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2362763 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | 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')
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