import pandas as pd
import numpy as np

def calculate_adx(df, period=14):
    df = df.copy()
    
    # Calculate True Range
    df['h_l'] = df['high'] - df['low']
    df['h_pc'] = (df['high'] - df['close'].shift(1)).abs()
    df['l_pc'] = (df['low'] - df['close'].shift(1)).abs()
    
    df['tr'] = df[['h_l', 'h_pc', 'l_pc']].max(axis=1)
    
    # Calculate Directional Movement
    df['+dm'] = np.where((df['high'] - df['high'].shift(1)) > (df['low'].shift(1) - df['low']), 
                         np.maximum(df['high'] - df['high'].shift(1), 0), 0)
    df['-dm'] = np.where((df['low'].shift(1) - df['low']) > (df['high'] - df['high'].shift(1)), 
                         np.maximum(df['low'].shift(1) - df['low'], 0), 0)
    
    alpha = 1 / period
    df['tr_smooth'] = df['tr'].ewm(alpha=alpha, adjust=False).mean() * period
    df['+dm_smooth'] = df['+dm'].ewm(alpha=alpha, adjust=False).mean() * period
    df['-dm_smooth'] = df['-dm'].ewm(alpha=alpha, adjust=False).mean() * period
    
    df['+di'] = 100 * (df['+dm_smooth'] / df['tr_smooth'])
    df['-di'] = 100 * (df['-dm_smooth'] / df['tr_smooth'])
    
    df['dx'] = 100 * (df['+di'] - df['-di']).abs() / (df['+di'] + df['-di']).replace(0, 1)
    df['adx'] = df['dx'].ewm(alpha=alpha, adjust=False).mean()
    return df

def run_stats():
    # Use February tick data because it's a full month and manageable
    print("Loading tick data (Feb 2026)...")
    ticks = pd.read_parquet('ticks_XAUUSD.sc_2026_02.parquet')
    
    print("Resampling to OHLC...")
    ticks['time_dt'] = pd.to_datetime(ticks['time_msc'], unit='ms')
    ticks.set_index('time_dt', inplace=True)
    
    # M1 Resample
    m1 = ticks['bid'].resample('1min').ohlc()
    m1.dropna(inplace=True)
    m1 = calculate_adx(m1, 14)
    
    # M5 Resample
    m5 = ticks['bid'].resample('5min').ohlc()
    m5.dropna(inplace=True)
    m5 = calculate_adx(m5, 14)
    
    # M15 Resample
    m15 = ticks['bid'].resample('15min').ohlc()
    m15.dropna(inplace=True)
    m15 = calculate_adx(m15, 14)
    
    # Reindex to M1
    print("Combining Dataframes...")
    df = m1[['close', 'adx']].rename(columns={'adx': 'adx_M1'})
    df = df.join(m5[['adx']].rename(columns={'adx': 'adx_M5'})).ffill()
    df = df.join(m15[['adx']].rename(columns={'adx': 'adx_M15'})).ffill()
    df.dropna(inplace=True)
    
    t1, t5, t15 = 20, 16, 16
    a1 = (df['adx_M1'] >= t1).astype(int)
    a5 = (df['adx_M5'] >= t5).astype(int)
    a15 = (df['adx_M15'] >= t15).astype(int)
    
    df['phase'] = 0
    df.loc[(a1==0) & (a5==0) & (a15==0), 'phase'] = 1
    df.loc[(a1==0) & (a5==0) & (a15==1), 'phase'] = 2
    df.loc[(a1==0) & (a5==1) & (a15==0), 'phase'] = 3
    df.loc[(a1==0) & (a5==1) & (a15==1), 'phase'] = 4
    df.loc[(a1==1) & (a5==0) & (a15==0), 'phase'] = 5
    df.loc[(a1==1) & (a5==0) & (a15==1), 'phase'] = 6
    df.loc[(a1==1) & (a5==1) & (a15==0), 'phase'] = 7
    df.loc[(a1==1) & (a5==1) & (a15==1), 'phase'] = 8
    
    # Phase Transitions
    df['phase_prev'] = df['phase'].shift(1)
    df['is_transition'] = df['phase'] != df['phase_prev']
    
    total_days = (df.index[-1] - df.index[0]).total_seconds() / 86400
    
    total_transitions = df['is_transition'].sum() - 1 # Remove first row True
    
    print("\n--- TRANSITION FREQUENCY ---")
    print(f"Dataset span: {total_days:.2f} Days")
    print(f"Average transitions per DAY:   {total_transitions / total_days:.1f}")
    print(f"Average transitions per WEEK:  {total_transitions / total_days * 5:.1f} (5 trading days)")
    print(f"Average transitions per MONTH: {total_transitions / total_days * 20:.1f} (20 trading days)")
    
    print("\n--- PHASE DISTRIBUTION (%) ---")
    phase_counts = df['phase'].value_counts(normalize=True) * 100
    print(phase_counts.sort_index().round(2))
    
    print("\n--- TRANSITION MATRIX (%) ---")
    trans_df = df[df['is_transition']][1:]
    matrix = trans_df.groupby(['phase_prev', 'phase']).size().unstack(fill_value=0)
    matrix = matrix.div(matrix.sum(axis=1), axis=0) * 100
    print(matrix.round(2))

if __name__ == '__main__':
    run_stats()