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dsr_prototype.py
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import streamlit as st
import pandas as pd
import numpy as np
import yfinance as yf
import time
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from stockstats import StockDataFrame
st.title('Stock Guru')
st.markdown("_~ C R Deepak Kumar_")
st.markdown("_You can now access Stocks in National Stock Exchange(NSE), Bombay Stock Exchange(BSE), NASDAQ and Crypto-Currencies_")
st.markdown("_Example:_")
st.markdown("_Larsen & Toubro in NSE - 'LT.NS'_")
st.markdown("_Larsen & Toubro in BSE - 'LT.BO'_")
st.markdown("_GOOGLE in NASDAQ - 'GOGGL'_")
st.markdown("_DOGECOIN in India - 'DOGE-INR'_")
st.markdown("_DOGECOIN in US - 'DOGE-USD'_")
st.markdown("_A stock's shares has already been loaded for the user to show how the application works_")
a=st.text_input('Company for Analysis','IDEA.NS')
Stocks_data=yf.download(a,period='6mo')
st.markdown("_A snippet of the data collected_")
st.table(Stocks_data['Adj Close'].head())
Stocks_data['sma_20'] = Stocks_data['Adj Close'].rolling(20).mean()
Stocks_data['sma_50'] = Stocks_data['Adj Close'].rolling(50).mean()
candlestick = go.Candlestick(
x=Stocks_data.index,
open=Stocks_data['Open'],
high=Stocks_data['High'],
low=Stocks_data['Low'],
close=Stocks_data['Close'])
# Create a candlestick figure
fig = go.Figure(data=[candlestick])
fig.add_trace(go.Scatter(x=Stocks_data.index, y=Stocks_data['sma_50'],
mode='lines',
name='sma_50'))
fig.add_trace(go.Scatter(x=Stocks_data.index, y=Stocks_data['sma_20'],
mode='lines',
name='sma_20'))
fig.update_layout(height=600, width=1000,
title_text="Trend Indicator")
st.plotly_chart(fig)
stockstats_df = StockDataFrame.retype(Stocks_data)
fig_rsi = make_subplots(rows=1, cols=2)
fig_rsi.add_trace(
go.Scatter(x=Stocks_data.index, y=Stocks_data['adj close']),
row=1, col=1
)
fig_rsi.add_trace(
go.Scatter(x=Stocks_data.index, y=stockstats_df['rsi_14']),
row=1, col=2
)
fig_rsi.update_layout(height=500, width=1000,title_text="Stock vs RSI")
st.plotly_chart(fig_rsi)
fig_bol = go.Figure(data=[candlestick])
fig_bol.add_trace(go.Scatter(x=Stocks_data.index, y=stockstats_df['boll'],
mode='lines',
name='mid'))
fig_bol.add_trace(go.Scatter(x=Stocks_data.index, y=stockstats_df['boll_ub'],
mode='lines',
name='upper'))
fig_bol.add_trace(go.Scatter(x=Stocks_data.index, y=stockstats_df['boll_lb'],
mode='lines',
name='lower'))
fig_bol.update_layout(height=500, width=1000,title_text="Stock vs Bollinger Bands")
st.plotly_chart(fig_bol)
st.markdown("_Finally the user can Check the Investment value by checking the Risk/Reward ratio_")
st.markdown("_Stocks' markets are NSE:'^NSEI', BSE:'^BSESN', NASDAQ: '^IXIC'_")
st.markdown("_A list of five stocks' shares has already been loaded for the user to show how the application works_")
st.markdown("_Time Period Notation: 'm'='Minute', 'd'='Days', 'mo'='Month'_")
st.markdown("_Stocks' markets are NSE:'^NSEI', BSE:'^BSESN', NASDAQ: '^IXIC'_")
if st.checkbox('Intraday Trading'):
Date=st.selectbox('Select the Period of time',['5m','15m','60m','90m','1d','3d','7d'])
a=st.text_input('1st Company for Comparision','LT.NS')
b=st.text_input('2nd Company for Comparision', 'BHEL.NS')
c=st.text_input('3rd Company for Comparision', 'TCS.NS')
d=st.text_input('4th Company for Comparision','TATASTEEL.NS')
e=st.text_input('5th Company for Comparision','WIPRO.NS')
f=st.text_input('Stocks compared to NSE:^NSEI, BSE:^BSESN, NASDAQ: ^IXIC','^NSEI')
Stocks=[a,b,c,d,e]
Stocks_data=yf.download(Stocks,period=Date,interval='1m')
Nifty=yf.download(f,period=Date,interval='1m')
st.markdown("_A snippet of the data collected_")
st.table(Stocks_data['Adj Close'].head())
Data_Close_price=Stocks_data['Adj Close'].join(Nifty['Adj Close'])
Value=Data_Close_price.pct_change()
Nifty_Value=Nifty['Adj Close'].pct_change()
Nifty_Value=pd.DataFrame(Nifty_Value)
Nifty_Value=Nifty_Value.reset_index()
Nifty_Value.columns=['Datetime','Adj Close']
#st.markdown("_Value of the market_")
import plotly.express as px
fig = px.line(Nifty_Value, x="Datetime", y=Nifty_Value['Adj Close'])
#st.plotly_chart(fig)
stock_returns=Value.drop(['Adj Close'],axis=1)
sp_returns=Value['Adj Close']
excess_returns=stock_returns.sub(sp_returns,axis=0)
avg_excess_return=excess_returns.mean()
#Returns=pd.DataFrame(avg_excess_return)
#st.markdown("_Returns in the Stocks_")
#fig_Return = go.Figure(data=[go.Bar(
#x=Returns.index, y=Returns[0],
#text=Returns.index,
#textposition='auto',
#)])
#st.plotly_chart(fig_Return)
sd_excess_return=excess_returns.std()
#Risk=pd.DataFrame(sd_excess_return)
#st.markdown("_Risk in the Stocks_")
#fig_Risk = go.Figure(data=[go.Bar(
#x=Risk.index, y=Risk[0],
#text=Risk.index,
#textposition='auto',
#)])
#st.plotly_chart(fig_Risk)
daily_sharpe_ratio=avg_excess_return.div(sd_excess_return)
annual_factor=np.sqrt(252)
annual_sharpe_ratio=daily_sharpe_ratio.mul(annual_factor)
Sharpe_ratio=pd.DataFrame(annual_sharpe_ratio)
st.markdown("_Investment Value according to the Risk & Return present in the Stocks_")
# Use textposition='auto' for direct text
fig_Sharpe = go.Figure(data=[go.Bar(
x=Sharpe_ratio.index, y=Sharpe_ratio[0],
text=Sharpe_ratio.index,
textposition='auto',
)])
st.plotly_chart(fig_Sharpe)
#Close_Price=Stocks_data['Adj Close'].iloc[-1,:]
#st.header('Price')
#st.table(Close_Price)
if st.checkbox('Swing and Position Trading'):
Date=st.selectbox('Select the Period of time',['1mo','2mo','3mo','4mo','5mo','6mo','7mo','8mo','9mo','10mo','11mo','12mo'])
a=st.text_input('1st Company for Comparision','SAIL.NS')
b=st.text_input('2nd Company for Comparision', 'ONGC.NS')
c=st.text_input('3rd Company for Comparision', 'BPCL.NS')
d=st.text_input('4th Company for Comparision','TATAMOTORS.NS')
e=st.text_input('5th Company for Comparision','TATAPOWER.NS')
f=st.text_input('Stocks compared to NSE:^NSEI, BSE:^BSESN, NASDAQ: ^IXIC ','^NSEI')
Stocks=[a,b,c,d,e]
Stocks_data=yf.download(Stocks,period=Date)
Nifty=yf.download(f,period=Date)
st.markdown("_A snippet of the data collected_")
st.table(Stocks_data['Adj Close'].head())
Data_Close_price=Stocks_data['Adj Close'].join(Nifty['Adj Close'])
Value=Data_Close_price.pct_change()
Nifty_Value=Nifty['Adj Close'].pct_change()
Nifty_Value=pd.DataFrame(Nifty_Value)
Nifty_Value=Nifty_Value.reset_index()
Nifty_Value.columns=['Datetime','Adj Close']
#st.markdown("_Value of the market_")
import plotly.express as px
fig = px.line(Nifty_Value, x="Datetime", y=Nifty_Value['Adj Close'])
#st.plotly_chart(fig)
stock_returns=Value.drop(['Adj Close'],axis=1)
sp_returns=Value['Adj Close']
excess_returns=stock_returns.sub(sp_returns,axis=0)
avg_excess_return=excess_returns.mean()
#Returns=pd.DataFrame(avg_excess_return)
#st.markdown("_Returns in the Stocks_")
#fig_Return = go.Figure(data=[go.Bar(
#x=Returns.index, y=Returns[0],
#text=Returns.index,
#textposition='auto',
#)])
# st.plotly_chart(fig_Return)
sd_excess_return=excess_returns.std()
#Risk=pd.DataFrame(sd_excess_return)
#st.markdown("_Risk in the Stocks_")
#fig_Risk = go.Figure(data=[go.Bar(
#x=Risk.index, y=Risk[0],
#text=Risk.index,
#textposition='auto',
#)])
#st.plotly_chart(fig_Risk)
daily_sharpe_ratio=avg_excess_return.div(sd_excess_return)
annual_factor=np.sqrt(252)
annual_sharpe_ratio=daily_sharpe_ratio.mul(annual_factor)
Sharpe_ratio=pd.DataFrame(annual_sharpe_ratio)
st.markdown("_Investment Value according to the Risk & Return present in the Stocks_")
# Use textposition='auto' for direct text
fig_Sharpe = go.Figure(data=[go.Bar(
x=Sharpe_ratio.index, y=Sharpe_ratio[0],
text=Sharpe_ratio.index,
textposition='auto',
)])
st.plotly_chart(fig_Sharpe)
#Close_Price=Stocks_data['Adj Close'].iloc[-1,:]
#st.header('Price')
#st.table(Close_Price)