import pandas as pd

def load_and_clean_data(flows_path, aum_path, rates_path, gov_path):
    """
    Loads raw CSVs and parses dates for consistent time-series analysis.
    """
    # 1. Load Flows
    flows = pd.read_csv(flows_path)
    flows['Centralisation Date'] = pd.to_datetime(flows['Centralisation Date'])
    
    # 2. Load AUM
    aum = pd.read_csv(aum_path)
    aum['Centralisation Date'] = pd.to_datetime(aum['Centralisation Date'])
    
    # 3. Load Market Data (STR Rates)
    # Handling potential dd/mm/yyyy formats common in EU data
    rates = pd.read_csv(rates_path)
    try:
        rates['Date'] = pd.to_datetime(rates['Date'], dayfirst=True)
    except:
        rates['Date'] = pd.to_datetime(rates['Date'])
        
    # 4. Load Gov Indices
    gov = pd.read_csv(gov_path)
    gov['Date'] = pd.to_datetime(gov['Date'])
    
    return flows, aum, rates, gov