Predictions

predictions_FB.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from fbprophet import Prophet
+import yfinance as yf
+import math
+import os
+import sys
+import pandas as pd
+pd.options.mode.chained_assignment = None
+
+
+class suppress_stdout_stderr(object):
+    '''
+    Used to supress the verbose output of the inner workings of the FB prophet model
+    '''
+    def __init__(self):
+        self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)]
+        self.save_fds = (os.dup(1), os.dup(2))
+
+    def __enter__(self):
+        os.dup2(self.null_fds[0], 1)
+        os.dup2(self.null_fds[1], 2)
+
+    def __exit__(self, *_):
+        os.dup2(self.save_fds[0], 1)
+        os.dup2(self.save_fds[1], 2)
+        os.close(self.null_fds[0])
+        os.close(self.null_fds[1])
+
+def fb_predict(dfi, plot = False, days =1):
+    '''
+    This function takes in:
+    dfi - A dataframe with just the column of close price named as Close and date as index 
+    plot - Whether you want to plot the resulyts or not( Default is False)
+    days - Number of days into the future you want to predict (Dafault is 1)
+
+    This function returns:
+    mean_err - The mean percentage error in calculations
+    pred - A pandas datafrmae with predictions, upper and lower bounds
+
+    '''
+    df = dfi.copy()
+    arr = [i for i in range(len(df))]
+    ar = df.index
+    df.index= arr
+    df['ds'] = ar
+    df = df[['ds', 'Close']]
+    df.rename(columns = {"Close":'y'}, inplace = True)
+
+    train_length = math.floor(0.1*len(df))
+    train = df[:-train_length]
+    test = df[-train_length:]
+    m = Prophet(daily_seasonality = True) 
+    with suppress_stdout_stderr():
+        m.fit(train) 
+    future = m.make_future_dataframe(periods=train_length) 
+    prediction = m.predict(future)
+    test_predict = prediction[-train_length:]
+    test['predict'] = test_predict['trend']
+    test['error'] =((test['y'] - test['predict'])/test['y'])*100
+    mean_err = round(test['error'].mean(),3)
+    mn = Prophet(daily_seasonality = True) 
+    with suppress_stdout_stderr():
+        mn.fit(df) 
+    future = mn.make_future_dataframe(periods=days) 
+    prediction = mn.predict(future)
+    pred = pd.DataFrame(prediction[-days:][['ds','trend', 'yhat_upper', 'yhat_lower']])
+    pred.rename(columns = {'ds' : 'Date', 'trend':'Guess', 'yhat_lower':'Lower Bound', 'yhat_upper':'Upper Bound'}, inplace = True)
+    pred.set_index(np.arange(1,len(pred)+1), inplace = True)
+    if plot:
+        m.plot(prediction)
+        plt.title("Prediction of the AZPN Stock Price using the Prophet")
+        plt.xlabel("Date")
+        plt.ylabel("Close Stock Price")
+        plt.show()
+    return mean_err, pred
+
+#Sample Run 
+# symbol = "AZPN"
+# days = 5
+# stock = yf.Ticker(symbol)
+# df = stock.history(period="max")
+# df = df[['Close']]
+# me , pred= fb_predict(df, days = days)
+# print("\n The prediction for {} after {} days is :\n".format(symbol, days))
+# print(pred)
+# print("\n Mean Percentage Error : ", me)
+
+
+
+