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import streamlit as st
import pandas as pd
import numpy as np
from pandas_datareader import data
from datetime import date, datetime
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
def dateArrange(data):
dates = data['Date'].to_list()
epochDates = []
for i in dates:
date_time = np.datetime64(i).astype(datetime)
splitDate = date_time.strftime("%Y-%#m-%d").split('-')
epochDate = datetime(int(splitDate[0]),int(splitDate[1]),int(splitDate[2]),0,0).timestamp()
epochDates.append(epochDate)
data['Date'] = epochDates
def dataScraper(ticker):
startdate = '2012-01-01'
today = date.today().strftime("%Y-%m-%d")
enddate = today
try:
panel_data = data.DataReader(ticker, 'yahoo', startdate, enddate).reset_index()
price_close = panel_data['Adj Close']
price_20dma = price_close.rolling(window=20).mean().to_list()[100:] # 20 Day Moving Average
price_50dma = price_close.rolling(window=50).mean().to_list()[100:] # 50 day
price_100dma = price_close.rolling(window=100).mean().to_list()[100:] # 100 day
panel_data = panel_data.iloc[100: , :]
panel_data['20dma'] = price_20dma
panel_data['50dma'] = price_50dma
panel_data['100dma'] = price_100dma
dateArrange(panel_data)
return panel_data
except:
print("Error while scraping data")
return
def predictor(stock_ticker):
prediction_list = []
stock_data = dataScraper(stock_ticker)
try:
stock_data.iat[0,0]
except:
print('Error with stock data')
return
from sklearn.linear_model import Lasso
alpha = 1.0
tol = 0.0008
max_iter = 10000
lasso = Lasso(alpha=alpha, max_iter=max_iter, tol=tol)
test_size = 0.1
print('\nModel = ' + str(lasso))
print('\n~ ' + stock_ticker.upper() + ' Next Day Price Predictions ~\n')
X = stock_data.iloc[:-1 , :]
y = {
'High' : stock_data.iloc[1: , :]['High'],
'Low' : stock_data.iloc[1: , :]['Low'],
'Close (Adjusted)': stock_data.iloc[1: , :]['Close']
}
sample = stock_data.iloc[-1:, :]
for i in y:
X_train, X_test, y_train, y_test = train_test_split(X, y[i], test_size=test_size)
y_pred_lasso = lasso.fit(np.array(X_train), np.array(y_train))
r2_score_lasso = r2_score(np.array(y_test), y_pred_lasso.predict(np.array(X_test)))
prediction = y_pred_lasso.predict(np.array(sample))
prediction_list.append(prediction)
output = "<h5 style='text-align: center; color: #0E7600;'>" + i + ' - ' + str(prediction) + '\n' + "</h1>"
st.markdown(output, unsafe_allow_html=True)
prediction_list.append(lasso)
prediction_list.append(r2_score_lasso)
r2score = 'R2 = ' + str(r2_score_lasso)
print(r2score)
st.markdown("<h1 style='text-align: center; color: #2BD314;'>Stock Price Predictor v2.0</h1>", unsafe_allow_html=True)
st.markdown("<h6 style='text-align: center; color: #094B00;'>by Bryan Mildort</h1>", unsafe_allow_html=True)
ticker = st.text_input('Enter Ticker to Scrape:', placeholder='SPY')
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.write(' ')
with col2:
st.write(' ')
with col3:
if st.button('Scrape!'):
predictor(ticker)
with col4:
st.write(' ')
with col5:
st.write(' ')
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