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import yfinance as yf |
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import pandas as pd |
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import numpy as np |
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import tensorflow as tf |
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import gradio as gr |
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import matplotlib.pyplot as plt |
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from sklearn.preprocessing import MinMaxScaler |
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def fetch_data(ticker, start_date, end_date): |
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data = yf.download(ticker, start=start_date, end=end_date) |
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return data |
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def prepare_data(data): |
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scaler = MinMaxScaler(feature_range=(0, 1)) |
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scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1)) |
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x_train, y_train = [], [] |
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for i in range(60, len(scaled_data)): |
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x_train.append(scaled_data[i-60:i, 0]) |
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y_train.append(scaled_data[i, 0]) |
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return np.array(x_train), np.array(y_train), scaler |
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def create_model(input_shape): |
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model = tf.keras.Sequential() |
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model.add(tf.keras.layers.LSTM(units=50, return_sequences=True, input_shape=input_shape)) |
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model.add(tf.keras.layers.LSTM(units=50, return_sequences=False)) |
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model.add(tf.keras.layers.Dense(units=25)) |
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model.add(tf.keras.layers.Dense(units=1)) |
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model.compile(optimizer='adam', loss='mean_squared_error') |
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return model |
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def predict_next_days(model, last_60_days, scaler): |
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last_60_days = np.array(last_60_days).reshape(-1, 1) |
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last_60_days_scaled = scaler.transform(last_60_days) |
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X_test = [] |
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X_test.append(last_60_days_scaled) |
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X_test = np.array(X_test) |
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predicted_price = model.predict(X_test) |
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predicted_price = scaler.inverse_transform(predicted_price) |
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return predicted_price[0][0] |
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def plot_graph(data, predicted_prices): |
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plt.figure(figsize=(14, 5)) |
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plt.plot(data['Close'], label='Historical Prices', color='blue') |
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plt.plot(predicted_prices, label='Predicted Prices', color='red') |
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plt.title('Stock Price Prediction') |
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plt.xlabel('Date') |
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plt.ylabel('Stock Price') |
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plt.legend() |
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plt.show() |
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def stock_prediction(ticker, start_date, end_date): |
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data = fetch_data(ticker, start_date, end_date) |
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x_train, y_train, scaler = prepare_data(data) |
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model = create_model((x_train.shape[1], 1)) |
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model.fit(x_train, y_train, batch_size=1, epochs=1) |
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last_60_days = data['Close'].values[-60:] |
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predicted_price = predict_next_days(model, last_60_days, scaler) |
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percentage_change = ((data['Close'][-1] - data['Close'][0]) / data['Close'][0]) * 100 |
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highest_value = data['Close'].max() |
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lowest_value = data['Close'].min() |
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plot_graph(data, predicted_price) |
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return { |
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"Predicted Price": predicted_price, |
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"Percentage Change": percentage_change, |
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"Highest Value": highest_value, |
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"Lowest Value": lowest_value, |
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} |
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stock_tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA", "FB", "NFLX", "NVDA", "INTC", "AMD"] |
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gr.Interface( |
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fn=stock_prediction, |
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inputs=[ |
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gr.Dropdown(choices=stock_tickers, label="Stock Ticker"), |
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gr.Date(label="Start Date"), |
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gr.Date(label="End Date"), |
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], |
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outputs=["json"], |
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).launch() |
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