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Octave Belot
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+ helpers for daily/intraday
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‎src/systemathics/apis/helpers/dataframe_helpers.py‎

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Original file line numberDiff line numberDiff line change
@@ -5,7 +5,10 @@
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functions:
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get_cds_index_daily - Get CDS Index daily data as a DataFrame using Ganymede gRPC API.
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get_cds_index_intraday - Get CDS Index intraday data as a DataFrame using Ganymede gRPC API.
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get_cds_daily - Get CDS daily data as a DataFrame using Ganymede gRPC API.
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get_cds_intraday - Get CDS intraday data as a DataFrame using Ganymede gRPC API.
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get_future_daily - Get future daily data as a DataFrame using Ganymede gRPC API.
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get_equity_daily - Get equity daily data as a DataFrame using Ganymede gRPC API.
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"""
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@@ -192,7 +195,174 @@ def get_cds_index_daily(ticker, start_date=None, end_date=None, batch=None, sele
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except Exception as e:
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print(f"Error: {str(e)}")
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return pd.DataFrame()
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def get_cds_daily(ticker, start_date=None, end_date=None, batch=None, selected_fields=None, provider="Markit"):
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"""
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Fetch CDS daily data from gRPC API for a given ticker and date range.
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Parameters:
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ticker (str): The ticker symbol
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start_date (datetime.date or str, optional): Start date for data retrieval.
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If None, set not limits
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end_date (datetime.date or str, optional): End date for data retrieval.
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If None, uses today's date
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batch (str, optional): Batch name to be used for filtering. If None, gets all batches.
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selected_fields (list, optional): List of specific fields to retrieve. If None, gets all fields.
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provider (str): Data provider, default is "Markit"
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Returns:
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pd.DataFrame: DataFrame with Date as index and all available fields as columns
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"""
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# All available fields for individual CDS
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all_fields = [
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"ConventionalSpread",
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"ParSpread",
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"Upfront",
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"RealRecovery",
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"AssumedRecovery",
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"DefaultProbability",
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"JumpToDefault",
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"JumpToZero"
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]
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# Use all fields if none specified, otherwise validate selected fields
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if selected_fields is None:
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fields = all_fields
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else:
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fields = [f for f in selected_fields if f in all_fields]
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if not fields:
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raise ValueError("No valid fields selected")
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# Create identifier for individual CDS (not index)
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id = identifier.Identifier(
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asset_type=asset.AssetType.ASSET_TYPE_CDS,
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ticker=ticker
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)
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id.provider.value = provider
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# Build constraints only if we have at least one date
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constraints_obj = None
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if start_date is not None or end_date is not None:
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# Create DateInterval with only the dates that are provided
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date_interval_kwargs = {}
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if start_date is not None:
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date_interval_kwargs['start_date'] = _parse_date_input(start_date)
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if end_date is not None:
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date_interval_kwargs['end_date'] = _parse_date_input(end_date)
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constraints_obj = constraints.Constraints(
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date_intervals=[date_interval.DateInterval(**date_interval_kwargs)]
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)
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if batch is None:
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# Create request with or without constraints
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request_kwargs = {
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'identifier': id,
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'fields': fields
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}
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if constraints_obj is not None:
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request_kwargs['constraints'] = constraints_obj
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try:
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# Open gRPC channel
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with channel_helpers.get_grpc_channel() as channel:
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# Send request and receive response
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token = token_helpers.get_token()
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first = True
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response = []
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info = None
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# Create service stub
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service = get_daily_service.DailyServiceStub(channel)
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if batch is None:
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# Create request with or without constraints
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request_kwargs = {
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'identifier': id,
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'fields': fields
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}
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if constraints_obj is not None:
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request_kwargs['constraints'] = constraints_obj
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vector_request = get_daily.DailyRequest(**request_kwargs)
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for data in service.DailyVectorStream(
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request=vector_request,
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metadata=[('authorization', token)]
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):
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if first:
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info = data
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first = False
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else:
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response.append(data.data)
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else:
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request_kwargs = {
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'identifier': id,
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'fields': fields,
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'key': batch
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}
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if constraints_obj is not None:
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request_kwargs['constraints'] = constraints_obj
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vector_key_request = get_daily.DailyVectorKeyRequest(**request_kwargs)
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for data in service.DailyVectorKeyStream(
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request=vector_key_request,
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metadata=[('authorization', token)]
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):
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if first:
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info = data
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first = False
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else:
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response.append(data.data)
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# Process the response
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if not response or info is None:
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print("No data received")
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return pd.DataFrame()
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# Get field indices
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available_fields = [f for f in info.info.fields]
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field_indices = {field: available_fields.index(field)
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for field in fields if field in available_fields}
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# Extract dates
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dates = [date(d.date.year, d.date.month, d.date.day) for d in response]
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# Extract keys
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keys = [b.key for b in response]
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# Create dictionary for DataFrame
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data_dict = {'Key': keys}
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# Extract data for each field
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for field_name, field_index in field_indices.items():
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data_dict[field_name] = [b.data[field_index] for b in response]
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# Create DataFrame
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df = pd.DataFrame(data_dict, index=dates)
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df.index.name = 'Date'
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# Sort by date for better readability
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df = df.sort_index()
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return df
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except grpc.RpcError as e:
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print(f"gRPC Error: {e.code().name}")
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print(f"Details: {e.details()}")
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return pd.DataFrame()
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except Exception as e:
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print(f"Error: {str(e)}")
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return pd.DataFrame()
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def get_cds_index_intraday(ticker, start_date=None, end_date=None, sampling=sampling.SAMPLING_ONE_MINUTE, selected_fields=None, provider="Markit"):
197367
"""
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Fetch CDS Index intraday data from gRPC API for a given ticker and date range.
@@ -313,6 +483,130 @@ def get_cds_index_intraday(ticker, start_date=None, end_date=None, sampling=samp
313483
print(f"Error: {str(e)}")
314484
return pd.DataFrame()
315485

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def get_cds_intraday(ticker, start_date=None, end_date=None, sampling=sampling.SAMPLING_ONE_MINUTE, selected_fields=None, provider="Markit"):
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"""
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Fetch CDS intraday data from gRPC API for a given ticker and date range.
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Parameters:
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ticker (str): The ticker symbol
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start_date (datetime.date or str, optional): Start date for data retrieval.
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If None, set not limits
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end_date (datetime.date or str, optional): End date for data retrieval.
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If None, set not limits
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sampling (sampling, optional): Sampling perdiod for intrday. Default to one minute.
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selected_fields (list, optional): List of specific fields to retrieve.
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If None, gets all fields.
499+
provider (str): Data provider, default is "Markit"
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Returns:
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pd.DataFrame: DataFrame with Date as index and all available fields as columns
502+
"""
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# All available fields for individual CDS
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all_fields = [
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'BidConventionalSpread',
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'BidParSpread',
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'BidUpfront',
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'MidConventionalSpread',
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'MidParSpread',
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'MidUpfront',
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'AskConventionalSpread',
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'AskParSpread',
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'AskUpfront'
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]
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# Use all fields if none specified, otherwise validate selected fields
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if selected_fields is None:
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fields = all_fields
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else:
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fields = [f for f in selected_fields if f in all_fields]
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if not fields:
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raise ValueError("No valid fields selected")
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# Create identifier for individual CDS (not index)
526+
id = identifier.Identifier(
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asset_type=asset.AssetType.ASSET_TYPE_CDS,
528+
ticker=ticker
529+
)
530+
id.provider.value = provider
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532+
# Build constraints only if we have at least one date
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constraints_obj = None
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if start_date is not None or end_date is not None:
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# Create DateInterval with only the dates that are provided
536+
date_interval_kwargs = {}
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if start_date is not None:
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date_interval_kwargs['start_date'] = _parse_date_input(start_date)
539+
if end_date is not None:
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date_interval_kwargs['end_date'] = _parse_date_input(end_date)
541+
constraints_obj = constraints.Constraints(
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date_intervals=[date_interval.DateInterval(**date_interval_kwargs)]
543+
)
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# Create request with or without constraints
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request_kwargs = {
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'identifier': id,
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'fields': fields,
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'sampling': sampling
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}
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if constraints_obj is not None:
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request_kwargs['constraints'] = constraints_obj
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try:
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# Open gRPC channel
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with channel_helpers.get_grpc_channel() as channel:
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# Send request and receive response
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token = token_helpers.get_token()
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first = True
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response = []
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info = None
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# Create service stub
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service = get_intraday_service.IntradayServiceStub(channel)
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scalar_request = get_intraday.IntradayRequest(**request_kwargs)
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566+
for data in service.IntradayScalarStream(request=scalar_request, metadata=[('authorization', token)]):
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if first:
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info = data
569+
first = False
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else:
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response.append(data.data)
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# Process the response
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if not response or info is None:
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print("No data received")
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return pd.DataFrame()
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# Get field indices
579+
available_fields = [f for f in info.info.fields]
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field_indices = {field: available_fields.index(field)
581+
for field in fields if field in available_fields}
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583+
# Extract dates
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dates = [datetime(d.datetime.year, d.datetime.month, d.datetime.day, d.datetime.hours, d.datetime.minutes, d.datetime.seconds) for d in response]
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# Create dictionary for DataFrame
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data_dict = {}
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# Extract data for each field
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for field_name, field_index in field_indices.items():
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data_dict[field_name] = [b.data[field_index] for b in response]
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# Create DataFrame
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df = pd.DataFrame(data_dict, index=dates)
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df.index.name = 'Datetime'
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# Sort by date for better readability
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df = df.sort_index()
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return df
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except grpc.RpcError as e:
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print(f"gRPC Error: {e.code().name}")
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print(f"Details: {e.details()}")
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return pd.DataFrame()
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except Exception as e:
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print(f"Error: {str(e)}")
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return pd.DataFrame()
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316610
def get_future_daily(ticker, start_date=None, end_date=None, provider="FirstRateData"):
317611
"""
318612
Fetch Future daily data from gRPC API for a given ticker and optionally filter by date range.

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