trading_bot.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout


data = pd.read_csv(r"C:\Users\darsh\OneDrive\Desktop\stock_datasets\nifty_data.csv")
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)


scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))


def prepare_data(sequence, n_steps):
    X, y = [], []
    for i in range(len(sequence)):
        end_ix = i + n_steps
        if end_ix > len(sequence) - 1:
            break
        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
        X.append(seq_x)
        y.append(seq_y)
    return np.array(X), np.array(y)

n_steps = 50
X, y = prepare_data(scaled_data, n_steps)


train_size = int(len(X) * 0.8)
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]


X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))


model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(n_steps, 1)))
model.add(Dropout(0.2))
model.add(LSTM(50, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(1))

model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X_train, y_train, epochs=20, batch_size=32)


predictions = model.predict(X_test)
predictions = scaler.inverse_transform(predictions)


plt.figure(figsize=(10, 6))
plt.plot(data.index[-len(predictions):], predictions, color='red', label='Predicted Prices')
plt.plot(data.index, data['Close'], color='blue', label='Actual Prices')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Stock Price Prediction')
plt.legend()
plt.show()