app.py

import yfinance as yf
import streamlit as st
import datetime 
import matplotlib.pyplot as plt
import talib 
import ta
import numpy as np
import matplotlib.ticker as mticker
import pandas as pd
import requests
yf.pdr_override()

st.write("""
# Technical Analysis Web Application
Shown below are the **Moving Average Crossovers**, **Bollinger Bands**, **MACD's**, **Commodity Channel Indexes**, **Relative Strength Indexes** and **Extended Market Calculators** of any stock!
""")

st.sidebar.header('User Input Parameters')

today = datetime.date.today()
def user_input_features():
    ticker = st.sidebar.text_input("Ticker", 'AAPL')
    start_date = st.sidebar.text_input("Start Date", '2019-01-01')
    end_date = st.sidebar.text_input("End Date", f'{today}')
    return ticker, start_date, end_date

symbol, start, end = user_input_features()

def get_symbol(symbol):
    url = "http://d.yimg.com/autoc.finance.yahoo.com/autoc?query={}&region=1&lang=en".format(symbol)
    result = requests.get(url).json()
    for x in result['ResultSet']['Result']:
        if x['symbol'] == symbol:
            return x['name']
company_name = get_symbol(symbol.upper())

start = pd.to_datetime(start)
end = pd.to_datetime(end)

# Read data 
data = yf.download(symbol,start,end)

# Adjusted Close Price
st.header(f"""
          Adjusted Close Price\n {company_name}
          """)
st.line_chart(data['Adj Close'])

# ## SMA and EMA
#Simple Moving Average
data['SMA'] = talib.SMA(data['Adj Close'], timeperiod = 20)

# Exponential Moving Average
data['EMA'] = talib.EMA(data['Adj Close'], timeperiod = 20)

# Plot
st.header(f"""
          Simple Moving Average vs. Exponential Moving Average\n {company_name}
          """)
st.line_chart(data[['Adj Close','SMA','EMA']])

# Bollinger Bands
data['upper_band'], data['middle_band'], data['lower_band'] = talib.BBANDS(data['Adj Close'], timeperiod =20)

# Plot
st.header(f"""
          Bollinger Bands\n {company_name}
          """)
st.line_chart(data[['Adj Close','upper_band','middle_band','lower_band']])

# ## MACD (Moving Average Convergence Divergence)
# MACD
data['macd'], data['macdsignal'], data['macdhist'] = talib.MACD(data['Adj Close'], fastperiod=12, slowperiod=26, signalperiod=9)

# Plot
st.header(f"""
          Moving Average Convergence Divergence\n {company_name}
          """)
st.line_chart(data[['macd','macdsignal']])

## CCI (Commodity Channel Index)
# CCI
cci = ta.trend.cci(data['High'], data['Low'], data['Close'], n=31, c=0.015)

# Plot
st.header(f"""
          Commodity Channel Index\n {company_name}
          """)
st.line_chart(cci)

# ## RSI (Relative Strength Index)
# RSI
data['RSI'] = talib.RSI(data['Adj Close'], timeperiod=14)

# Plot
st.header(f"""
          Relative Strength Index\n {company_name}
          """)
st.line_chart(data['RSI'])

# ## OBV (On Balance Volume)
# OBV
data['OBV'] = talib.OBV(data['Adj Close'], data['Volume'])/10**6

# Plot
st.header(f"""
          On Balance Volume\n {company_name}
          """)
st.line_chart(data['OBV'])

# Extended Market
fig, ax1 = plt.subplots() 

#Asks for stock ticker
sma = 50
limit = 10

data = yf.download(symbol,start, today)

#calculates sma and creates a column in the dataframe
data['SMA'+str(sma)] = data.iloc[:,4].rolling(window=sma).mean() 
data['PC'] = ((data["Adj Close"]/data['SMA'+str(sma)])-1)*100

mean = round(data["PC"].mean(), 2)
stdev = round(data["PC"].std(), 2)
current= round(data["PC"][-1], 2)
yday= round(data["PC"][-2], 2)

stats = [['Mean', mean], ['Standard Deviation', stdev], ['Current', current], ['Yesterday', yday]]

frame = pd.DataFrame(stats,
                   columns = ['Statistic', 'Value'])

st.header(f"""
          Extended Market Calculator\n {company_name}
          """)
st.dataframe(frame.style.hide_index())

# fixed bin size
bins = np.arange(-100, 100, 1) 
plt.rcParams['figure.figsize'] = 15, 10
plt.xlim([data["PC"].min()-5, data["PC"].max()+5])

plt.hist(data["PC"], bins=bins, alpha=0.5)
plt.title(symbol+"-- % From "+str(sma)+" SMA Histogram since "+str(start.year))
plt.xlabel('Percent from '+str(sma)+' SMA (bin size = 1)')
plt.ylabel('Count')

plt.axvline( x=mean, ymin=0, ymax=1, color='k', linestyle='--')
plt.axvline( x=stdev+mean, ymin=0, ymax=1, color='gray', alpha=1, linestyle='--')
plt.axvline( x=2*stdev+mean, ymin=0, ymax=1, color='gray',alpha=.75, linestyle='--')
plt.axvline( x=3*stdev+mean, ymin=0, ymax=1, color='gray', alpha=.5, linestyle='--')
plt.axvline( x=-stdev+mean, ymin=0, ymax=1, color='gray', alpha=1, linestyle='--')
plt.axvline( x=-2*stdev+mean, ymin=0, ymax=1, color='gray',alpha=.75, linestyle='--')
plt.axvline( x=-3*stdev+mean, ymin=0, ymax=1, color='gray', alpha=.5, linestyle='--')

plt.axvline( x=current, ymin=0, ymax=1, color='r', label = 'today')
plt.axvline( x=yday, ymin=0, ymax=1, color='blue', label = 'yesterday')

#add more x axis labels
ax1.xaxis.set_major_locator(mticker.MaxNLocator(14)) 

st.pyplot(fig)

#Create Plots
fig2, ax2 = plt.subplots() 

data=data[-150:]

data['PC'].plot(label='close',color='k')
plt.title(symbol+"-- % From "+str(sma)+" SMA Over last 100 days")
plt.xlabel('Date') 
plt.ylabel('Percent from '+str(sma)+' EMA')

#add more x axis labels
ax2.xaxis.set_major_locator(mticker.MaxNLocator(8)) 
plt.axhline( y=limit, xmin=0, xmax=1, color='r')
plt.rcParams['figure.figsize'] = 15, 10
st.pyplot(fig2)