Spaces:
Sleeping
Sleeping
| 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'} | |
| time_intervals = ['1d', '1m', '5m', '15m', '60m'] | |
| 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([], label='Please Select Stock from your selected index', interactive=True) | |
| d3 = gr.Dropdown(time_intervals, label='Select Time Interval', value='1d', interactive=True) | |
| d4 = gr.Radio(['Line Graph', 'Candlestick Graph'], label='Select Graph Type', value='Line Graph', interactive=True) | |
| d5 = gr.Dropdown(['ARIMA', 'Prophet', 'LSTM'], label='Select Forecasting Method', value='ARIMA', interactive=True) | |
| def forecast_series(series, model="ARIMA", forecast_period=7): | |
| predictions = list() | |
| if series.shape[1] > 1: | |
| series = series['Close'].values.tolist() | |
| if model == "ARIMA": | |
| 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) | |
| elif model == "Prophet": | |
| # Implement Prophet forecasting method | |
| pass | |
| elif model == "LSTM": | |
| # Implement LSTM forecasting method | |
| pass | |
| return predictions | |
| def is_business_day(a_date): | |
| return a_date.weekday() < 5 | |
| def get_stocks_from_index(idx): | |
| stock_data = PyTickerSymbols() | |
| index = ticker_dict[idx] | |
| stocks = list(stock_data.get_stocks_by_index(index)) | |
| stock_names = [f"{stock['name']}:{stock['symbol']}" for stock in stocks] | |
| return gr.Dropdown(choices=stock_names, label='Please Select Stock from your selected index', interactive=True) | |
| d1.input(get_stocks_from_index, d1, d2) | |
| def get_stock_graph(idx, stock, interval, graph_type, forecast_method): | |
| stock_name, ticker_name = stock.split(":") | |
| if ticker_dict[idx] == 'FTSE 100': | |
| ticker_name += '.L' if ticker_name[-1] != '.' else 'L' | |
| elif ticker_dict[idx] == 'CAC 40': | |
| ticker_name += '.PA' | |
| series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE, interval=interval) | |
| series = series.reset_index() | |
| predictions = forecast_series(series, model=forecast_method) | |
| last_date = pd.to_datetime(series['Date'].values[-1]) | |
| forecast_week = [last_date + timedelta(days=i) for i in range(1, FORECAST_PERIOD + 1) if is_business_day(last_date + timedelta(days=i))] | |
| forecast = pd.DataFrame({"Date": forecast_week, "Forecast": predictions}) | |
| if graph_type == 'Line Graph': | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter(x=series['Date'], y=series['Close'], mode='lines', name='Historical')) | |
| fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast')) | |
| else: # Candlestick Graph | |
| fig = go.Figure(data=[go.Candlestick(x=series['Date'], | |
| open=series['Open'], | |
| high=series['High'], | |
| low=series['Low'], | |
| close=series['Close'], | |
| name='Historical')]) | |
| fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast')) | |
| fig.update_layout(title=f"Stock Price of {stock_name}", | |
| xaxis_title="Date", | |
| yaxis_title="Price") | |
| return fig | |
| out = gr.Plot() | |
| inputs = [d1, d2, d3, d4, d5] | |
| d2.input(get_stock_graph, inputs, out) | |
| d3.input(get_stock_graph, inputs, out) | |
| d4.input(get_stock_graph, inputs, out) | |
| d5.input(get_stock_graph, inputs, out) | |
| demo.launch() |