import pandas as pd import numpy as np import time 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) # Smoothed TR, +DM, -DM # Wilder's Smoothing: Current = Previous + (Current - Previous) / n # EWM with alpha = 1/n 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 # Calculate +DI and -DI df['+di'] = 100 * (df['+dm_smooth'] / df['tr_smooth']) df['-di'] = 100 * (df['-dm_smooth'] / df['tr_smooth']) # Calculate DX df['dx'] = 100 * (df['+di'] - df['-di']).abs() / (df['+di'] + df['-di']).replace(0, 1) # Smoothed DX = ADX df['adx'] = df['dx'].ewm(alpha=alpha, adjust=False).mean() return df def process_data(file_M1, file_M5, file_M15): print("Loading data...") m1 = pd.read_parquet(file_M1) m5 = pd.read_parquet(file_M5) m15 = pd.read_parquet(file_M15) m1['time_dt'] = pd.to_datetime(m1['time'], unit='s') m5['time_dt'] = pd.to_datetime(m5['time'], unit='s') m15['time_dt'] = pd.to_datetime(m15['time'], unit='s') print("Calculating ADX...") m1 = calculate_adx(m1, 14) m5 = calculate_adx(m5, 14) m15 = calculate_adx(m15, 14) # Merge data to M1 freq m1.set_index('time_dt', inplace=True) m5.set_index('time_dt', inplace=True) m15.set_index('time_dt', inplace=True) df = m1[['close', 'adx', '+di', '-di']].rename(columns={'adx': 'adx_M1', '+di': 'di_plus_M1', '-di': 'di_minus_M1'}) m5_cols = m5[['adx', '+di', '-di']].rename(columns={'adx': 'adx_M5', '+di': 'di_plus_M5', '-di': 'di_minus_M5'}) df = df.join(m5_cols) df[['adx_M5', 'di_plus_M5', 'di_minus_M5']] = df[['adx_M5', 'di_plus_M5', 'di_minus_M5']].ffill() m15_cols = m15[['adx', '+di', '-di']].rename(columns={'adx': 'adx_M15', '+di': 'di_plus_M15', '-di': 'di_minus_M15'}) df = df.join(m15_cols) df[['adx_M15', 'di_plus_M15', 'di_minus_M15']] = df[['adx_M15', 'di_plus_M15', 'di_minus_M15']].ffill() df.dropna(inplace=True) # T thresholds t1 = 20 t5 = 16 t15 = 16 # Determine Phases a1 = (df['adx_M1'] >= t1).astype(int) a5 = (df['adx_M5'] >= t5).astype(int) a15 = (df['adx_M15'] >= t15).astype(int) # phase mapping 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 # Calculate Phase Transitions df['phase_prev'] = df['phase'].shift(1) df['is_transition'] = df['phase'] != df['phase_prev'] # Calculate Phase Age age = [] current_age = 0 curr_p = df['phase'].iloc[0] for p in df['phase']: if p == curr_p: current_age += 1 else: current_age = 1 curr_p = p age.append(current_age) df['phase_age_minutes'] = age # Calculate DMI Dominance (M15) di_sum = df['di_plus_M15'] + df['di_minus_M15'] di_max = df[['di_plus_M15', 'di_minus_M15']].max(axis=1) df['dmi_dominance'] = (di_max / di_sum) * 100 # Identify Future State (Next 30 minutes max displacement) # Are we successfully transitioning into a trend or reversing? # If we are at Phase 8, does it stay in P8/P7 or drop to P1? print("Evaluating Transitions...") transitions = [] for i in range(1, len(df)-30): if df['is_transition'].iloc[i]: pf = df['phase'].iloc[i] prev_pf = df['phase_prev'].iloc[i] dmi_dom = df['dmi_dominance'].iloc[i] # look ahead 30 mins, what is the dominant phase? future_phases = df['phase'].iloc[i+1:i+31] avg_future = future_phases.mean() max_future = future_phases.max() min_future = future_phases.min() transitions.append({ 'time': df.index[i], 'phase_prev': prev_pf, 'phase_new': pf, 'dmi_dominance': dmi_dom, 'future_avg_phase': avg_future, 'future_max_phase': max_future }) t_df = pd.DataFrame(transitions) # Let's analyze Phase 8 formations print("\n=== L0 (Phase 8) Simulation Results ===") p8_entries = t_df[t_df['phase_new'] == 8] print(f"Total entries into Phase 8: {len(p8_entries)}") # 1. P8 from P7 (Strong Micro) p7_to_p8 = p8_entries[p8_entries['phase_prev'] == 7] print(f"P7 -> P8 Transitions: {len(p7_to_p8)}") if len(p7_to_p8) > 0: # Success = reaches P8 and holds avg >= 7 success_7_8 = p7_to_p8[p7_to_p8['future_avg_phase'] >= 6.5] print(f" -> Win Rate (Holding Trend): {len(success_7_8)/len(p7_to_p8)*100:.2f}%") # High DMI vs Low DMI high_dmi = p7_to_p8[p7_to_p8['dmi_dominance'] > 65] if len(high_dmi) > 0: success_high = high_dmi[high_dmi['future_avg_phase'] >= 6.5] print(f" -> Win Rate w/ DMI > 65%: {len(success_high)/len(high_dmi)*100:.2f}% (Total: {len(high_dmi)})") low_dmi = p7_to_p8[p7_to_p8['dmi_dominance'] <= 55] if len(low_dmi) > 0: success_low = low_dmi[low_dmi['future_avg_phase'] >= 6.5] print(f" -> Win Rate w/ DMI <= 55%: {len(success_low)/len(low_dmi)*100:.2f}% (Total: {len(low_dmi)})") # Matrix of probabilities print("\n=== Phase Transition Probability Matrix (D0 Validations) ===") matrix = t_df.groupby(['phase_prev', 'phase_new']).size().unstack(fill_value=0) matrix = matrix.div(matrix.sum(axis=1), axis=0) * 100 print(matrix.round(2)) if __name__ == '__main__': process_data('native_rates_M1.parquet', 'native_rates_M5.parquet', 'native_rates_M15.parquet')