stockprediction/app.py at main · techylax/stockprediction · GitHub

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from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader as data
import yfinance as yf
import tensorflow as tf
from keras.models import load_model
import streamlit as st

st.title("Stock Trend Analysis")
user_input = st.text_input("Enter Stock Ticker", "AAPL")

start = '2010-10-20'
end = '2018-10-20'
df = yf.download(user_input, start, end)
df.head()

# Data Description

st.subheader("Data from 2015-2018")
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)


# Moving Averrage
st.subheader("Closing Price vs Time Chart with 100MA")
ma100 = df.Close.rolling(100).mean()
ma200 = df.Close.rolling(200).mean()
fig = plt.figure(figsize=(12, 6))
plt.plot(ma100)
plt.plot(ma200)
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)

# Splittinf Data into Training and Testing

data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])
data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70): int(len(df))])

scaler = MinMaxScaler(feature_range=(0, 1))
data_training_array = scaler.fit_transform(data_training)

# # Splitting Data into x_train & y_train

# x_train = []
# y_train = []
# for i in range(100, data_training_array.shape[0]):
#     x_train.append(data_training_array[i-100:i])
#     y_train.append(data_training_array[i, 0])

# x_train = np.array(x_train)
# y_train = np.array(y_train)


# Load ML 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 Orginal ")
fig2 = plt.figure(figsize=(12, 6))
plt.plot(y_test, 'b', label="Orginial Price")
plt.plot(y_predicted, 'r', label="Predicted Price")
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
st.pyplot(fig2)