]]

Filters results by certificate log API (RFC6962 vs. static).

One of the following:
"RFC6962"
"STATIC"
log_operator: Optional[SequenceNotStr[str]]

Filters results by certificate log operator.

name: Optional[SequenceNotStr[str]]

Array of names used to label the series in the response.

public_key_algorithm: Optional[List[Literal["DSA", "ECDSA", "RSA"]]]

Filters results by public key algorithm.

One of the following:
"DSA"
"ECDSA"
"RSA"
signature_algorithm: Optional[List[Literal["DSA_SHA_1", "DSA_SHA_256", "ECDSA_SHA_1", 12 more]]]

Filters results by signature algorithm.

One of the following:
"DSA_SHA_1"
"DSA_SHA_256"
"ECDSA_SHA_1"
"ECDSA_SHA_256"
"ECDSA_SHA_384"
"ECDSA_SHA_512"
"PSS_SHA_256"
"PSS_SHA_384"
"PSS_SHA_512"
"RSA_MD2"
"RSA_MD5"
"RSA_SHA_1"
"RSA_SHA_256"
"RSA_SHA_384"
"RSA_SHA_512"
tld: Optional[SequenceNotStr[str]]

Filters results by top-level domain.

unique_entries: Optional[List[Literal["true", "false"]]]

Specifies whether to filter out duplicate certificates and pre-certificates. Set to true for unique entries only.

One of the following:
"true"
"false"
validation_level: Optional[List[Literal["DOMAIN", "ORGANIZATION", "EXTENDED"]]]

Filters results by validation level.

One of the following:
"DOMAIN"
"ORGANIZATION"
"EXTENDED"
ReturnsExpand Collapse
class CTTimeseriesResponse: …
meta: Meta

Metadata for the results.

agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]

Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to Aggregation intervals.

One of the following:
"FIFTEEN_MINUTES"
"ONE_HOUR"
"ONE_DAY"
"ONE_WEEK"
"ONE_MONTH"
confidence_info: MetaConfidenceInfo
annotations: List[MetaConfidenceInfoAnnotation]
data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]

Data source for annotations.

One of the following:
"ALL"
"AI_BOTS"
"AI_GATEWAY"
"BGP"
"BOTS"
"CONNECTION_ANOMALY"
"CT"
"DNS"
"DNS_MAGNITUDE"
"DNS_AS112"
"DOS"
"EMAIL_ROUTING"
"EMAIL_SECURITY"
"FW"
"FW_PG"
"HTTP"
"HTTP_CONTROL"
"HTTP_CRAWLER_REFERER"
"HTTP_ORIGINS"
"IQI"
"LEAKED_CREDENTIALS"
"NET"
"ROBOTS_TXT"
"SPEED"
"WORKERS_AI"
description: str
end_date: datetime
formatdate-time
event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]

Event type for annotations.

One of the following:
"EVENT"
"GENERAL"
"OUTAGE"
"PARTIAL_PROJECTION"
"PIPELINE"
"TRAFFIC_ANOMALY"
is_instantaneous: bool

Whether event is a single point in time or a time range.

linked_url: str
formaturi
start_date: datetime
formatdate-time
level: int

Provides an indication of how much confidence Cloudflare has in the data.

date_range: List[MetaDateRange]
end_time: datetime

Adjusted end of date range.

formatdate-time
start_time: datetime

Adjusted start of date range.

formatdate-time
last_updated: datetime

Timestamp of the last dataset update.

formatdate-time
normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]

Normalization method applied to the results. Refer to Normalization methods.

One of the following:
"PERCENTAGE"
"MIN0_MAX"
"MIN_MAX"
"RAW_VALUES"
"PERCENTAGE_CHANGE"
"ROLLING_AVERAGE"
"OVERLAPPED_PERCENTAGE"
"RATIO"
units: List[MetaUnit]

Measurement units for the results.

name: str
value: str

Get certificates time series

import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ct.timeseries()
print(response.meta)
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z"
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    }
  },
  "success": true
}
Returns Examples
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z"
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    }
  },
  "success": true
}