Upload arima.py

arima.py

@@ -4,11 +4,13 @@ from bs4 import BeautifulSoup
 import io   
 import yfinance as yf
 from datetime import datetime
+from datetime import timedelta
 import numpy as np
 import statsmodels.api as sm
 
-# import plotly.express as px
-import matplotlib.pyplot as plt
+import plotly.express as px
+import plotly.graph_objects as go
+# import matplotlib.pyplot as plt
 
 # from statistics import covariance
 
@@ -50,6 +52,9 @@ def get_model_accuracy(data, ticker_symbol):
 
 def main(tickers, earliest_date):
     df = pd.read_csv('data_and_sp500.csv')
+    df = df[['Date']+tickers]
+    fig = px.line(df, x='Date', y=df.columns)
+
     for ticker in tickers:
         x = np.array(df['Date'])
         y = np.array(df[ticker])
@@ -59,20 +64,25 @@ def main(tickers, earliest_date):
         model_fit = model.fit(disp=-1)
         # print(model_fit.summary())
         forecast = model_fit.forecast(7, alpha=0.05)#.predict(start=1259, end=1289)
-
-        data = pd.Series(y, x)
-
+        begin_date = datetime.strptime('2021-10-22', '%Y-%m-%d')
+        forecast_dates = [begin_date+timedelta(days=i-1258) for i in forecast.index]
+        fig.add_trace(go.Scatter(x=forecast_dates, y=forecast.to_list(),
+                                    mode='lines',
+                                    name='{} forecast'.format(ticker)))
+
+    fig.update_xaxes(range=[begin_date-timedelta(days=120), begin_date+timedelta(days=10)])
+    fig.show()
         # plt.plot(fit1.fittedvalues, marker="o", color="blue", label='smoothing')
 
-        plt.plot(x, y, label='{} historical'.format(ticker))
-        plt.plot(forecast, label='{} forecast'.format(ticker))
-        plt.legend(loc="upper left")
-        step = 10
-        plt.xticks([x[i+step] for i in range(-step, len(x), step) if i+step < len(x)], rotation=90)
+    #     plt.plot(x, y, label='{} historical'.format(ticker))
+    #     plt.plot(forecast, label='{} forecast'.format(ticker))
+    #     plt.legend(loc="upper left")
+    #     step = 10
+    #     plt.xticks([x[i+step] for i in range(-step, len(x), step) if i+step < len(x)], rotation=90)
 
-    plt.title('ARIMA forecast model vs. actual for {}'.format('ticker'))
-    plt.xlim(df.shape[0]-100, df.shape[0]+21)
-    plt.show()
+    # plt.title('ARIMA forecast model vs. actual for {}'.format('ticker'))
+    # plt.xlim(df.shape[0]-21, df.shape[0]+21)
+    # plt.show()
     # plot_df = data.to_frame().reset_index().rename(columns={'index': 'date', 0: 'price'})
     # plot_df.columns = ['date', 'price']
     # print(plot_df)