if t = sessionEndtime(0,1) then
begin
	print(file("C:\Data\Sysname-NQ.csv"),d+19000000:8:0,",",netProfit + Openpositionprofit);
end;

20100201,1960.00
20100202,2790.00
20100203,2330.00
20100204,-2290.00
20100205,-3740.00
20100208,-3970.00
20100209,-2360.00
20100210,-3020.00
20100211,-630.00

import tkinter as tk
from tkinter import filedialog
import pandas as pd
import matplotlib.pyplot as plt
import os

def extract_market_name(filename):
    """Extracts the market name from the filename formatted as 'system-market.csv'."""
    return os.path.splitext(filename)[0].split('-')[-1]

def load_equity_data(files):
    """Loads and aligns equity data from multiple CSV files."""
    data_dict = {}

    for file in files:
        market = extract_market_name(os.path.basename(file))
        df = pd.read_csv(file, header=None, names=["Date", market], dtype={0: str, 1: float})
        df["Date"] = pd.to_datetime(df["Date"], format='%Y%m%d')
        data_dict[market] = df.set_index("Date")

    combined_df = pd.concat(data_dict.values(), axis=1, join='outer').fillna(method='ffill').fillna(method='bfill')
    combined_df["Total Equity"] = combined_df.sum(axis=1)
    return combined_df

def calculate_metrics(equity_curve):
    """Calculates total profit and maximum drawdown from the equity curve."""
    total_profit = equity_curve.iloc[-1] - equity_curve.iloc[0]
    peak = equity_curve.cummax()
    drawdown = peak - equity_curve
    max_drawdown = drawdown.max()
    return total_profit, max_drawdown, drawdown

def calculate_correlation(df):
    """Calculates and prints the correlation matrix of equity curves."""
    correlation_matrix = df.corr()
    print("\n--- Correlation Matrix ---")
    print(correlation_matrix.to_string())
    print("\n--------------------------")

def plot_equity_and_drawdown(df, drawdown):
    """Plots the combined equity curve and drawdown as separate subplots."""
    fig, ax = plt.subplots(2, 1, figsize=(12, 8), sharex=True)

    # Plot equity curves
    ax[0].set_title("Equity Curve")
    ax[0].set_ylabel("Equity Value")
    for column in df.columns[:-1]:  # Exclude "Total Equity"
        ax[0].plot(df.index, df[column], linestyle='dotted', alpha=0.6, label=f"{column} (Individual)")
    ax[0].plot(df.index, df["Total Equity"], label="Total Equity", linewidth=2, color='black')
    ax[0].legend()
    ax[0].grid()

    # Plot drawdown
    ax[1].set_title("Drawdown")
    ax[1].set_xlabel("Date")
    ax[1].set_ylabel("Drawdown Amount")
    ax[1].plot(drawdown.index, drawdown, color='red', linestyle='solid', alpha=0.7, label='Drawdown')
    ax[1].fill_between(drawdown.index, drawdown, color='red', alpha=0.3)
    ax[1].legend()
    ax[1].grid()

    plt.show()

def main():
    root = tk.Tk()
    root.withdraw()
    files = filedialog.askopenfilenames(title="Select Equity Files", filetypes=[("CSV files", "*.csv")])

    if not files:
        print("No files selected.")
        return

    combined_df = load_equity_data(files)
    total_profit, max_drawdown, drawdown = calculate_metrics(combined_df["Total Equity"])

    print("\n--- Performance Metrics ---")
    print(f"Total Profit: {total_profit:.2f}")
    print(f"Maximum Drawdown: {max_drawdown:.2f}")
    print("--------------------------")

    calculate_correlation(combined_df.drop(columns=["Total Equity"]))
    plot_equity_and_drawdown(combined_df, drawdown)

if __name__ == "__main__":
    main()