diff --git a/Line Function v3.5.py b/Line Function v3.5.py
new file mode 100644
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+++ b/Line Function v3.5.py	
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+import datetime as dt
+import matplotlib.pyplot as plt
+import matplotlib.dates
+import pandas as pd
+import pandas_datareader.data as web
+import numpy as np
+
+def pct_change(first, second):
+    return (second-first) / first*100.00
+
+start = dt.datetime(2017, 5, 1)
+end = dt.datetime(2017, 7, 30)
+df = web.DataReader('MSFT', 'google', start, end)  # retrieving data from google
+
+# df2 = pd.read_csv('D:\Folders\ArJuN\Coding\My Projects\Trading Algorithm\VEDL.txt', names = ['Date','Time','Open','High','Low', 'Close', 'Volume'])
+# df2.index = df['Time']
+# df2 = df2.drop('Time', axis=1)
+# df2.shift(-1)
+# print(df2.head())
+
+high_1 = df['High'].max()
+idx_1 = df['High'].idxmax()  # use this to get index in timestamp format
+arrays = df.loc[idx_1:, 'High']  # this returns arrays starting from ts_idx_1 in df[High]
+# skipdates = 10  this lets us skip index by the specified points in array.
+arraysWskip = arrays[11:]  # returns array with keeping distance from the highest high 1
+
+high_2 = 0
+
+# this loops see whether the high 2 is below the specified percent change.
+for i in arraysWskip:
+    if pct_change(high_1, arraysWskip.max()) < -3.50:
+        high_2 = i
+        print('Found high_2 which is < -2.00 : ', high_2)
+        print('high2 = ', high_2)
+        idx_2 = arraysWskip[arraysWskip == high_2].index.tolist()[0]
+        print('idx 2 = ', idx_2)
+        # idx_2 = arraysWskip.idxmax()
+        # =============Draws Line using x and y coordinates===============#
+        idx = [idx_1, idx_2]
+        # dates = [ts_idx_1, ts_idx_2]
+        x = matplotlib.dates.date2num(idx)
+        y = [high_1, high_2]
+        Difference = x[1] - x[0]
+
+        print('y1 = ', high_1)
+        print('x1 = ', x[0])
+        print('y2 = ', high_2)
+        print('x2 = ', x[1])
+        coefficients = np.polyfit(x, y, 1)
+        polynomial = np.poly1d(coefficients)
+        # the np.linspace lets you set number of data points, line length.
+        x_axis = np.linspace(x[0], x[1] + Difference, 3)  # linspace(start, end, num)
+        y_axis = polynomial(x_axis)
+        Predicted_Value = y_axis[2]
+        print("Predicted_Value", Predicted_Value)
+
+        plt.plot(x_axis, y_axis)
+        plt.plot(x[0], y[0], 'go')
+        plt.plot(x[1], y[1], 'go')
+        loc = matplotlib.dates.AutoDateLocator()
+        print("loc = ", loc)
+        plt.gca().xaxis.set_major_locator(loc)
+        plt.gca().xaxis.set_major_formatter(matplotlib.dates.AutoDateFormatter(loc))
+        plt.gcf().autofmt_xdate()
+        df['High'].plot()
+        plt.grid('on')
+        plt.show()
+        break
+    print("looping... i = ", i)
+    # else: pass
+print("Out of the loop!")
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