Data Collection¶

EBA data collector for financial network analysis.

This module provides the EBACollector class for retrieving data from the European Banking Authority (EBA) transparency exercises and stress tests.

class scr_financial.data.collectors.eba_collector.EBACollector[source]¶

Bases: object

Collector for European Banking Authority (EBA) data.

This class provides methods for retrieving data from EBA transparency exercises and stress tests, which include key bank metrics like CET1 ratio, leverage ratio, and liquidity coverage ratio.

__init__()[source]¶

Initialize the EBA data collector.

Return type:: None

__repr__()[source]¶

Return a string representation of this collector.

Returns:: Class name and base URL.
Return type:: str

collect_transparency_data(start_date, end_date, bank_list=None)[source]¶

Collect data from EBA transparency exercises.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
bank_list (List[str] | None) – List of bank identifiers to include.

Returns:

DataFrame containing bank solvency metrics.

Return type:

DataFrame

collect_aggregated_data(start_date, end_date, bank_list=None)[source]¶

Collect aggregated data from EBA.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
bank_list (List[str] | None) – List of bank identifiers to include.

Returns:

DataFrame containing bank liquidity metrics.

Return type:

DataFrame

ECB data collector for financial network analysis.

This module provides the ECBCollector class for retrieving data from the European Central Bank (ECB) Statistical Data Warehouse and other ECB sources.

class scr_financial.data.collectors.ecb_collector.ECBCollector[source]¶

Bases: object

Collector for European Central Bank (ECB) data.

This class provides methods for retrieving data from ECB sources, including TARGET2 interbank payment data, CISS systemic stress indicator, and G-SIB data.

__init__()[source]¶

Initialize the ECB data collector.

Return type:: None

__repr__()[source]¶

Return a string representation of this collector.

Returns:: Class name and base URL.
Return type:: str

collect_target2_data(start_date, end_date, bank_list=None)[source]¶

Collect TARGET2 interbank payment data.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
bank_list (List[str] | None) – List of bank identifiers to include.

Returns:

DataFrame containing interbank payment volumes.

Return type:

DataFrame

collect_ciss_data(start_date, end_date)[source]¶

Collect ECB Composite Indicator of Systemic Stress (CISS) data.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.

Returns:

DataFrame containing CISS values.

Return type:

DataFrame

collect_gsib_data(start_date, end_date, bank_list=None)[source]¶

Collect G-SIB (Global Systemically Important Bank) indicator data.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
bank_list (List[str] | None) – List of bank identifiers to include.

Returns:

DataFrame containing G-SIB indicators.

Return type:

DataFrame

Market data collector for financial network analysis.

This module provides the MarketDataCollector class for retrieving market data such as CDS spreads, equity prices, and other market indicators.

class scr_financial.data.collectors.market_collector.MarketDataCollector[source]¶

Bases: object

Collector for market data.

This class provides methods for retrieving market data such as CDS spreads, equity prices, SRISK, and other market indicators from various sources.

__init__()[source]¶

Initialize the market data collector.

Return type:: None

__repr__()[source]¶

Return a string representation of this collector.

Returns:: Class name and FRED base URL.
Return type:: str

collect_cds_data(start_date, end_date, bank_list=None)[source]¶

Collect CDS spread data.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
bank_list (List[str] | None) – List of bank identifiers to include.

Returns:

DataFrame containing CDS spreads.

Return type:

DataFrame

collect_srisk_data(start_date, end_date, bank_list=None)[source]¶

Collect SRISK data from NYU V-Lab.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
bank_list (List[str] | None) – List of bank identifiers to include.

Returns:

DataFrame containing SRISK values.

Return type:

DataFrame

collect_equity_data(start_date, end_date, bank_list=None)[source]¶

Collect equity price data.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
bank_list (List[str] | None) – List of bank identifiers to include.

Returns:

DataFrame containing equity prices.

Return type:

DataFrame

collect_funding_stress_data(start_date, end_date)[source]¶

Collect funding stress indicators.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.

Returns:

DataFrame containing funding stress indicators.

Return type:

DataFrame

collect_bis_data(start_date, end_date, indicator)[source]¶

Collect data from Bank for International Settlements (BIS).

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
indicator (str) – Indicator to collect (e.g., ‘credit_to_GDP_gap’).

Returns:

DataFrame containing BIS data.

Return type:

DataFrame

Data preprocessor for financial network analysis.

This module provides the DataPreprocessor class for loading, cleaning, and normalizing financial data from various sources.

class scr_financial.data.preprocessor.DataPreprocessor(start_date, end_date, bank_list=None)[source]¶

Bases: object

Handles loading, cleaning, and normalizing financial data from various sources.

Parameters:

start_date (str) – Start date for data collection in ‘YYYY-MM-DD’ format.
end_date (str) – End date for data collection in ‘YYYY-MM-DD’ format.
bank_list (List[str] | None) – List of bank identifiers to include in the analysis.

node_data¶: Dictionary containing bank-specific attributes.

edge_data¶: Dictionary containing interbank network data.

system_data¶: Dictionary containing system-wide indicators.

Raises:

ValueError – If start_date or end_date cannot be parsed as dates, or if end_date is earlier than start_date.

Parameters:

start_date (str)
end_date (str)
bank_list (List[str] | None)

__init__(start_date, end_date, bank_list=None)[source]¶

Initialize the data preprocessor with date range and optional bank list.

Parameters:

start_date (str)
end_date (str)
bank_list (List[str] | None)

Return type:

None

load_bank_node_data(data_sources)[source]¶

Load bank-specific attributes from specified sources.

Parameters:: data_sources (Dict[str, str]) – Dictionary mapping attribute categories to data sources.
Returns:: Dictionary of loaded bank attributes.
Return type:: Dict[str, DataFrame]

load_interbank_exposures(source='ECB_TARGET2')[source]¶

Load interbank network data from specified source.

Parameters:: source (str) – Source for interbank exposure data, by default ‘ECB_TARGET2’.
Returns:: Dictionary of interbank exposure data.
Return type:: Dict[str, DataFrame]

load_system_indicators()[source]¶

Load system-wide financial indicators.

Returns:: Dictionary of system-wide indicators.
Return type:: Dict[str, DataFrame]

normalize_edge_weights(method='degree')[source]¶

Apply normalization to edge weights.

Parameters:: method (str) – Normalization method, by default ‘degree’.
Returns:: Dictionary of normalized edge weights.
Return type:: Dict[str, DataFrame]

filter_network(method='threshold', threshold=0.05)[source]¶

Apply filtering techniques to reduce noise in the network.

Parameters:

method (str) – Filtering method, by default ‘threshold’.
threshold (float) – Threshold value for filtering, by default 0.05.

Returns:

Dictionary of filtered edge data.

Return type:

Dict[str, DataFrame]

align_timescales()[source]¶

Align data with different frequencies to a common timeline.

Return type:: None

get_data_for_timepoint(time_point)[source]¶

Get all data for a specific time point.

Parameters:: time_point (str | datetime) – Time point for which to retrieve data.
Returns:: Dictionary containing node_data, edge_data, and system_data for the specified time point.
Return type:: Dict[str, Dict]