# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import pickle

# Step 1: Data Collection
# Assuming you have a CSV file 'stock_data.csv' containing historical data

# Read the CSV file into a pandas DataFrame
df = pd.read_excel('output.xlsx')
parallelDataset = pd.read_excel('NSETickersFor-2023-04-27.inputDataset.xlsx')
# Step 2: Feature Engineering
# Preprocess and transform the data as needed
# Normalize or scale the features, handle missing data, etc.

# Step 3: Splitting the Data
# Split the data into training and testing datasets
X = df.drop(['Label', 'timestamp'], axis=1)  # Input features
parallel_X = parallelDataset.drop(['Label', 'timestamp'], axis=1)  # Input features
y = df['Label']  # Output labels
parallel_y = parallelDataset['Label']  # Output labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
parallel_X_train, parallel_X_test, parallel_y_train, parallel_y_test = train_test_split(parallel_X, parallel_y, test_size=0.2, random_state=42)

# Step 4: Model Selection
# Choose a supervised learning algorithm (e.g., Random Forest)
model = RandomForestClassifier()

# Step 5: Model Training
# Train the model using the training dataset
model.fit(X_train, y_train)

# Step 5.5: save the model 
filename = 'finalized_model.sav'
pickle.dump(model, open(filename, 'wb'))
 
# Step 6: Model Evaluation
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy on trained dataset: {accuracy}")
# Evaluate the model using the testing dataset
parallel_y_pred = model.predict(parallel_X_test)
parallel_accuracy = accuracy_score(parallel_y_test, parallel_y_pred)
print(f"Accuracy on parallel dataset: {parallel_accuracy}")

# Step 7: Real-Time Prediction
# Continuously feed real-time data to the trained model and make predictions

# Step 8: Backtesting and Optimization
# Analyze the performance of the predictions and optimize the model as needed

# Step 9: Implementation
# Integrate the model into a real-time trading system for automatic trading

