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b79786e | 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 | import pandas as pd
import numpy as np
def calculate_adx(df, period=14):
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)
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
print("Loading 4-Months of tickflow data...")
m1 = pd.read_parquet('tickflow_M1.parquet')
m5 = pd.read_parquet('tickflow_M5.parquet')
m15 = pd.read_parquet('tickflow_M15.parquet')
# Indices are already DatetimeIndex 'dt'
print("Calculating ADX for massive dataset...")
m1 = calculate_adx(m1, 14)
m5 = calculate_adx(m5, 14)
m15 = calculate_adx(m15, 14)
df = m1[['open', 'high', 'low', 'close', 'adx', '+di', '-di']].copy()
df.rename(columns={'adx': 'adx_M1', '+di': 'di_plus_M1', '-di': 'di_minus_M1'}, inplace=True)
m5_cols = m5[['adx', '+di', '-di']].rename(columns={'adx': 'adx_M5', '+di': 'di_plus_M5', '-di': 'di_minus_M5'})
df = df.join(m5_cols, how='left').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, how='left').ffill()
df.dropna(inplace=True)
print(f"Total Rows Ready: {len(df)}")
t = 18.0
a1 = (df['adx_M1'] >= t).astype(int)
a5 = (df['adx_M5'] >= t).astype(int)
a15 = (df['adx_M15'] >= t).astype(int)
# Correct EA Mapping
df['phase'] = 1
df.loc[(a15==0) & (a5==0) & (a1==1), 'phase'] = 2
df.loc[(a15==0) & (a5==1) & (a1==0), 'phase'] = 3
df.loc[(a15==1) & (a5==0) & (a1==0), 'phase'] = 4
df.loc[(a15==0) & (a5==1) & (a1==1), 'phase'] = 5
df.loc[(a15==1) & (a5==0) & (a1==1), 'phase'] = 6
df.loc[(a15==1) & (a5==1) & (a1==0), 'phase'] = 7
df.loc[(a15==1) & (a5==1) & (a1==1), 'phase'] = 8
df['phase_prev'] = df['phase'].shift(1)
df['phase_transition'] = df['phase'] != df['phase_prev']
df['dmi_dir_m15'] = np.where(df['di_plus_M15'] > df['di_minus_M15'], 1, -1)
df['dmi_dir_m5'] = np.where(df['di_plus_M5'] > df['di_minus_M5'], 1, -1)
results = []
tp_pips = 20.0
sl_pips = 20.0
print("Scanning 64 possible transitions over 4 months...")
for p_prev in range(1, 9):
for p_curr in range(1, 9):
if p_prev == p_curr: continue
mask = (df['phase_transition']) & (df['phase_prev'] == p_prev) & (df['phase'] == p_curr)
indices = np.where(mask)[0]
if len(indices) < 20: continue # minimum threshold for stats reliability
wins_trend = 0
wins_fade = 0
for i in indices:
if i > len(df) - 60: continue
entry = df['close'].iloc[i]
# determine the primary active direction
if p_prev >= 5 or p_curr >= 5:
dir_mult = df['dmi_dir_m15'].iloc[i]
elif p_curr in [3, 4] or p_prev in [3, 4]:
dir_mult = df['dmi_dir_m5'].iloc[i]
else:
dir_mult = 1 if df['close'].iloc[i] < df['open'].iloc[i] else -1 # Sideways fade
future_highs = df['high'].iloc[i+1:i+61].values
future_lows = df['low'].iloc[i+1:i+61].values
# Trend Check
wt = 0
if dir_mult == 1:
for h, l in zip(future_highs, future_lows):
if h >= entry + (tp_pips * 0.01): wt=1; break
if l <= entry - (sl_pips * 0.01): break
else:
for h, l in zip(future_highs, future_lows):
if l <= entry - (tp_pips * 0.01): wt=1; break
if h >= entry + (sl_pips * 0.01): break
wins_trend += wt
# Fade Check
wf = 0
dir_fade = -dir_mult
if dir_fade == 1:
for h, l in zip(future_highs, future_lows):
if h >= entry + (tp_pips * 0.01): wf=1; break
if l <= entry - (sl_pips * 0.01): break
else:
for h, l in zip(future_highs, future_lows):
if l <= entry - (tp_pips * 0.01): wf=1; break
if h >= entry + (sl_pips * 0.01): break
wins_fade += wf
count = len(indices)
wr_trend = (wins_trend / count) * 100
wr_fade = (wins_fade / count) * 100
results.append({
'Trans': f'P{p_prev} -> P{p_curr}',
'Count': count,
'Trend_WR': round(wr_trend, 1),
'Fade_WR': round(wr_fade, 1)
})
res_df = pd.DataFrame(results)
res_df = res_df.sort_values('Count', ascending=False)
with open('out_large_matrix.txt', 'w') as f:
f.write(res_df.to_string(index=False))
print("Done! Saved to out_large_matrix.txt")
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