-
Notifications
You must be signed in to change notification settings - Fork 0
/
app.py
104 lines (67 loc) · 2.23 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
import imp
from matplotlib import ticker
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas_datareader as data
import yfinance as yf
from yahoofinancials import YahooFinancials
from keras.models import load_model
import streamlit as st
st.title('stock trend predict')
user_input = st.text_input('Enter Stock Ticker', 'TSLA')
df = data.DataReader(user_input,'yahoo',start = '2000-01-01', end = '2022-03-31' )
#describing data
st.subheader('data from 2000 -2022')
st.write(df.describe())
#visualization
st.subheader('Closing Price vs Time Chart')
fig = plt.figure(figsize = (12,6))
plt.plot(df.Close)
st.pyplot(fig)
st.subheader('Closing Price vs Time Chart with 100MA')
ma100 = df.Close.rolling(100).mean()
fig = plt.figure(figsize = (12,6))
plt.plot(ma100)
plt.plot(df.Close)
st.pyplot(fig)
st.subheader('Closing Price vs Time Chart with 100MA &200MA')
ma100 = df.Close.rolling(100).mean()
ma200 = df.Close.rolling(200).mean()
fig = plt.figure(figsize = (12,6))
plt.plot(ma100, 'r')
plt.plot(ma200, 'g')
plt.plot(df.Close, 'b')
st.pyplot(fig)
#splitting data into training and testing Close
data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.80)])
data_testing = pd.DataFrame(df['Close'][int(len(df)*0.80): int(len(df))])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0,1))
data_training_array = scaler.fit_transform(data_training)
#load my model
model = load_model('keras_model.h5')
#testing part
past_100_days = data_training.tail(100)
final_df = past_100_days.append(data_testing, ignore_index=True)
input_data = scaler.fit_transform(final_df)
x_test = []
y_test = []
for i in range(100, input_data.shape[0]):
x_test.append(input_data[i-100: i])
y_test.append(input_data[i, 0])
x_test, y_test = np.array(x_test), np.array(y_test)
y_predicted = model.predict(x_test)
scaler = scaler.scale_
scale_factor = 1/scaler[0]
y_predicted = y_predicted * scale_factor
y_test = y_test*scale_factor
#final graph
st.subheader('Prediction vs Original')
fig2 = plt.figure(figsize=(12,6))
plt.plot(y_test, 'b', label = 'Orignal price')
plt.plot(y_predicted, 'r', label = 'Predicted price')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
st.pyplot(fig2)