153 lines
5.3 KiB
Python
153 lines
5.3 KiB
Python
import pandas as pd
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def detect_ruptures(df, epsilon=0.05):
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# Colonnes clés pour identifier les comptes
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key_cols = [
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'Agreement - Code',
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'Company - Id',
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'Company - Ultimate Parent Id',
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'Registrar Account - Region',
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'RegistrarAccount - Country',
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'Registrar Account - ID'
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]
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# Travailler sur une copie
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df_temp = df.copy()
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# Colonnes de dates
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df_temp['Centralisation Date'] = pd.to_datetime(df_temp['Centralisation Date'])
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# Dates distinctes
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full_dates = (
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pd.Series(df_temp['Centralisation Date'].unique())
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.sort_values()
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.reset_index(drop=True)
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)
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# Combinaisons comptes × dates
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accounts = df_temp[key_cols].drop_duplicates()
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full_index = accounts.merge(
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pd.DataFrame({'Centralisation Date': full_dates}),
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how='cross'
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)
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# Agréger les AUM par clé
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agg_cols = key_cols + ['Centralisation Date']
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df_agg = (
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df_temp.groupby(agg_cols)['Value - AUM €']
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.sum()
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.reset_index()
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)
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# Merge sur toutes les combinaisons
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df_full = pd.merge(full_index, df_agg, on=agg_cols, how='left')
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# Remplissage des trous par 0
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df_full['Value - AUM €'] = df_full['Value - AUM €'].fillna(0)
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# Tri
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df_full = df_full.sort_values(key_cols + ['Centralisation Date'])
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# Variation et valeur précédente
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df_full['AUM_diff'] = df_full.groupby(key_cols)['Value - AUM €'].diff().fillna(0)
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df_full['prev_value'] = df_full.groupby(key_cols)['Value - AUM €'].shift(1).fillna(0)
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# Comptes qui perdent tout
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df_zero = df_full[(df_full['AUM_diff'] < 0) & (df_full['Value - AUM €'] == 0)].copy()
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# Comptes qui partent de 0
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df_from_zero = df_full[(df_full['AUM_diff'] > 0) & (df_full['prev_value'] == 0)].copy()
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# Colonnes pour le merge (sans ID)
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merge_cols = [
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'Centralisation Date',
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'Agreement - Code',
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'Company - Id',
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'Company - Ultimate Parent Id',
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'Registrar Account - Region',
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'RegistrarAccount - Country'
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]
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# Détection des ruptures
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ruptures = pd.merge(df_zero, df_from_zero, on=merge_cols, suffixes=('_old','_new'))
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# Calcul de la différence relative selon epsilon
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ruptures['diff_rel'] = abs(ruptures['AUM_diff_old'] + ruptures['AUM_diff_new']) / (
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(abs(ruptures['AUM_diff_old']) + abs(ruptures['AUM_diff_new'])) / 2
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)
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# Filtrage avec epsilon
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ruptures = ruptures[ruptures['diff_rel'] <= epsilon].drop(columns=['diff_rel'])
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# Colonnes finales
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ruptures_df = ruptures[['Centralisation Date','Registrar Account - ID_old','Registrar Account - ID_new','AUM_diff_new']]
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ruptures_df.columns = ['date','old_account','new_account','value']
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return ruptures_df
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def check_isin_continuity(df, rupture, tol=0.05):
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"""
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Vérifie que les ISIN évoluent continuellement entre old_account et new_account.
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Args:
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df
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rupture (pd.DataFrame): Table avec colonnes ['date', 'old_account', 'new_account', 'value']
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tol (float): Tolérance relative maximale (5%)
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Returns:
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pd.DataFrame: Table avec colonnes supplémentaires :
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'isin', 'old_value', 'new_value', 'relative_change', 'check'
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"""
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df['Centralisation Date'] = pd.to_datetime(df['Centralisation Date'])
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rupture['date'] = pd.to_datetime(rupture['date'])
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# Dictionnaire des dates disponibles par compte pour trouver la date précédente
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dates_by_account = df.groupby('Registrar Account - ID')['Centralisation Date'].unique().to_dict()
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records = []
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for _, row in rupture.iterrows():
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date = row['date']
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old_account = row['old_account']
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new_account = row['new_account']
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# Date précédente pour l'ancien compte
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past_dates = [d for d in dates_by_account.get(old_account, []) if d < date]
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if not past_dates:
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continue
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prev_date = max(past_dates)
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# Filtrer df pour old_account à date précédente et new_account à date de rupture
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df_old = df[(df['Registrar Account - ID'] == old_account) &
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(df['Centralisation Date'] == prev_date)]
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df_new = df[(df['Registrar Account - ID'] == new_account) &
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(df['Centralisation Date'] == date)]
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# Tous les ISIN concernés
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isins = set(df_old['Product - Isin']).union(df_new['Product - Isin'])
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for isin in isins:
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old_val = df_old[df_old['Product - Isin'] == isin]['Quantity - AUM'].sum()
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new_val = df_new[df_new['Product - Isin'] == isin]['Quantity - AUM'].sum()
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old = df_old['Quantity - AUM'].sum()
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if old_val == 0:
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rel_change = None
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check = True
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else:
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rel_change = (new_val - old_val) / old
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check = abs(rel_change) <= tol
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records.append({
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'date': date,
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'old_account': old_account,
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'new_account': new_account,
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'isin': isin,
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'old_value': old_val,
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'new_value': new_val,
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'relative_change': rel_change,
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'check': check
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})
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return pd.DataFrame(records) |