numerapi.signalsapi module

API for Numerai Signals

class numerapi.signalsapi.SignalsAPI(*args, **kwargs)

Bases: Api

“API for Numerai Signals

daily_model_performances(username: str) → List[Dict]

Fetch daily Numerai Signals performance of a model.

Parameters:

username (str) –

Returns:

list of daily user performance entries

For each entry in the list, there is a dict with the following content:

• date (datetime)

• corrRank (int)

• corrRep (float or None)

• mmcRank (int)

• mmcRep (float or None)

• icRank (int)

• icRep (float or None)

• tcRank (int)

• tcRep (float or None)

• corr20dRank (int)

• corr20dRep (float or None)

• corr60Rank (int)

• corr60Rep (float or None)

• mmc20dRank (int)

• mmc20dRep (float or None)

Return type:

list of dicts

Example

>>> api = SignalsAPI()
>>> api.daily_model_performances("floury_kerril_moodle")
[{'corrRank': 45,
  'corrRep': -0.00010935616731632354,
  'corr20dRank': None,
  'corr20dRep': None,
  'mmc20dRank': None,
  'mmc20dRep': None,
  'date': datetime.datetime(2020, 9, 18, 0, 0, tzinfo=tzutc()),
  'mmcRank': 6,
  'mmcRep': 0.0,
  'icRank': 6,
  'icRep': 0.0,
  ...},
  ...
  ]

download_validation_data() → None

download CSV file with historical targets and ticker universe

get_leaderboard(limit: int = 50, offset: int = 0) → List[Dict]

Get the current Numerai Signals leaderboard :param limit: number of items to return (optional, defaults to 50) :type limit: int :param offset: number of items to skip (optional, defaults to 0) :type offset: int

Returns:

list of leaderboard entries Each dict contains the following items:

• username (str)

• sharpe (float)

• rank (int)

• prevRank (int)

• today (float)

• mmc (float)

• mmcRank (int)

• icRep (float)

• icRank (int)

• tcRep (float)

• tcRank (int)

• nmrStaked (float)

Return type:

list of dicts

Example

>>> numerapi.SignalsAPI().get_leaderboard(1)
[{'prevRank': 1,
  'rank': 1,
  'sharpe': 2.3,
  'today': 0.01321,
  'username': 'floury_kerril_moodle',
  'mmc': -0.0101202715,
  'mmcRank': 30,
  'nmrStaked': 13.0,
  'icRep': -0.0101202715,
  'icRank': 30,
  ..
 }]

public_user_profile(username: str) → Dict

Fetch the public Numerai Signals profile of a user.

Parameters:

username (str) –

Returns:

user profile including the following fields:

• username (str)

• startDate (datetime)

• id (string)

• bio (str)

• nmrStaked (decimal.Decimal)

Return type:

dict

Example

>>> api = SignalsAPI()
>>> api.public_user_profile("floury_kerril_moodle")
{'bio': None,
 'id': '635db2a4-bdc6-4e5d-b515-f5120392c8c9',
 'startDate': datetime.datetime(2019, 3, 26, 0, 43),
 'username': 'floury_kerril_moodle',
 'nmrStaked': Decimal('14.630994874320760131')}

stake_get(username) → Decimal

get current stake for a given users

Parameters:

username (str) –

Returns:

current stake

Return type:

decimal.Decimal

Example

>>> SignalsAPI().stake_get("uuazed")
Decimal('14.63')

submission_status(model_id: str | None = None) → None

submission status of the last submission associated with the account

DEPRECATED numerai no longer provides this data. This will be removed in one of the next versions

Parameters:

model_id (str) –

Example

>>> api = SignalsAPI(secret_key="..", public_id="..")
>>> model_id = api.get_models()['uuazed']
>>> api.submission_status(model_id)

ticker_universe() → List[str]

fetch universe of accepted tickers

Returns:

list of currently accepted tickers

Return type:

list of strings

Example

>>> SignalsAPI().ticker_universe()
["MSFT", "AMZN", "APPL", ...]

upload_predictions(file_path: str = 'predictions.csv', model_id: str | None = None, df: DataFrame | None = None, timeout: None | float | Tuple[float, float] = (10, 60)) → str

Upload predictions from file. Will read TRIGGER_ID from the environment if this model is enabled with a Numerai Compute cluster setup by Numerai CLI.

Parameters:

• file_path (str) – CSV file with predictions that will get uploaded

• model_id (str) – Target model UUID (required for accounts with multiple models)

• df (pandas.DataFrame) – Pandas DataFrame to upload, if function is given df and file_path, df will be uploaded

• timeout (float|tuple(float,float)) – waiting time (connection timeout, read timeout)

Returns:

submission_id

Return type:

str

Example

>>> api = SignalsAPI(secret_key="..", public_id="..")
>>> model_id = api.get_models()['uuazed']
>>> api.upload_predictions("prediction.cvs", model_id=model_id)
'93c46857-fed9-4594-981e-82db2b358daf'

>>> # upload directly from a pandas DataFrame:
>>> api.upload_predictions(df = predictions_df, model_id=model_id)