108 lines
4.8 KiB
Python
108 lines
4.8 KiB
Python
import pandas as pd
|
|
import statsmodels.api as sm
|
|
import numpy as np
|
|
|
|
def compute_static_features(flows_df, aum_df):
|
|
"""Generates descriptive features from Flows and AUM."""
|
|
|
|
# --- 1. Flow Dynamics ---
|
|
flow_stats = flows_df.groupby('Registrar Account - ID').agg(
|
|
total_subs=('Value € - Subscription', 'sum'),
|
|
total_reds=('Value € - Redemption', 'sum'),
|
|
net_flow_vol=('Value € - NetFlows', 'sum'),
|
|
txn_count=('Agreement - Code', 'count'),
|
|
# Tenure: Days between first and last activity
|
|
tenure_days=('Centralisation Date', lambda x: (x.max() - x.min()).days)
|
|
)
|
|
|
|
# Flow Ratio: -1 (Pure Seller) to +1 (Pure Buyer)
|
|
flow_stats['buy_sell_ratio'] = (flow_stats['total_subs'] - flow_stats['total_reds']) / \
|
|
(flow_stats['total_subs'] + flow_stats['total_reds'] + 1e-6)
|
|
|
|
# --- 2. Product Preferences ---
|
|
# Calculate % of flows going to each Asset Type
|
|
asset_pivot = flows_df.groupby(['Registrar Account - ID', 'Product - Asset Type'])['Value € - Subscription'].sum().unstack(fill_value=0)
|
|
asset_pct = asset_pivot.div(asset_pivot.sum(axis=1) + 1e-6, axis=0).add_prefix('pct_flow_')
|
|
|
|
# --- 3. AUM Stats ---
|
|
aum_stats = aum_df.groupby('Registrar Account - ID').agg(
|
|
avg_aum=('Value - AUM €', 'mean'),
|
|
aum_volatility=('Value - AUM €', 'std')
|
|
)
|
|
|
|
# Merge all static features
|
|
features = flow_stats.join(asset_pct).join(aum_stats, how='outer').fillna(0)
|
|
return features
|
|
|
|
def compute_market_sensitivities(flows_df, aum_df, rates_df, gov_df, freq='M'):
|
|
"""
|
|
Computes Beta sensitivity to Rates and Gov Bonds.
|
|
Filters clients based on Activity (>5 txns) and Wealth (>0 AUM).
|
|
"""
|
|
|
|
# --- 1. Prepare Market Factors ---
|
|
# Resample Rates (Take last value of period)
|
|
rates_res = rates_df.set_index('Date').resample(freq)['Yld to Maturity'].last()
|
|
delta_rates = rates_res.diff().rename('Delta_Rate')
|
|
|
|
# Resample Gov Bonds (Using 'EG04' 7-10Y Euro Gov as proxy)
|
|
gov_target = gov_df[gov_df['Bond/Index'] == 'EG04'].set_index('Date')
|
|
gov_target = gov_target[~gov_target.index.duplicated(keep='first')] # Dedup
|
|
# Calculate return over period
|
|
gov_res = gov_target['Total Return % 1-wk-LOC'].resample(freq).apply(lambda x: (1 + x/100).prod() - 1)
|
|
gov_res = gov_res.rename('Bond_Return')
|
|
|
|
market_factors = pd.concat([delta_rates, gov_res], axis=1).dropna()
|
|
|
|
# --- 2. Identify Eligible Clients (Funneling) ---
|
|
# Criterion A: Wealthy enough (Mean AUM > 1000 EUR to avoid division by zero)
|
|
mean_aum = aum_df.groupby('Registrar Account - ID')['Value - AUM €'].mean()
|
|
valid_aum_clients = mean_aum[mean_aum > 1000].index
|
|
|
|
# Criterion B: Active enough (Transactions >= 6)
|
|
txn_counts = flows_df['Registrar Account - ID'].value_counts()
|
|
active_clients = txn_counts[txn_counts >= 6].index
|
|
|
|
# Intersection: Clients who are BOTH wealthy enough and active enough
|
|
eligible_clients = list(set(valid_aum_clients) & set(active_clients))
|
|
|
|
print(f"Regression Funnel: {len(eligible_clients)} clients eligible out of {len(mean_aum)} total accounts.")
|
|
|
|
# --- 3. Run Regression on Eligible Clients ---
|
|
flows_df['Period'] = flows_df['Centralisation Date'].dt.to_period(freq).dt.to_timestamp()
|
|
client_betas = []
|
|
|
|
for client in eligible_clients:
|
|
# Get Client Flows
|
|
c_flows = flows_df[flows_df['Registrar Account - ID'] == client]
|
|
c_ts = c_flows.groupby('Period')['Quantity - NetFlows'].sum()
|
|
|
|
# Merge with market data
|
|
merged = pd.concat([c_ts, market_factors], axis=1, join='inner')
|
|
|
|
# Ensure we have enough data points for a valid regression
|
|
if len(merged) >= 6:
|
|
# Normalize Y by Client's Average AUM (Approximation of AUM_{t-1})
|
|
# Y = NetFlow_t / Mean_AUM_i
|
|
client_avg_wealth = mean_aum.loc[client]
|
|
Y = merged['Quantity - NetFlows'] / client_avg_wealth
|
|
|
|
X = merged[['Delta_Rate', 'Bond_Return']]
|
|
X = sm.add_constant(X)
|
|
|
|
try:
|
|
model = sm.OLS(Y, X).fit()
|
|
client_betas.append({
|
|
'Registrar Account - ID': client,
|
|
'alpha': model.params.get('const', 0), # Intercept (Autonomous Trend)
|
|
'beta_rate': model.params.get('Delta_Rate', 0),
|
|
'beta_bond': model.params.get('Bond_Return', 0),
|
|
'r_squared': model.rsquared
|
|
})
|
|
except:
|
|
continue
|
|
|
|
if not client_betas:
|
|
return pd.DataFrame(columns=['Registrar Account - ID', 'alpha', 'beta_rate', 'beta_bond', 'r_squared'])
|
|
|
|
return pd.DataFrame(client_betas).set_index('Registrar Account - ID') |