import pandas as pd import math from datetime import datetime import hvplot.pandas import math import numpy as np # load data def get_processing_result_of_stocks_df(stock_df, profile_df): # add sector_name display_name name ticker_sector_map = dict( zip(profile_df['ticker'], profile_df['aggregate_sector'])) ticker_display_name_map = dict( zip(profile_df['ticker'], profile_df['display_name'])) ticker_name_map = dict(zip(profile_df['ticker'], profile_df['name'])) stock_df['display_name'] = stock_df['ticker'].map(ticker_display_name_map) stock_df['name'] = stock_df['ticker'].map(ticker_name_map) stock_df['aggregate_sector'] = stock_df['ticker'].map(ticker_sector_map) # calculate pct using closing price stock_df.sort_values(by=['date'], inplace=True) stock_df['pct'] = stock_df.groupby('ticker')['close'].pct_change() # calculate weight TODO: think about how to optimize this stock_df = stock_df.merge(profile_df[['weight', 'date', 'ticker']], on=[ 'ticker', 'date'], how='outer') stock_df.rename(columns={'weight': 'initial_weight'}, inplace=True) # create if not in stock_df stock_df['current_weight'] = float('nan') stock_df['previous_weight'] = float('nan') df_grouped = stock_df.groupby('ticker') for _, group in df_grouped: pre_w = float('nan') ini_w = float('nan') for index, row in group.iterrows(): cur_w = float('nan') # if has initial weight, the following row all use this initial weight if not pd.isna(row['initial_weight']): ini_w = row['initial_weight'] pre_w = ini_w cur_w = ini_w # just calculate current weight based on previous weight else: cur_w = pre_w * (1 + row['pct']) stock_df.loc[index, 'current_weight'] = cur_w stock_df.loc[index, 'previous_weight'] = pre_w stock_df.loc[index, 'initial_weight'] = ini_w pre_w = cur_w stock_df.rename(columns={'weight': 'initial_weight'}, inplace=True) stock_df.dropna(subset=['close'], inplace=True) # normalize weight stock_df['prev_w_in_p'] = stock_df['previous_weight'] / \ stock_df.groupby('date')['previous_weight'].transform('sum') stock_df['ini_w_in_p'] = stock_df['initial_weight'] / \ stock_df.groupby('date')['initial_weight'].transform('sum') # calculate weighted pct in portfolio stock_df['portfolio_pct'] = stock_df['pct'] * stock_df['prev_w_in_p'] # calculate weight in sector TODO: remove stock_df['prev_w_in_sectore'] = stock_df['previous_weight'] / \ stock_df.groupby(['date', 'aggregate_sector'])[ 'previous_weight'].transform('sum') stock_df['ini_w_in_sector'] = stock_df['initial_weight'] / \ stock_df.groupby(['date', 'aggregate_sector'])[ 'initial_weight'].transform('sum') # weighted pct in sector TODO: remove stock_df['sector_pct'] = stock_df['pct'] * stock_df['prev_w_in_sectore'] # portfolio return stock_df['portfolio_return'] = (stock_df.groupby( 'ticker')['portfolio_pct'].cumprod() + 1) - 1 # stock_df['cum_p_pct'] = stock_df.groupby( # 'ticker')['portfolio_pct'].cumsum() # stock_df['portfolio_return'] = np.exp(stock_df['cum_p_pct']) - 1 # stock return stock_df['return'] = (stock_df.groupby('ticker')['pct'].cumprod() + 1) - 1 # stock_df['cum_pct'] = stock_df.groupby( # 'ticker')['pct'].cumsum() # stock_df['return'] = np.exp(stock_df['cum_pct']) - 1 # drop intermediate columns stock_df = stock_df.drop(columns=['cum_p_pct']) # risk stock_df['risk'] = stock_df.groupby('ticker')['pct']\ .transform(lambda x: x.rolling(len(x), min_periods=1).std() * math.sqrt(252)) # fill na aggregate_sector stock_df['aggregate_sector'].fillna('其他', inplace=True) # sector return stock_df['sector_return'] = stock_df['ini_w_in_sector'] * \ stock_df['return'] return stock_df # total return by date def get_portfolio_evaluation(portfolio_stock, benchmark_stock, profile_df): # agg by date agg_p_stock = portfolio_stock\ .groupby('date', as_index=False)\ .agg({'portfolio_return': 'sum', 'portfolio_pct': 'sum'}) agg_b_stock = benchmark_stock\ .groupby('date', as_index=False)\ .agg({'portfolio_return': 'sum', 'portfolio_pct': 'sum'}) # add pct of benchmark merged_df = pd.merge(agg_p_stock, agg_b_stock, on=[ 'date'], how='left', suffixes=('_p', '_b')) # portfolio mkt cap mkt_adjustment = pd.DataFrame(profile_df.groupby('date')['weight'].sum()) mkt_adjustment.rename(columns={'weight': 'mkt_cap'}, inplace=True) merged_df = merged_df.merge(mkt_adjustment, on=['date'], how='outer') for i in range(len(merged_df)): if pd.isna(merged_df.loc[i, 'mkt_cap']) and i > 0: merged_df.loc[i, 'mkt_cap'] = merged_df.loc[i-1, 'mkt_cap'] * (1 + merged_df.loc[i, 'portfolio_pct_p']) # drop where portfolio_return_p is nan merged_df.dropna(subset=['portfolio_return_p'], inplace=True) # portfolio pnl TODO seem I can just use current wegith to do this merged_df['prev_mkt_cap'] = merged_df['mkt_cap'].shift(1) merged_df['pnl'] = merged_df['prev_mkt_cap'] * merged_df['portfolio_pct_p'] # risk std(pct) merged_df['risk'] = merged_df['portfolio_pct_p'].rolling( len(merged_df), min_periods=1).std() * math.sqrt(252) # active return merged_df['active_return'] = merged_df['portfolio_pct_p'] - \ merged_df['portfolio_pct_b'] # tracking errro std(active return) merged_df['tracking_error'] = merged_df['active_return'].rolling( len(merged_df), min_periods=1).std() * math.sqrt(252) # cum pnl merged_df['cum_pnl'] = merged_df['pnl'].cumsum() return merged_df def get_portfolio_sector_evaluation(portfolio_stock, benchmark_df): # aggregate on sector and day p_sector_df = portfolio_stock.groupby(['date', 'aggregate_sector'], as_index=False)\ .agg({'prev_w_in_p': 'sum', 'ini_w_in_p': "sum", "current_weight": 'sum', "portfolio_pct": "sum", 'sector_return': "sum", 'ini_w_in_sector': 'sum', "portfolio_return": "sum"}) b_sector_df = benchmark_df.groupby(['date', 'aggregate_sector'], as_index=False)\ .agg({'prev_w_in_p': 'sum', 'ini_w_in_p': "sum", "current_weight": 'sum', "portfolio_pct": "sum", "portfolio_return": "sum", 'sector_return': "sum", 'ini_w_in_sector': 'sum'}) # merge portfolio and benchmark merge_df = p_sector_df.merge( b_sector_df, on=['date', 'aggregate_sector'], how='outer', suffixes=('_p', '_b')) # to acomendate bhb result merge_df.rename(columns={'sector_return_p': 'return_p', 'sector_return_b': 'return_b'}, inplace=True) # active return merge_df['active_return'] = merge_df['portfolio_return_p'] - \ merge_df['portfolio_return_b'] # risk merge_df['risk'] = merge_df.groupby('aggregate_sector')['portfolio_pct_p']\ .transform(lambda x: x.rolling(len(x), min_periods=1).std() * math.sqrt(252)) # tracking error merge_df['tracking_error'] = merge_df.groupby('aggregate_sector')['active_return']\ .transform(lambda x: x.rolling(len(x), min_periods=1).std() * math.sqrt(252)) return merge_df # sector_eval_df = get_portfolio_sector_evaluation(portfolio_stock, benchmark_stock) # sector_eval_df[sector_eval_df.date == datetime(2021, 10,13)].hvplot.bar(x='aggregate_sector', y=['portfolio_pct_p','portfolio_pct_b'], stacked=True, rot=90, title='sector pct') def merge_on_date(calculated_ps, calculated_bs): p_selected = calculated_ps.reset_index( )[['ini_w_in_p', 'portfolio_return', 'date', 'ticker', 'display_name', 'return']] b_selected = calculated_bs.reset_index( )[['ini_w_in_p', 'portfolio_return', 'date', 'ticker', 'return']] merged_stock_df = pd.merge(p_selected, b_selected, on=[ 'date', 'ticker'], how='outer', suffixes=('_p', '_b')) return merged_stock_df # merged_df = merge_on_date(portfolio_stock, benchmark_stock) def get_bhb_result(merged_stock_df): # merged_stock_df['ini_w_in_p_p'].fillna(0, inplace=True) # merged_stock_df['ini_w_in_p_b'].fillna(0, inplace=True) # merged_stock_df['portfolio_return_b'].fillna(0, inplace=True) # merged_stock_df['portfolio_return_p'].fillna(0, inplace=True) # allocation merged_stock_df['allocation'] = (merged_stock_df['ini_w_in_p_p'] - merged_stock_df['ini_w_in_p_b']) \ * merged_stock_df['return_b'] # selection merged_stock_df['selection'] = merged_stock_df['ini_w_in_p_b'] * \ (merged_stock_df['return_p'] - merged_stock_df['return_b']) # interaction merged_stock_df['interaction'] = (merged_stock_df['ini_w_in_p_p'] - merged_stock_df['ini_w_in_p_b']) * \ (merged_stock_df['return_p'] - merged_stock_df['return_b']) # excess merged_stock_df['excess'] = merged_stock_df['portfolio_return_p'] - \ merged_stock_df['portfolio_return_b'] # replace inf with nan # merged_stock_df.replace([np.inf, -np.inf], np.nan, inplace=True) return merged_stock_df def calculate_total_attribution_by_sector(calculated_p_stock, calculated_b_stock): sector_view_p = calculated_p_stock.groupby(['date', 'aggregate_sector']).aggregate({ 'prev_w_in_p': 'sum', 'sector_pct': 'sum'}) sector_view_b = calculated_b_stock.groupby(['date', 'aggregate_sector']).aggregate({ 'prev_w_in_p': 'sum', 'sector_pct': 'sum'}) sector_view_p['weighted_return'] = sector_view_p.prev_w_in_p * \ sector_view_p.sector_pct sector_view_b['weighted_return'] = sector_view_b.prev_w_in_p * \ sector_view_b.sector_pct merged_df = pd.merge(sector_view_p, sector_view_b, left_index=True, right_index=True, how='outer', suffixes=['_b', '_p']) merged_df.fillna(0, inplace=True) merged_df['active_return'] = merged_df['weighted_return_p'] - \ merged_df['weighted_return_b'] merged_df['allocation'] = ( merged_df.prev_w_in_p_p - merged_df.prev_w_in_p_b) * merged_df.sector_pct_b merged_df['selection'] = ( merged_df.sector_pct_p - merged_df.sector_pct_b) * merged_df.prev_w_in_p_b merged_df['interaction'] = (merged_df.sector_pct_p - merged_df.sector_pct_b) * ( merged_df.prev_w_in_p_p - merged_df.prev_w_in_p_b) merged_df['notinal_return'] = merged_df.allocation + \ merged_df.selection + merged_df.interaction return merged_df.reset_index() def calculate_total_attribution(calculated_p_stock, calculated_b_stock): ''' using pct between two row's data of ticker to calculate the attribute, use this method if need to calculate weekly attribut, yearly attribut, etc. ''' merged_df = pd.merge(calculated_b_stock, calculated_p_stock, on=[ 'date', 'ticker'], how='outer', suffixes=['_b', '_p']) df = merged_df[['pct_p', 'pct_b', 'prev_w_in_p_p', 'prev_w_in_p_b', 'ticker', 'date']] df.fillna(0, inplace=True) df['active_return'] = df.pct_p * \ df.prev_w_in_p_p - df.pct_b * df.prev_w_in_p_b # allocation df['allocation'] = (df.prev_w_in_p_p - df.prev_w_in_p_b) * df.pct_b df['selection'] = (df.pct_p - df.pct_b) * df.prev_w_in_p_b df['interaction'] = (df.pct_p - df.pct_b) * \ (df.prev_w_in_p_p - df.prev_w_in_p_b) df['notional_return'] = df.allocation + df.selection + df.interaction daily_bnb_result = df.groupby(['date']).aggregate( {'allocation': 'sum', 'selection': 'sum', 'interaction': 'sum', 'notional_return': 'sum', 'active_return': 'sum'}) daily_bnb_result['date'] = daily_bnb_result.index return daily_bnb_result.reset_index(drop=True) # return df def calcualte_return(df: pd.DataFrame, start, end): ''' calcualte return within a window for each entry of ticker inclusive this is an intermediate step to calculate attribute calculation using the weighted_log_return ''' df = df[(df.time >= start) & (df.time <= end)].copy() inter_df = df.sort_values(by=['time']) inter_df['cum_log_return'] = inter_df.groupby( 'ticker')['log_return'].cumsum() inter_df['percentage_return'] = np.exp( inter_df['cum_log_return']) - 1 # patch df['return'] = inter_df['percentage_return'] return df def calculate_weighted_return(df: pd.DataFrame, start, end): ''' calcualte weighted return within a window for each entry of ticker inclusive calculation using the weighted_log_return ''' df = df[(df.time >= start) & (df.time <= end)].copy() inter_df = df.sort_values(by=['time']) inter_df['cum_weighted_log_return'] = inter_df.groupby( 'ticker')['weighted_log_return'].cumsum() inter_df['percentage_return'] = np.exp( inter_df['cum_weighted_log_return']) - 1 # patch df['weighted_return'] = inter_df['percentage_return'] return df def calculate_log_return(df: pd.DataFrame): ''' patch df with the weighted log return and unweighted log return calculated using close price an intermediate step to calculate the weighted return, the benefit using this is this can be aggregated with any time window and work for both portfolio and benchmark ''' inter_df = df.sort_values(by=['time']) grouped = inter_df.groupby('ticker') inter_df['prev_w'] = grouped['weight'].shift(1) inter_df['prev_close'] = grouped['close'].shift(1) inter_df['log_return'] = np.log(inter_df['close'] / inter_df['prev_close']) inter_df['weighted_log_return'] = inter_df['log_return'] * \ inter_df['prev_w'] # patch df['log_return'] = inter_df['log_return'] df['weighted_log_return'] = inter_df['weighted_log_return'] # TODO: change to log return instead # def calculate_return(df, start, end): # df = df[(df.time >= start) & (df.time <= end)].copy() # df.sort_values(by=['time'], inplace=True) # grouped = df.groupby('ticker') # df['return'] = (1 + grouped.pct.cumprod()) - 1 # return df # def calculate_norm_return(df, start, end): # ''' # calculate accumlative normalized return within a window # for each entry of ticker using norm_pct # normalized return is the weighted return in respect to # the whole portfolio # Return # ------ # dataframe # dataframe with return for each ticker # ''' # df = df[(df.time >= start) & (df.time <= end)].copy() # df.sort_values(by=['time'], inplace=True) # grouped = df.groupby('ticker') # df['norm_return'] = (1 + grouped.norm_pct.cumprod()) - 1 # return df def _uniformize_time_series(profile_df): ''' a helper function to create analytic_df make each entry in the time series has the same dimension by filling none holding stock that was held in previous period has 0 shares and 0 ini_w Parameters ---------- profile_df : dataframe portfolio profile dataframe or benchmark profile dataframe Returns ------- dataframe dataframe with uniformized time series ''' # Get unique time periods time_periods = profile_df['time'].unique() time_periods = sorted(time_periods) # Iterate through time periods for i in range(len(time_periods) - 1): current_period = time_periods[i] next_period = time_periods[i + 1] current_df = profile_df[profile_df['time'] == current_period] next_df = profile_df[profile_df['time'] == next_period] tickers_current = current_df['ticker'] tickers_next = next_df['ticker'] # row that has ticker not in tickers_next missing_tickers = current_df[~tickers_current.isin( tickers_next)].copy() if len(missing_tickers) != 0: missing_tickers.time = next_period missing_tickers.shares = 0 missing_tickers.ini_w = 0 profile_df = pd.concat( [profile_df, missing_tickers], ignore_index=True) # reset index return profile_df.reset_index(drop=True) def create_analytic_df(price_df, profile_df): ''' create a df for analysis processing filling information from profile df to stock price df ''' uni_profile_df = _uniformize_time_series(profile_df) # TODO handle rename column here df = price_df.merge(uni_profile_df, on=['ticker', 'time'], how='outer') df.sort_values(by=['ticker', 'time'], inplace=True) # add sector, aggregate_sector, display_name and name to missing rows grouped = df.groupby('ticker') df['sector'] = grouped['sector'].fillna(method='ffill') df['aggregate_sector'] = grouped['aggregate_sector'].fillna(method='ffill') df['display_name'] = grouped['display_name'].fillna(method='ffill') df['name'] = grouped['name'].fillna(method='ffill') # assign missing ini_w df['ini_w'] = grouped['ini_w'].fillna(method='ffill') # assign missing shares, benchmark doesn't have shares if ('shares' in df.columns): df['shares'] = grouped['shares'].fillna(method='ffill') # remove profile and price entry before first profile entry from df df.dropna(subset=['ini_w'], inplace=True) df.dropna(subset=['close'], inplace=True) # remove where weight is 0 df = df[df['ini_w'] != 0].copy() return df def calculate_attributes_between_dates(start, end, calculated_p_stock, calculated_b_stock): ''' calculate the attributes to explain the active return between two time series entries, the time series entry right after or at start and another time serie right before or at end return a df with attributes to explain the active return between start and end time series ''' p_ranged_df = calculated_p_stock[(calculated_p_stock.date >= start) & ( calculated_p_stock.date <= end)] b_ranged_df = calculated_b_stock[(calculated_b_stock.date >= start) & ( calculated_b_stock.date <= end)] # return and weight of portfolio p_start_df = p_ranged_df[p_ranged_df.date == p_ranged_df.date.min()] p_end_df = p_ranged_df[p_ranged_df.date == p_ranged_df.date.max()] p_concat = pd.concat([p_start_df, p_end_df]) # pct is unweighted return p_concat['pct'] = p_concat.groupby('ticker')['close'].pct_change() p_concat = p_concat.dropna(subset=['pct']) p_concat['prev_w_in_p'] = p_concat['ticker'].map( lambda x: p_start_df[p_start_df.ticker == x]['prev_w_in_p'].values[0]) # p_concatp_concat[['date', 'display_name', 'pct', # 'close', 'prev_w_in_p', 'ini_w_in_p']] # return and weight of benchmark b_start_df = b_ranged_df[b_ranged_df.date == b_ranged_df.date.min()] b_end_df = b_ranged_df[b_ranged_df.date == b_ranged_df.date.max()] b_concat = pd.concat([b_start_df, b_end_df]) b_concat['pct'] = b_concat.groupby('ticker')['close'].pct_change() b_concat = b_concat.dropna(subset=['pct']) b_concat['prev_w_in_p'] = b_concat['ticker'].map( lambda x: b_concat[b_concat.ticker == x]['prev_w_in_p'].values[0]) # b_concat = b_concat[['date', 'display_name', 'pct', # 'close', 'prev_w_in_p', 'ini_w_in_p']] merged_df = pd.merge(b_concat, p_concat, on=[ 'ticker', 'date'], suffixes=('_b', '_p'), how='outer') df = merged_df[['display_name_p', 'display_name_b', 'ticker', 'pct_b', 'pct_p', 'prev_w_in_p_b', 'prev_w_in_p_p']].copy() # indicate weather stock is in portfolio df['in_portfolio'] = False df.loc[df.display_name_p.notnull(), 'in_portfolio'] = True # fill display_name df['display_name_p'] = df['display_name_p'].fillna(df['display_name_b']) df['display_name_b'] = df['display_name_b'].fillna(df['display_name_p']) # treat nan weight and pct as 0 df.fillna(0, inplace=True) # allocation, selection, interaction, notional return, active return df['allocation'] = (df.prev_w_in_p_p - df.prev_w_in_p_b) * df.pct_b df['selection'] = (df.pct_p - df.pct_b) * df.prev_w_in_p_b df['interaction'] = (df.pct_p - df.pct_b) * \ (df.prev_w_in_p_p - df.prev_w_in_p_b) df['notional_return'] = df.allocation + df.selection + df.interaction # weighted return df['return'] = df.prev_w_in_p_p * df.pct_p # weight * prev_w is the weighted return df['active_return'] = df.prev_w_in_p_p * \ df.pct_p - df.prev_w_in_p_b * df.pct_b return df def calculate_cum_pnl(df, start, end): '''return df with cumulative pnl within a window''' df = df[df.time.between(start, end, inclusive='both')].copy() df.sort_values(by=['time'], inplace=True) grouped = df.groupby('ticker') df['cum_pnl'] = grouped['pnl'].cumsum() return df def change_resolution(df, freq='W'): ''' aggregate by keeping the first entry of the freq period, the resolution of the df, default to weekly ''' df['freq'] = pd.to_datetime(df['date']).dt.to_period(freq) return df.groupby('freq').first().reset_index() def calculate_pnl(df): ''' patch df with pnl column pnl is calculated using cash ''' df.sort_values(by=['time'], inplace=True) grouped = df.groupby('ticker') df['pnl'] = grouped['cash'].diff() def calculate_pct(df): ''' calculate pct using close price ''' df.sort_values(by=['time'], inplace=True) grouped = df.groupby('ticker') df['pct'] = grouped['close'].pct_change() def calculate_norm_pct(df): ''' use weight to calculate the norm pct ''' df['norm_pct'] = df.weight * df.pct def calculate_weight_using_cash(df): ''' patch df with current weight for each entry use cash to calculate weight Parameters ---------- df : dataframe dataframe with processed cash column ''' df['weight'] = float('nan') grouped = df.groupby('time') df.weight = grouped.cash.transform(lambda x: x / x.sum()) def calculate_cash(df): ''' patch df with cash column cash = shares * close Parameters ---------- df : dataframe dataframe with processed shares and close column ''' df['cash'] = df['shares'] * df['close'] def calculate_weight_using_pct(df): ''' calculate weight using weight column calculate benchmark stock using this, since benchmark stock doesn't have share information Parameters ---------- df: dataframe dataframe with weight, pct on closing and ini_w columns ''' df.sort_values(by=['time'], inplace=True) grouped = df.groupby('ticker') for _, group in grouped: prev_row = None for index, row in group.iterrows(): if prev_row is None: prev_row = df.loc[index] continue df.loc[index, 'weight'] = prev_row['weight'] * (1 + row['pct']) prev_row = df.loc[index] # normalize weight grouped = df.groupby('time') normed_weight = grouped['weight'].transform(lambda x: x / x.sum()) df['weight'] = normed_weight def calculate_periodic_BHB(agg_b, agg_p): ''' calculate periodic BHB for each ticker entry the accumulated return of a period will be used, the weight is the weight at the began of the period Note: ---- if only one entry in a period, the return will be nan, Parameters ---------- agg_b : pd.DataFrame aggregated benchmark analytic_df agg_p : pd.DataFrame aggregated portfolio analytic_df Returns ------- pd.DataFrame periodic BHB result contain allocation, interaction, selection, nominal_active_return and active_return ''' # merge both agg_b['in_benchmark'] = True agg_p['in_portfolio'] = True selected_column = ['ticker', 'aggregate_sector', 'prev_weight', 'return', 'period', 'display_name'] columns_to_fill = ['return_b', 'return_p', 'prev_weight_p', 'prev_weight_b'] merged_df = pd.merge(agg_b[['in_benchmark'] + selected_column], agg_p, how='outer', on=['period', 'ticker'], suffixes=('_b', '_p')) merged_df['in_portfolio'].fillna(False, inplace=True) merged_df['in_benchmark'].fillna(False, inplace=True) merged_df[columns_to_fill] = merged_df[columns_to_fill].fillna(0) # complement fill aggregate_sector and display_name merged_df['aggregate_sector_b'].fillna( merged_df['aggregate_sector_p'], inplace=True) merged_df["display_name_b"].fillna(merged_df.display_name_p, inplace=True) merged_df.rename(columns={'aggregate_sector_b': 'aggregate_sector', 'display_name_b': 'display_name', }, inplace=True) merged_df.drop(columns=['aggregate_sector_p', 'display_name_p'], inplace=True) # calculate active return merged_df['weighted_return_p'] = merged_df['return_p'] * \ merged_df['prev_weight_p'] merged_df['weighted_return_b'] = merged_df['return_b'] * \ merged_df['prev_weight_b'] merged_df['active_return'] = merged_df['weighted_return_p'] - \ merged_df['weighted_return_b'] # allocation, interaction, selection and nominal active return merged_df['allocation'] = ( merged_df.prev_weight_p - merged_df.prev_weight_b) * merged_df.return_b merged_df['interaction'] = (merged_df.return_p - merged_df.return_b) \ * (merged_df.prev_weight_p - merged_df.prev_weight_b) merged_df['selection'] = ( merged_df.return_p - merged_df.return_b) * merged_df.prev_weight_b merged_df['notional_active_return'] = merged_df['allocation'] + \ merged_df['interaction'] + merged_df['selection'] return merged_df def _merge_anlaytic_df(portfolio_df, benchmark_df): pass def aggregate_analytic_df_by_period(df, freq): ''' return an aggregated analytic_df with weekly, monthly, yearly or daily frequency each ticker will have 1 rows for each period, cash is the value at the end of the period. shares is the # of shares at end of the period. prev_weight is the weight of that ticker entry at end of previous period. log_return is sum of log_return within the period. weight is the weight of that ticker entry at end of the period. return is from last of previous period to last of current period. Parameters ---------- df : pd.DataFrame analytic_df, dateframe of stock price has weight, log_return information freq : str weekly: 'W-MON' start on tuesday end on monday, monthly: 'M', yearly: 'Y', daily: "D" Returns ------- pd.DataFrame aggregated analytic_df with weekly, monthly, yearly or daily frequency ''' # create prev_weight df.sort_values(by=['time'], inplace=True) grouped = df.groupby('ticker') df['prev_weight'] = grouped['weight'].shift(1) # aggregate by summing log return and keep the first prev_weight df['period'] = df.time.dt.to_period(freq) grouped = df.groupby(['period', 'ticker']) agg_rules = {'display_name': 'first', 'aggregate_sector': 'first', 'prev_weight': 'first', 'log_return': 'sum', 'weight': 'last' } # handle aggregate on benchamrk if 'cash' in df.columns and 'shares' in df.columns: agg_rules['cash'] = 'last' agg_rules['shares'] = 'last' # aggregation agg_df = grouped.agg(agg_rules) # calculate return by convert sum log return to percentage return agg_df['return'] = np.exp(agg_df.log_return) - 1 # make it a one dimensional dataframe agg_df.reset_index(inplace=True) return agg_df def aggregate_bhb_df(df, by="total"): keys = ['period', 'aggregate_sector'] if by == 'sector' else ['period'] agg_df = df.groupby(keys)[['active_return', 'allocation', 'interaction', 'selection', 'notional_active_return']].sum() return agg_df