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| import datetime | |
| import gradio as gr | |
| import pandas as pd | |
| import yfinance as yf | |
| import seaborn as sns; | |
| sns.set() | |
| import matplotlib.pyplot as plt | |
| import plotly.graph_objects as go | |
| from datetime import date, timedelta | |
| from matplotlib import pyplot as plt | |
| from plotly.subplots import make_subplots | |
| from pytickersymbols import PyTickerSymbols | |
| from statsmodels.tsa.arima.model import ARIMA | |
| from pandas.plotting import autocorrelation_plot | |
| from dateutil.relativedelta import relativedelta | |
| index_options = ['FTSE 100(UK)', 'NASDAQ(USA)', 'CAC 40(FRANCE)'] | |
| ticker_dict = {'FTSE 100(UK)': 'FTSE 100', 'NASDAQ(USA)': 'NASDAQ 100', 'CAC 40(FRANCE)': 'CAC 40'} | |
| global START_DATE, END_DATE | |
| END_DATE = date.today() | |
| START_DATE = END_DATE - relativedelta(years=1) | |
| FORECAST_PERIOD = 7 | |
| demo = gr.Blocks() | |
| stock_names = [] | |
| with demo: | |
| d1 = gr.Dropdown(index_options, label='Please select Index...', | |
| info='Will be adding more indices later on', | |
| interactive=True) | |
| d2 = gr.Dropdown([]) # for specific stocks | |
| # d3 = gr.Dropdown(['General News']) | |
| def forecast_series(series, model="ARIMA", forecast_period=7): | |
| predictions = list() | |
| if series.shape[1] > 1: | |
| series = series['Close'].values.tolist() | |
| plt.show() | |
| if model == "ARIMA": | |
| ## Do grid search here --> Custom for all stocks | |
| for i in range(forecast_period): | |
| model = ARIMA(series, order=(5, 1, 0)) | |
| model_fit = model.fit() | |
| output = model_fit.forecast() | |
| yhat = output[0] | |
| predictions.append(yhat) | |
| series.append(yhat) | |
| return predictions | |
| def is_business_day(a_date): | |
| return a_date.weekday() < 5 | |
| def get_stocks_from_index(idx): | |
| stock_data = PyTickerSymbols() | |
| # indices = stock_data.get_all_indices() | |
| index = ticker_dict[idx] | |
| stock_data = PyTickerSymbols() | |
| # returns 2d list with the following information | |
| # 'name', 'symbol', 'country', 'indices', 'industries', 'symbols', 'metadata', 'isins', 'akas' | |
| stocks = list(stock_data.get_stocks_by_index(index)) ##converting filter object to list | |
| stock_names = [] | |
| for stock in stocks: | |
| stock_names.append(stock['name'] + ':' + stock['symbol']) | |
| d2 = gr.Dropdown(choices=stock_names, label='Please Select Stock from your selected index', interactive=True) | |
| return d2 | |
| d1.input(get_stocks_from_index, d1, d2) | |
| out = gr.Plot(every=10) | |
| def get_stock_graph(idx, stock): | |
| stock_name = stock.split(":")[0] | |
| ticker_name = stock.split(":")[1] | |
| if ticker_dict[idx] == 'FTSE 100': | |
| if ticker_name[-1] == '.': | |
| ticker_name += 'L' | |
| else: | |
| ticker_name += '.L' | |
| elif ticker_dict[idx] == 'CAC 40': | |
| ticker_name += '.PA' | |
| ## Can also download lower interval data apparently using line below | |
| # data = yf.download(tickers="MSFT", period="5d", interval="1m") | |
| series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE) # stock.split(":")[1] | |
| series = series.reset_index() | |
| predictions = forecast_series(series) | |
| last_date = pd.to_datetime(series['Date'].values[-1]) | |
| forecast_week = [] | |
| while len(forecast_week) != FORECAST_PERIOD: | |
| if is_business_day(last_date): | |
| forecast_week.append(last_date) | |
| last_date += timedelta(days=1) | |
| forecast = pd.DataFrame({"Date": forecast_week, "Forecast": predictions}) | |
| fig = plt.figure(figsize=(14, 5)) | |
| sns.set_style("ticks") | |
| sns.lineplot(data=series, x="Date", y="Close", color="firebrick") | |
| sns.lineplot(data=forecast, x="Date", y="Forecast", color="blue") | |
| sns.despine() | |
| plt.title("Stock Price of {}".format(stock_name), size='x-large', color='blue') # stock.split(":")[0] | |
| text = "Your stock is:" + str(stock) | |
| return fig | |
| d2.input(get_stock_graph, [d1, d2], out) | |
| demo.launch() |