import pandas as pd
import numpy as np
import os

# Load 2022 EURUSD 1m data
df = pd.read_excel("data/DAT_XLSX_EURUSD_M1_2021.xlsx", header=None, names=[
    'timestamp', 'open', 'high', 'low', 'close', 'volume'
])
df['timestamp'] = pd.to_datetime(df['timestamp'])
df.set_index('timestamp', inplace=True)

# Resample to 15-minute intervals
df = df.resample('15min').agg({
    'open': 'first',
    'high': 'max',
    'low': 'min',
    'close': 'last',
    'volume': 'sum'
}).dropna()

# Create perfect actions
lookahead = 4  # ~1 hour
profit_threshold = 0.001
loss_threshold = 0.001

labels = []
for i in range(len(df) - lookahead):
    current_close = df.iloc[i]['close']
    future_window = df.iloc[i + 1:i + 1 + lookahead]
    max_future_price = future_window['high'].max()
    min_future_price = future_window['low'].min()

    if max_future_price >= current_close * (1 + profit_threshold):
        labels.append(1)
    elif min_future_price <= current_close * (1 - loss_threshold):
        labels.append(-1)
    else:
        labels.append(0)

labels += [0] * lookahead
df['perfect_action'] = labels

# Simulate perfect trader
balance = 10000.0
position = 0.0
entry_price = 0.0
win_count = 0
loss_count = 0
trades_count = 0
trades = []

for i in range(len(df)):
    price = df.iloc[i]['close']
    action = df.iloc[i]['perfect_action']

    if action == 1 and position == 0:
        position = balance / price
        entry_price = price
        trades.append({
            "timestamp": df.index[i],
            "action": "buy",
            "price": price,
            "balance": balance
        })
        balance = 0
        trades_count += 1

    elif action == -1 and position > 0:
        exit_value = position * price
        pnl = exit_value - (position * entry_price)
        if pnl > 0:
            win_count += 1
        else:
            loss_count += 1
        balance = exit_value
        trades.append({
            "timestamp": df.index[i],
            "action": "sell",
            "price": price,
            "balance": balance
        })
        position = 0
        entry_price = 0

# Finalize any open position
if position > 0:
    balance = position * df.iloc[-1]['close']

# Results
print("\n📊 Perfect Trader Simulation:")
print(f"🟢 Starting Balance: $10,000.00")
print(f"🔚 Ending Balance: ${balance:,.2f}")
print(f"💼 Total Trades: {trades_count}")
print(f"✅ Wins: {win_count} | ❌ Losses: {loss_count}")
if trades_count > 0:
    print(f"📈 Win Rate: {win_count / trades_count * 100:.2f}%")

# Save trade log
output_path = "ml/perfect_trades.csv"
os.makedirs("ml", exist_ok=True)

trades_df = pd.DataFrame(trades)
trades_df.to_csv(output_path, index=False)
print(f"📁 Perfect trades saved to: {output_path}")