37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
import pandas as pd
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from data_loader import load_and_clean_data
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from features import compute_static_features, compute_market_sensitivities
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from clustering import run_clustering_pipeline, get_cluster_profiles
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def main():
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print("--- Starting Carmignac Client Clustering Pipeline ---")
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print("Loading data...")
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flows, aum, rates, gov = load_and_clean_data(
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rates_path='data/str_rates.csv',
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gov_path='data/eur_gov_indices.csv'
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)
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print("Computing static features...")
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static_feats = compute_static_features(flows, aum)
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print("Computing market sensitivities (Betas)...")
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# Use 'W' (Weekly) to maximize points for the sample.
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# Use 'M' (Monthly) for the full dataset.
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sensitivity_feats = compute_market_sensitivities(flows, aum, rates, gov, freq='W')
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full_features = static_feats.join(sensitivity_feats, how='left')
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# Clustering
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print(f"Running Clustering on {len(full_features)} clients...")
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clustered_df, centers = run_clustering_pipeline(full_features, n_clusters=3)
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print("\n--- Cluster Profiles (Mean Values) ---")
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profiles = get_cluster_profiles(clustered_df)
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print(profiles.T)
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clustered_df.to_csv('clustering/client_clusters.csv')
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print("\nResults saved to 'clustering/client_clusters.csv'")
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if __name__ == "__main__":
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main() |