File size: 6,588 Bytes
c5ef85d | 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 | import pandas as pd
import numpy as np
import os
from scipy import stats
input_file = 'merged_extracted_orders_and_deals.csv'
output_file = '4_layer_output.csv'
if not os.path.exists(input_file):
print(f"Error: File {input_file} not found.")
else:
print("Loading data...")
df = pd.read_csv(input_file)
# Ensure numeric types
df['Profit'] = pd.to_numeric(df['Profit'], errors='coerce').fillna(0)
df['Balance'] = pd.to_numeric(df['Balance'], errors='coerce').fillna(0)
# ==========================================
# --- PREVIOUS COLUMNS CALCULATION ---
# ==========================================
df['Profitable Trade'] = np.where(df['Profit'] > 0, 1, 0)
df['Unprofitable Trade'] = np.where(df['Profit'] < 0, 1, 0)
# Cumulative Counts (Internal variables)
win_c = df['Profitable Trade'].cumsum()
loss_c = df['Unprofitable Trade'].cumsum()
# --- NEWLY REQUESTED COLUMNS ---
df['rolling_profitable_trade'] = win_c
df['rolling_unprofitable_trade'] = loss_c
# -------------------------------
# Basic Rolling Metrics
df['rolling_winrate'] = (win_c / (win_c + loss_c)).fillna(0)
df['rolling_gross_profit'] = df['Profit'].clip(lower=0).cumsum()
df['rolling_gross_loss'] = df['Profit'].clip(upper=0).cumsum()
df['rolling_net'] = df['Profit'].cumsum()
# Rolling Profit Factor
df['rolling_profit_factor'] = (df['rolling_gross_profit'] / df['rolling_gross_loss'].abs()).replace([np.inf, -np.inf], 0).fillna(0)
# Drawdowns
initial_bal = df['Balance'].iloc[0] - df['Profit'].iloc[0]
df['rolling_balance_drawdown_absolute'] = (initial_bal - df['Balance'].cummin()).clip(lower=0)
peaks = df['Balance'].cummax()
dd_vals = peaks - df['Balance']
df['rolling_balance_drawdown_maximal'] = dd_vals.cummax()
df['rolling_balance_drawdown_relative'] = ((dd_vals / peaks.replace(0, np.nan)) * 100).fillna(0).cummax()
df['rolling_total_deals'] = np.arange(1, len(df) + 1)
df['rolling_win_count'] = win_c
df['rolling_lose_count'] = loss_c
df['rolling_total_trades'] = win_c + loss_c
# Rolling Averages
df['rolling_average_profit'] = (df['rolling_gross_profit'] / win_c.replace(0, np.nan)).fillna(0)
df['rolling_average_loss'] = (df['rolling_gross_loss'] / loss_c.replace(0, np.nan)).fillna(0)
df['rolling_expected_payoff'] = (df['rolling_net'] / df['rolling_total_trades'].replace(0, np.nan)).fillna(0)
# Rolling LR Correlation
print("Calculating Linear Regression (Correlation)...")
corrs = []
y = df['Balance'].values
for i in range(1, len(df) + 1):
if i < 2:
corrs.append(0)
continue
xi = np.arange(i)
yi = y[:i]
slope, intercept, r_val, p_val, std_err = stats.linregress(xi, yi)
corrs.append(r_val)
df['rolling_LR_correlation'] = corrs
# ==========================================
# --- NEW 10 COLUMNS ADDITION ---
# ==========================================
print("Calculating new streak and trade type metrics...")
# 1. Short vs Long Wins
df['rolling_long_trades_won'] = np.where(
(df['Type_order'].str.contains('buy', case=False, na=False)) & (df['Profitable Trade'] == 1), 1, 0
).cumsum()
df['rolling_short_trades_won'] = np.where(
(df['Type_order'].str.contains('sell', case=False, na=False)) & (df['Profitable Trade'] == 1), 1, 0
).cumsum()
# 2. Largest Profit/Loss Trade
df['rolling_largest_profit_trade'] = df['Profit'].clip(lower=0).cummax()
df['rolling_largest_loss_trade'] = df['Profit'].clip(upper=0).cummin()
# 3. Consecutive Metrics
mask_trade = df['Profit'] != 0
df_trades = df[mask_trade].copy()
if not df_trades.empty:
is_win = df_trades['Profit'] > 0
is_loss = df_trades['Profit'] < 0
group_id = (is_win != is_win.shift()).cumsum()
df_trades['streak_counter'] = df_trades.groupby(group_id).cumcount() + 1
df_trades['curr_win_streak'] = np.where(is_win, df_trades['streak_counter'], 0)
df_trades['curr_loss_streak'] = np.where(is_loss, df_trades['streak_counter'], 0)
df_trades['rolling_maximum_consecutive_wins'] = df_trades['curr_win_streak'].cummax()
df_trades['rolling_maximum_consecutive_loses'] = df_trades['curr_loss_streak'].cummax()
df_trades['streak_sum'] = df_trades.groupby(group_id)['Profit'].cumsum()
df_trades['curr_win_streak_sum'] = np.where(is_win, df_trades['streak_sum'], 0)
df_trades['curr_loss_streak_sum'] = np.where(is_loss, df_trades['streak_sum'], 0)
df_trades['rolling_maximal_consecutive_profit'] = df_trades['curr_win_streak_sum'].cummax()
df_trades['rolling_maximal_consecutive_loss'] = df_trades['curr_loss_streak_sum'].cummin()
# Identify start of a streak
win_start = (is_win) & (~is_win.shift(1).fillna(False))
loss_start = (is_loss) & (~is_loss.shift(1).fillna(False))
total_wins = is_win.cumsum()
total_win_streaks = win_start.cumsum()
total_losses = is_loss.cumsum()
total_loss_streaks = loss_start.cumsum()
df_trades['rolling_average_consecutive_wins'] = (total_wins / total_win_streaks.replace(0, np.nan)).fillna(0)
df_trades['rolling_average_consecutive_loses'] = (total_losses / total_loss_streaks.replace(0, np.nan)).fillna(0)
new_cols = [
'rolling_maximum_consecutive_wins', 'rolling_maximum_consecutive_loses',
'rolling_maximal_consecutive_profit', 'rolling_maximal_consecutive_loss',
'rolling_average_consecutive_wins', 'rolling_average_consecutive_loses'
]
df = df.join(df_trades[new_cols])
df[new_cols] = df[new_cols].ffill().fillna(0)
else:
for col in ['rolling_maximum_consecutive_wins', 'rolling_maximum_consecutive_loses',
'rolling_maximal_consecutive_profit', 'rolling_maximal_consecutive_loss',
'rollling_average_consecutive_wins', 'rolling_average_consecutive_loses']:
df[col] = 0
# ==========================================
# --- SAVE OUTPUT ---
# ==========================================
df.to_csv(output_file, index=False)
print(f"Success! Processed {len(df)} rows.")
print(f"Saved to: {output_file}") |