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def fetch_data(ticker, start_date, end_date):
# Fetch data using yfinance
data = yf.download(ticker, start=start_date, end=end_date)
return data
def preprocess_data(data):
# Select 'Close' prices for prediction
prices = data['Close'].values.reshape(-1, 1)
# Normalize the data
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_prices = scaler.fit_transform(prices)
# Create dataset with features and labels
x_data, y_data = [], []
for i in range(60, len(scaled_prices)):
x_data.append(scaled_prices[i-60:i, 0])
y_data.append(scaled_prices[i, 0])
return np.array(x_data), np.array(y_data), scaler
def build_model():
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(60, 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=1)) # Prediction for the next price
model.compile(optimizer='adam', loss='mean_squared_error')
return model
def train_model(x_data, y_data):
# Reshape data for LSTM
x_data = np.reshape(x_data, (x_data.shape[0], x_data.shape[1], 1))
model = build_model()
model.fit(x_data, y_data, epochs=50, batch_size=32)
return model
def make_prediction(model, data, scaler):
# Prepare the last 60 days of data
last_data = data['Close'].values[-60:].reshape(-1, 1)
last_data_scaled = scaler.transform(last_data)
x_test = []
x_test.append(last_data_scaled)
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predicted_price = model.predict(x_test)
predicted_price = scaler.inverse_transform(predicted_price) # Convert back to original scale
return predicted_price[0][0]
def stock_prediction(ticker, start_date, end_date):
data = fetch_data(ticker, start_date, end_date)
x_data, y_data, scaler = preprocess_data(data)
model = train_model(x_data, y_data)
predicted_price = make_prediction(model, data, scaler)
# Calculate percentage change, highest and lowest values
percentage_change = ((predicted_price - data['Close'].iloc[-1]) / data['Close'].iloc[-1]) * 100
highest_price = data['Close'].max()
lowest_price = data['Close'].min()
# Visualization
plt.figure(figsize=(14, 5))
plt.plot(data['Close'], label='Historical Prices')
plt.axhline(y=predicted_price, color='r', linestyle='--', label='Predicted Price')
plt.title(f'{ticker} Price Prediction')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()
return {
'Predicted Price': predicted_price,
'Percentage Change': percentage_change,
'Highest Price': highest_price,
'Lowest Price': lowest_price
}
# Gradio Interface
ticker_options = ['AAPL', 'GOOGL', 'AMZN', 'MSFT', 'TSLA', 'FB', 'NFLX', 'NVDA', 'BRK.B', 'DIS']
iface = gr.Interface(
fn=stock_prediction,
inputs=[
gr.inputs.Dropdown(choices=ticker_options, label='Stock Ticker'),
gr.inputs.Date(label='Start Date'),
gr.inputs.Date(label='End Date')
],
outputs='json'
)
iface.launch()
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