Create app.py

app.py

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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()