The Ann folder contains a script focused on building an Artificial Neural Network (ANN) for stock price prediction using TensorFlow and Keras.
Description: Implements a feedforward neural network to predict the next day's opening price and trading volume based on historical stock data.
Dependencies: pandas, numpy, scikit-learn, tensorflow
Code:
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
data = pd.read_csv('stocks.csv')
data['Next_Open'] = data['Open'].shift(-1)
data['Next_Volume'] = data['Volume'].shift(-1)
data = data.dropna()
X = data[['Open', 'High', 'Low', 'Close', 'Volume']].values
y = data[['Next_Open', 'Next_Volume]].values
scaler_x = MinMaxScaler()
scaler_y = MinMaxScaler()
X = scaler_x.fit_transform(X)
y = scaler_y.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = Sequential([
Dense(64, activation='relu', input_dim=X_train.shape[1]),
Dense(32, activation='relu'),
Dense(2, activation='linear')
])
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
history = model.fit(X_train, y_train, epochs=50, batch_size=1, validation_split=0.2, verbose=1)