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| from flask import Flask, render_template, request | |
| import pandas as pd | |
| import numpy as np | |
| import tensorflow as tf | |
| import yfinance as yf | |
| import pickle | |
| app = Flask(__name__) | |
| # Define tickers and load models | |
| tickers = ['AMZN', 'TSLA', 'AAPL'] | |
| models = {} | |
| for ticker in tickers: | |
| model = tf.keras.models.load_model(f'model_{ticker}.h5') | |
| models[ticker] = model | |
| with open('min_max.pickle', 'rb') as handle: | |
| min_max_scaling = pickle.load(handle) | |
| # Function to prepare the data for model input | |
| def prepare_data(data): | |
| # Assuming the data is a 1D array of closing prices | |
| # Reshape the data to have the shape (batch_size, timesteps, features) | |
| data = np.array(data) | |
| data = data.reshape(1, data.shape[0], 1) | |
| return data | |
| # Function to get the last 60 days data for a ticker | |
| def get_last_60_days_data(ticker): | |
| # Define the end date as yesterday | |
| end_date = pd.Timestamp.today() - pd.Timedelta(days=1) | |
| # Define the start date as 120 days before the end date | |
| start_date = end_date - pd.Timedelta(days=120) | |
| # Fetch the stock data using yfinance | |
| stock_data = yf.download(ticker, start=start_date, end=end_date, progress=False) | |
| # Ensure we have enough data (at least 60 days) | |
| if len(stock_data) < 60: | |
| return None | |
| # Extract the last 60 days 'Close' prices from the stock data | |
| last_60_days_data = stock_data['Close'].tolist()[-60:] | |
| last_60_days_data = (last_60_days_data - min_max_scaling[ticker][0])/(min_max_scaling[ticker][1] - min_max_scaling[ticker][0]) | |
| return last_60_days_data.tolist() | |
| # Function to predict the next day closing value using the model | |
| def predict_next_day(ticker, data): | |
| model = models[ticker] | |
| data = prepare_data(data) | |
| prediction = model.predict(data) | |
| return prediction[0] | |
| def scale_back_data(data,ticker): | |
| data = np.array(data) | |
| data = data * (min_max_scaling[ticker][1] - min_max_scaling[ticker][0]) + min_max_scaling[ticker][0] | |
| return data.tolist() | |
| # @app.route('/') | |
| # def hello_world(): | |
| # return "Hello World" | |
| # Flask route to handle the main page | |
| def index(): | |
| if request.method == 'POST': | |
| selected_ticker = request.form['ticker'] | |
| last_60_days_data = get_last_60_days_data(selected_ticker) | |
| last_60_days_data_original = get_last_60_days_data(selected_ticker) | |
| predictions = predict_next_day(selected_ticker, last_60_days_data) | |
| # for _ in range(10): | |
| # next_day_prediction = predict_next_day(selected_ticker, last_60_days_data) | |
| # predictions.append(next_day_prediction) | |
| # last_60_days_data.append(next_day_prediction) | |
| # last_60_days_data.pop(0) | |
| predictions = scale_back_data(predictions,selected_ticker) | |
| last_60_days_data_original = scale_back_data(last_60_days_data_original,selected_ticker) | |
| return render_template('index.html', tickers=tickers, selected_ticker=selected_ticker, predictions=predictions, last_60_days_data=last_60_days_data_original) | |
| else: | |
| return render_template('index.html', tickers=tickers, selected_ticker=None, predictions=None, last_60_days_data=None) | |
| if __name__ == '__main__': | |
| app.run() |