Generate carry and rolldown risk report based on the sensitivity of trades to bucketed shocks of the instantaneous (ON) forward rates. Here shock_i is applied to the ON forward rates over [t_i, t_{i+1}] where {t_i} is a time grid from base_curve.value_date to grid_last_date with buckets of size grid_bucket_tenor Args: base_curve (DiscountCurve): base curve to apply bumps to grid_last_date: the last date for the grid that defines shocks grid_bucket_tenor (str): spacing of grid points as tenor string e.g'3M' trades (list): a list of trades to calculate deltas of trade_labels (list, optional): trade labels to identify trades in the output. Defaults to None in which case these are auto generted bump_size (float, optional): How big of a bump to apply to bechmarks. Output always expressed as change in value per 1 bp. Defaults to 1.0*G_BASIS_POINT. Returns: (dict, Dataframe): (base_values, risk_report) base_value is a dictionary with trade_labels as keys and base trade values as values risk_report is a dataframe with time buickets for rows and carry/rolldown and DV01 columns per trade, and a total for all trades for each measure. Columns marked 'ROLL' have carry in the first row and rolldown in all the others

Generate forward rate deltas (forward delta ladder) risk report, which is the sensitivity of trades to bucketed shocks of the instantaneous (ON) forward rates. Here shock_i is applied to the ON forward rates over [t_i, t_{i+1}] where {t_i} is a time grid from base_curve.value_date to grid_last_date with buckets of size grid_bucket_tenor Args: base_curve (DiscountCurve): base curve to apply bumps to grid_last_date: the last date for the grid that defines shocks grid_bucket_tenor (str): spacing of grid points as tenor string eg '3M' trades (list): a list of trades to calculate deltas of trade_labels (list, optional): trade labels to identify trades in output. Defaults to None in which case these are auto generted bump_size (float, optional): How big of a bump to apply to bechmarks. Output always expressed as change in value per 1 bp. Defaults to 1.0*G_BASIS_POINT. Returns: (dict, Dataframe): (base_values, risk_report) base_value is a dictionary with trade_labels as keys and base trade values as values risk_report is a dataframe with bump details for rows and a column of forward rate deltas per trade, and a total for all trades

Generate forward rate deltas risk report, which is the sensitivity of trades to bucketed shocks of the instantaneous (ON) forward rates. Here shock_i is applied to the ON forward rates over [t_i, t_{i+1}] where {t_i} is the 'grid' argument Args: base_curve (DiscountCurve): base curve to apply bumps to grid: Date grid that defines ON forward rate bumps. Note that curve.value_date must be explictly included (if so desired) trades (list): a list of trades to calculate deltas of trade_labels (list, optional): trade labels to identify trades in the output. Defaults to None in which case these are auto generted bump_size (float, optional): How big of a bump to apply to bechmarks. Output always expressed as change in value per 1 bp. Defaults to 1.0*G_BASIS_POINT. Returns: (dict, Dataframe): (base_values, risk_report) base_value is a dictionary with trade_labels as keys and base trade values as values risk_report is a dataframe with bump details for rows and a column of forward rate deltas per trade, and a total for all trades

Calculate deltas (change in value to 1bp bump) of the trades to all benchmarks in the base curve. Supported trades are depos, fras, swaps. trade_labels are used to identify trades in the output, if not provided simple ones are generated Args: base_curve (IborSingleCurve): Base curve to be bumped trades (list): a list of trades to calculate deltas of trade_labels (list, optional): trade labels to identify trades in the output. Defaults to None in which case these are auto generted bump_size (float, optional): How big of a bump to apply to bechmarks. Output always expressed as change in value per 1 bp. Defaults to 1.0*G_BASIS_POINT. Returns: (base_values, risk_report): base_value is a dictionary with trade_labels as keys and base trade values as values risk_report is a dataframe with benchmarks as rows and a column of par deltas per trade, and a total for all trades