import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

# Step 1: Data collection
# Assuming you have a CSV file containing historical stock data
data = pd.read_csv('stock_data.csv')

# Step 2: Data preprocessing
# Drop any irrelevant columns and handle missing values if necessary
data = data.dropna()

# Step 3: Feature engineering
# Add additional features or indicators if desired
# Example: Adding a moving average
data['MA_50'] = data['Close'].rolling(window=50).mean()

# Step 4: Label generation
# Define the target variable to be predicted (e.g., future price change)
data['Target'] = np.where(data['Close'].shift(-1) > data['Close'], 1, 0)

# Step 5: Data splitting
features = data.drop(['Target'], axis=1).values
labels = data['Target'].values
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, shuffle=False)

# Step 6: Neural network architecture
class StockNN(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(StockNN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.sigmoid(out)
        return out

input_size = X_train.shape[1]
hidden_size = 64
model = StockNN(input_size, hidden_size)

# Step 7: Model training
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train, dtype=torch.float32).unsqueeze(1)

num_epochs = 10
batch_size = 32
num_batches = len(X_train_tensor) // batch_size

for epoch in range(num_epochs):
    for i in range(num_batches):
        start_idx = i * batch_size
        end_idx = (i + 1) * batch_size
        inputs = X_train_tensor[start_idx:end_idx]
        targets = y_train_tensor[start_idx:end_idx]

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

# Step 8: Model evaluation
X_test_tensor = torch.tensor(X_test, dtype=torch.float32)
y_test_tensor = torch.tensor(y_test, dtype=torch.float32).unsqueeze(1)

with torch.no_grad():
    outputs = model(X_test_tensor)
    predicted_labels = (outputs >= 0.5).float()
    accuracy = (predicted_labels == y_test_tensor).sum().item() / len(y_test_tensor)

print('Test Accuracy:', accuracy)

# Step 9: Hyperparameter tuning
# Adjust hyperparameters and repeat steps 6-8

# Step 10: Model testing
# Evaluate the model on unseen data or real-time data

# Step 11: Deployment
# Implement the trained model in a live trading environment
# Monitor performance and update the model as needed
