# %% Import required packages
import torch
import matplotlib.pyplot as plt
from src.models.transformer_model import TransformerModel
from src.models.rl_model import RLModel
from models.trading_model import TradingAgent
from src.evaluation.evaluate import evaluate_trading_agent
from src.data.data_preprocessing import load_processed_data

# %% Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# %% Load processed data
data = load_processed_data('./data/processed/processed_data.csv')

# %% Initialize models
transformer_model = TransformerModel().to(device)
rl_model = RLModel().to(device)
trading_agent = TradingAgent(transformer_model, rl_model)

# %% Load model weights
transformer_model.load_state_dict(torch.load('./models/transformer_model.pth'))
rl_model.load_state_dict(torch.load('./models/rl_model.pth'))

# %% Evaluate the trading agent
evaluation_results = evaluate_trading_agent(trading_agent, data)

# %% Display evaluation results
print("Total Profit: ", evaluation_results['total_profit'])
print("Total Trades Made: ", evaluation_results['total_trades'])
print("Successful Trades: ", evaluation_results['successful_trades'])

# %% Plot profit over time
plt.plot(evaluation_results['profits_over_time'])
plt.xlabel('Time')
plt.ylabel('Profit')
plt.title('Profit Over Time')
plt.show()

# %% Plot trade outcomes
plt.bar(['Successful Trades', 'Unsuccessful Trades'], 
        [evaluation_results['successful_trades'], evaluation_results['total_trades'] - evaluation_results['successful_trades']])
plt.xlabel('Trade Outcome')
plt.ylabel('Number of Trades')
plt.title('Trade Outcomes')
plt.show()

# %% Save evaluation results
with open('./logs/evaluation_results.txt', 'w') as f:
    for key, value in evaluation_results.items():
        if key != 'profits_over_time':  # Don't save the profits over time in the text file
            f.write(f'{key}: {value}\n')
            
# %% Save profits over time as a CSV file
import pandas as pd

profits_over_time_df = pd.DataFrame(evaluation_results['profits_over_time'], columns=['Profit'])
profits_over_time_df.to_csv('./logs/profits_over_time.csv', index=False)