91 lines
3.9 KiB
Python
91 lines
3.9 KiB
Python
import pandas as pd
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import statsmodels.api as sm
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def compute_static_features(flows_df, aum_df):
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"""Generates descriptive features from Flows and AUM."""
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# --- 1. Flow Dynamics ---
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flow_stats = flows_df.groupby('Registrar Account - ID').agg(
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total_subs=('Value € - Subscription', 'sum'),
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total_reds=('Value € - Redemption', 'sum'),
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net_flow_vol=('Value € - NetFlows', 'sum'),
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txn_count=('Agreement - Code', 'count'),
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# Tenure: Days between first and last activity
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tenure_days=('Centralisation Date', lambda x: (x.max() - x.min()).days)
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)
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# Flow Ratio: -1 (Pure Seller) to +1 (Pure Buyer)
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flow_stats['buy_sell_ratio'] = (flow_stats['total_subs'] - flow_stats['total_reds']) / \
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(flow_stats['total_subs'] + flow_stats['total_reds'] + 1e-6)
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# --- 2. Product Preferences ---
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# Calculate % of flows going to each Asset Type
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asset_pivot = flows_df.groupby(['Registrar Account - ID', 'Product - Asset Type'])['Value € - Subscription'].sum().unstack(fill_value=0)
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asset_pct = asset_pivot.div(asset_pivot.sum(axis=1) + 1e-6, axis=0).add_prefix('pct_flow_')
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# --- 3. AUM Stats ---
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aum_stats = aum_df.groupby('Registrar Account - ID').agg(
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avg_aum=('Value - AUM €', 'mean'),
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aum_volatility=('Value - AUM €', 'std')
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)
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# Merge all static features
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features = flow_stats.join(asset_pct).join(aum_stats, how='outer').fillna(0)
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return features
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def compute_market_sensitivities(flows_df, rates_df, gov_df, freq='M'):
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"""
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Computes Beta sensitivity to Rates and Gov Bonds.
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Freq: 'M' (Monthly) recommended for long history.
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"""
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# 1. Prepare Market Factors
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# Resample Rates (Take last value of period)
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rates_res = rates_df.set_index('Date').resample(freq)['Yld to Maturity'].last()
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delta_rates = rates_res.diff().rename('Delta_Rate')
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# Resample Gov Bonds (Using 'EG04' 7-10Y Euro Gov as proxy)
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gov_target = gov_df[gov_df['Bond/Index'] == 'EG04'].set_index('Date')
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gov_target = gov_target[~gov_target.index.duplicated(keep='first')] # Dedup
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# Calculate return over period
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gov_res = gov_target['Total Return % 1-wk-LOC'].resample(freq).apply(lambda x: (1 + x/100).prod() - 1)
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gov_res = gov_res.rename('Bond_Return')
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market_factors = pd.concat([delta_rates, gov_res], axis=1).dropna()
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# 2. Prepare Client Flows (Aggregated by same frequency)
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flows_df['Period'] = flows_df['Centralisation Date'].dt.to_period(freq).dt.to_timestamp()
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client_betas = []
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# Only analyze clients with sufficient activity (>5 transactions)
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active_clients = flows_df['Registrar Account - ID'].value_counts()
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active_clients = active_clients[active_clients >= 5].index
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for client in active_clients:
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c_flows = flows_df[flows_df['Registrar Account - ID'] == client]
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c_ts = c_flows.groupby('Period')['Quantity - NetFlows'].sum()
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# Inner join to align dates (Client Activity vs Market Data)
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merged = pd.concat([c_ts, market_factors], axis=1, join='inner')
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# Need enough points for regression
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if len(merged) >= 5:
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Y = merged['Quantity - NetFlows']
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X = merged[['Delta_Rate', 'Bond_Return']]
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X = sm.add_constant(X)
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try:
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model = sm.OLS(Y, X).fit()
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client_betas.append({
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'Registrar Account - ID': client,
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'beta_rate': model.params.get('Delta_Rate', 0),
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'beta_bond': model.params.get('Bond_Return', 0),
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'r_squared': model.rsquared
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})
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except:
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continue
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if not client_betas:
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return pd.DataFrame(columns=['Registrar Account - ID', 'beta_rate', 'beta_bond', 'r_squared'])
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return pd.DataFrame(client_betas).set_index('Registrar Account - ID') |