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311 changes: 311 additions & 0 deletions code.py
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import pandas_datareader as web
import datetime
import numpy as np
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
pd.set_option('mode.chained_assignment', None)
import matplotlib.pyplot as plt
from keras.models import load_model
import streamlit as st
from streamlit_option_menu import option_menu
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn import metrics


from sklearn.model_selection import train_test_split

plt.style.use('bmh')

start = datetime.datetime(2010,1,1)
end = datetime.datetime.now()
with st.sidebar:
selected = option_menu(
menu_title=" Main Menu ",
options = ["CLASSIFICATION", "LINEAR REGRESSION", "SUPPORT VECTOR REGRESSION", "DECISION TREE REGRESSION", "RANDOM FOREST REGRESSION"],
)
if selected == "CLASSIFICATION":
st.title("STOCK ANALYSIS")
user_input = st.text_input("ENTER YOUR STOCK", 'NFLX')
df = web.DataReader(user_input, 'yahoo', start, end)

st.subheader("Data")
st.write(df.describe())

st.subheader("CLOSING PRICE V/S TIME", 'r')
fig = plt.figure(figsize = (18,6))
plt.plot(df.Close,'g')
st.pyplot(fig)

st.subheader("100 DAYS MOVING AVERAGE", 'r')
ma100 = df.Close.rolling(100).mean()
fig = plt.figure(figsize = (18,6))
plt.plot(ma100,'r')
plt.plot(df.Close,'g')
st.pyplot(fig)
st.subheader("Returns")
df['Return'] = df['Adj Close'].pct_change(365).shift(-365)
list_of_features = ['High', 'Low', 'Open', 'Close','Volume','Adj Close']
X = df[list_of_features]
X.tail()
y= np.where(df.Return > 0,1,0)
df
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state= 423)
treeClassifier = DecisionTreeClassifier(max_depth = 3, min_samples_leaf = 6)
treeClassifier.fit(X_train,y_train)
y_pred = treeClassifier.predict(X_test)
st.subheader("WILL THIS STOCK GENERATE A RETURN?")
from sklearn.metrics import classification_report
report = classification_report(y_test,y_pred)
st.write(report)
if selected == "DECISION TREE REGRESSION":
st.title("STOCK ANALYSIS")
user_input = st.text_input("ENTER YOUR STOCK", 'NFLX')
df = web.DataReader(user_input, 'yahoo', start, end)

st.subheader("Data")
st.write(df.describe())

#visualization
st.subheader("CLOSING PRICE V/S TIME")
fig = plt.figure(figsize=(18, 6))
plt.plot(df.Close, 'g')
st.pyplot(fig)

st.subheader("100 DAYS MOVING AVERAGE", 'r')
ma100 = df.Close.rolling(100).mean()
fig = plt.figure(figsize=(18, 6))
plt.plot(ma100, 'r')
plt.plot(df.Close, 'g')
st.pyplot(fig)

#head
st.subheader("SAMPLE")
st.write("HEAD")
future_days = 365
df['Prediction'] = df[['Adj Close']].shift(-future_days)
st.write(df.head())
#tail
st.write("TAIL")
st.write(df.tail())

#dependent
X = np.array(df.drop(['Prediction'], 1))[:-future_days]

#independent
y = np.array(df['Prediction'])[:-future_days]
#training
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
#model = DecisionTreeRegressor()
tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days)
x_future = np.array(x_future)
#result
st.subheader("PREDICTED VALUES")
tree_prediction = tree.predict(x_future)
print(tree_prediction)
lr_prediction = lr.predict(x_future)
st.write(lr_prediction)

#graphical_visualization
st.subheader("PREDICTION GRAPH")
predictions = tree_prediction
valid = df[X.shape[0]:]
valid['Predictions'] = predictions
fig = plt.figure(figsize=(16, 8))
plt.title('Decision Tree Regression')
plt.xlabel('Days')
plt.ylabel('Closing Price')
plt.plot(df['Close'])
plt.plot(valid[['Close', 'Predictions']])
plt.legend(['Orignal', 'Val', 'Predicted'])
st.pyplot(fig)
if selected == "LINEAR REGRESSION":
st.title("STOCK ANALYSIS")
user_input = st.text_input("ENTER YOUR STOCK", 'NFLX')
df = web.DataReader(user_input, 'yahoo', start, end)

st.subheader("Data")
st.write(df.describe())

st.subheader("SAMPLE")
st.write("HEAD")
forecast = 365
df['Prediction'] = df[['Adj Close']].shift(-forecast)
st.write(df.head())

# tail
st.write("TAIL")
st.write(df.tail())

#visualization
st.subheader("CLOSING PRICE V/S TIME", 'r')
fig = plt.figure(figsize=(18, 6))
plt.plot(df.Close, 'g')
st.pyplot(fig)

st.subheader("100 DAYS MOVING AVERAGE", 'r')
ma100 = df.Close.rolling(100).mean()
fig = plt.figure(figsize=(18, 6))
plt.plot(ma100, 'r')
plt.plot(df.Close, 'g')
st.pyplot(fig)

#X
X = np.array(df.drop(['Prediction'], 1))
X = X[:-forecast]
#y
y = np.array(df['Prediction'])
y = y[:-forecast]
#split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
linear_regression = LinearRegression()
linear_regression.fit(x_train, y_train)
linear_regression_confidence = linear_regression.score(x_test, y_test)
st.write("Linear Regression confidence= ", linear_regression_confidence)

x_forecast_out = np.array(df.drop(['Prediction'], 1))[-forecast:]

linear_regression_prediction = linear_regression.predict(x_forecast_out)
st.write(linear_regression_prediction)
st.subheader("PREDICTION GRAPH")
predictions = linear_regression_prediction
valid = df[X.shape[0]:]
valid['Predictions'] = predictions
fig2 = plt.figure(figsize=(16, 8))
plt.title('Linear Regression Prediction')
plt.xlabel('Days')
plt.ylabel('Closing Price')
plt.plot(df['Adj Close'])
plt.plot(valid[['Adj Close', 'Predictions']])
plt.legend(['Orignal', 'Val', 'Predicted'])
st.pyplot(fig2)
if selected == "SUPPORT VECTOR REGRESSION":
st.title("STOCK ANALYSIS")
user_input = st.text_input("ENTER YOUR STOCK", 'NFLX')
df = web.DataReader(user_input, 'yahoo', start, end)

st.subheader("Data")
st.write(df.describe())

st.subheader("SAMPLE")
st.write("HEAD")
forecast = 365
df['Prediction'] = df[['Adj Close']].shift(-forecast)
st.write(df.head())

# tail
st.write("TAIL")
st.write(df.tail())
# visualization
st.subheader("CLOSING PRICE V/S TIME", 'r')
fig = plt.figure(figsize=(18, 6))
plt.plot(df.Close, 'g')
st.pyplot(fig)

st.subheader("100 DAYS MOVING AVERAGE", 'r')
ma100 = df.Close.rolling(100).mean()
fig = plt.figure(figsize=(18, 6))
plt.plot(ma100, 'r')
plt.plot(df.Close, 'g')
st.pyplot(fig)


# X
X = np.array(df.drop(['Prediction'], 1))
X = X[:-forecast]
# y
y = np.array(df['Prediction'])
y = y[:-forecast]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
svr_rbf.fit(x_train, y_train)

svm_confidence = svr_rbf.score(x_test, y_test)
st.write('SVM confidence: ', svm_confidence)

x_forecast_out = np.array(df.drop(['Prediction'], 1))[-forecast:]

svm_prediction = svr_rbf.predict(x_forecast_out)
st.write(svm_prediction)

st.subheader("PREDICTION GRAPH")
predictions = svm_prediction
valid = df[X.shape[0]:]
valid['Predictions'] = predictions
fig3 = plt.figure(figsize=(16, 8))
plt.title('SUPPORT VECTOR REGRESSION')
plt.xlabel('Days')
plt.ylabel('Closing Price')
plt.plot(df['Adj Close'])
plt.plot(valid[['Adj Close', 'Predictions']])
plt.legend(['Orignal', 'Val', 'Predicted'])
st.pyplot(fig3)
if selected == 'RANDOM FOREST REGRESSION':
st.title("STOCK ANALYSIS")
user_input = st.text_input("ENTER YOUR STOCK", 'NFLX')
df = web.DataReader(user_input, 'yahoo', start, end)

st.subheader("Data")
st.write(df.describe())

st.write("HEAD")
forecast = 365
df['Prediction'] = df[['Adj Close']].shift(-forecast)
st.write(df.head())

# tail
st.write("TAIL")
st.write(df.tail())

st.subheader("CLOSING PRICE V/S TIME", 'r')
fig = plt.figure(figsize=(18, 6))
plt.plot(df.Close, 'g')
st.pyplot(fig)

st.subheader("100 DAYS MOVING AVERAGE", 'r')
ma100 = df.Close.rolling(100).mean()
fig = plt.figure(figsize=(18, 6))
plt.plot(ma100, 'r')
plt.plot(df.Close, 'g')
st.pyplot(fig)

future_days = 365
df['Prediction'] = df[['Close']].shift(-future_days)
df.tail(4)

#X
X = np.array(df.drop(['Prediction'], 1))[:-future_days]
#Y
y = np.array(df['Prediction'])[:-future_days]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

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You are using the variable X in valid = df[X.shape[0]:], but it's not defined anywhere in the given code. I guess you should use X_train. And also the predictions array is being assigned to the valid DataFrame's 'Predictions' column through the line valid['Predictions'] = predictions. Assigning values to a new column in a slice may not function as intended because valid is produced as a slice of the original DataFrame df. You can make a new DataFrame just for the forecasts to get around this problem.

Etree = RandomForestRegressor().fit(x_train, y_train)

x_E_future = df.drop(['Prediction'], 1)[:-future_days]
x_E_future = x_E_future.tail(future_days)
x_E_future = np.array(x_E_future)

st.subheader("PREDICTED VALUES")
tree_E_prediction = Etree.predict(x_E_future)
st.write(tree_E_prediction)

st.subheader("PREDICTION GRAPH")
predictions = tree_E_prediction
valid = df[X.shape[0]:]
valid['Predictions'] = predictions
fig4 = plt.figure(figsize=(16, 8))
plt.title('Random Forest Regression')
plt.xlabel('Days')
plt.ylabel('Closing Price')
plt.plot(df['Close'])
plt.plot(valid[['Close', 'Predictions']])
plt.legend(['Orignal', 'Val', 'Predicted'])
st.pyplot(fig4)