code: def brinson_pa(
      positions,
      bench="SH000905",
      group_field="industry",
      group_method="category",
      group_n=None,
      deal_price="vwap",
      freq="day",
  ):
      """brinson profit attribution
  
      :param positions: The position produced by the backtest class
      :param bench: The benchmark for comparing. TODO: if no benchmark is set, the equal-weighted is used.
      :param group_field: The field used to set the group for assets allocation.
                          `industry` and `market_value` is often used.
      :param group_method: 'category' or 'bins'. The method used to set the group for asstes allocation
                           `bin` will split the value into `group_n` bins and each bins represents a group
      :param group_n: . Only used when group_method == 'bins'.
  
      :return:
          A dataframe with three columns: RAA(excess Return of Assets Allocation),  RSS(excess Return of Stock Selectino),  RTotal(Total excess Return)
                                          Every row corresponds to a trading day, the value corresponds to the next return for this trading day
          The middle info of brinson profit attribution
      """
      # group_method will decide how to group the group_field.
      dates = sorted(positions.keys())
  
      start_date, end_date = min(dates), max(dates)
  
      bench_stock_weight = get_benchmark_weight(
          bench, start_date, end_date, freq
      )
  
      # The attributes for allocation will not
      if not group_field.startswith("$"):
          group_field = "$" + group_field
      if not deal_price.startswith("$"):
          deal_price = "$" + deal_price
  
      # FIXME: In current version.  Some attributes(such as market_value) of some
      # suspend stock is NAN. So we have to get more date to forward fill the NAN
      shift_start_date = start_date - datetime.timedelta(days=250)
      instruments = D.list_instruments(
          D.instruments(market="all"),
          start_time=shift_start_date,
          end_time=end_date,
          as_list=True,
          freq=freq,
      )
      stock_df = D.features(
          instruments,
          [group_field, deal_price],
          start_time=shift_start_date,
          end_time=end_date,
          freq=freq,
      )
      stock_df.columns = [group_field, "deal_price"]
  
      stock_group_field = stock_df[group_field].unstack().T
      # FIXME: some attributes of some suspend stock is NAN.
      stock_group_field = stock_group_field.fillna(method="ffill")
      stock_group_field = stock_group_field.loc[start_date:end_date]
  
      stock_group = get_stock_group(
          stock_group_field, bench_stock_weight, group_method, group_n
      )
  
      deal_price_df = stock_df["deal_price"].unstack().T
      deal_price_df = deal_price_df.fillna(method="ffill")
  
      # NOTE:
      # The return will be slightly different from the of the return in the report.
      # Here the position are adjusted at the end of the trading day with close
      stock_ret = (
          deal_price_df - deal_price_df.shift(1)
      ) / deal_price_df.shift(1)
      stock_ret = stock_ret.shift(-1).loc[start_date:end_date]
  
      port_stock_weight_df = get_stock_weight_df(positions)
  
      # decomposing the portofolio
      port_group_weight_df, port_group_ret_df = decompose_portofolio(
          port_stock_weight_df, stock_group, stock_ret
      )
      bench_group_weight_df, bench_group_ret_df = decompose_portofolio(
          bench_stock_weight, stock_group, stock_ret
      )
  
      # if the group return of the portofolio is NaN, replace it with the market
      # value
      mod_port_group_ret_df = port_group_ret_df.copy()
      mod_port_group_ret_df[
          mod_port_group_ret_df.isna()
      ] = bench_group_ret_df
  
      Q1 = (bench_group_weight_df * bench_group_ret_df).sum(axis=1)
      Q2 = (port_group_weight_df * bench_group_ret_df).sum(axis=1)
      Q3 = (bench_group_weight_df * mod_port_group_ret_df).sum(axis=1)
      Q4 = (port_group_weight_df * mod_port_group_ret_df).sum(axis=1)
  
      return (
          pd.DataFrame(
              {
                  "RAA": Q2
                  - Q1,  # The excess profit from the assets allocation
                  "RSS": Q3
                  - Q1,  # The excess profit from the stocks selection
                  # The excess profit from the interaction of assets allocation and stocks selection
                  "RIN": Q4 - Q3 - Q2 + Q1,
                  "RTotal": Q4 - Q1,  # The totoal excess profit
              },
          ),
          {
              "port_group_ret": port_group_ret_df,
              "port_group_weight": port_group_weight_df,
              "bench_group_ret": bench_group_ret_df,
              "bench_group_weight": bench_group_weight_df,
              "stock_group": stock_group,
              "bench_stock_weight": bench_stock_weight,
              "port_stock_weight": port_stock_weight_df,
              "stock_ret": stock_ret,
          },
      )
  
file_path: qlib/qlib/backtest/profit_attribution.py
imports: import datetime
  from pathlib import Path
  import numpy
  import pandas
  from ..config import C
  from ..data import D
  from .position import Position
label_desc: Use image node_id 7 for calling a global function with example usage: brinson_pa(positions, bench, group_field, group_method, group_n, deal_price, freq) and returns: pd,  dict