Spaces:
Runtime error
Runtime error
File size: 4,012 Bytes
696af78 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
import streamlit as st
from nsetools import Nse
from nsepy import get_history
from datetime import date, timedelta
from PatternRecognition import GetPatternForData
from VixAnalysis import *
import yfinance as yf
today = date.today()
nse = Nse()
all_stock_codes = nse.get_stock_codes()
all_stock_names = dict(zip(nse.get_stock_codes().values(),nse.get_stock_codes().keys()))
del all_stock_names['NAME OF COMPANY']
st.set_page_config(page_title='Jarvis', page_icon='N', layout='wide', initial_sidebar_state='auto')
st.title('Jarvis')
stock_tab, mf_tab = st.tabs(['Stocks', 'Mutual Funds'])
with stock_tab:
stock_selector_col, tenure_selector_start_col, tenure_selector_end_col = st.columns(3)
with stock_selector_col:
selected_stock = st.selectbox('Please select a stock for analysis',
list(all_stock_names.keys()),help='Choose stock to analyze')
ticker_yf = all_stock_names[selected_stock]+".NS"
with tenure_selector_start_col:
start_date = st.date_input("Start Date", today-timedelta(40))
with tenure_selector_end_col:
end_date = st.date_input("End Date", today)
if (end_date<start_date):
st.error('Start date should be before End date')
time_delta = end_date-start_date
if time_delta.days < 35:
st.error('Please extend the range of dates. We need atleast 30 days of data for analysis.')
with st.spinner(text=f'Please Wait...Loading data for {selected_stock}'):
tk = yf.Ticker(ticker_yf)
stock_data_df = tk.history(start=start_date, end=end_date, interval="1d").reset_index()
chart_title_col, download_button_col = st.columns(2)
with chart_title_col:
st.subheader(f'OHLC Chart for {selected_stock}')
with download_button_col:
@st.cache
def convert_df(df):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
return df.to_csv().encode('utf-8')
csv = convert_df(stock_data_df)
st.download_button(
label="Download data as CSV",
data=csv,
file_name=f'{selected_stock}.csv',
mime='text/csv',
)
fig_main_cs = go.Figure(data=[go.Candlestick(x=stock_data_df['Date'],
open=stock_data_df['Open'],
high=stock_data_df['High'],
low=stock_data_df['Low'],
close=stock_data_df['Close'])])
st.plotly_chart(fig_main_cs, use_container_width=True)
st.subheader('Candlestick Matched pattern:')
pattern_expander_col, cs_gauge_col = st.columns((1,3))
matched_pattern, tech_df = GetPatternForData(stock_data_df=stock_data_df)
if matched_pattern is not None:
with pattern_expander_col:
with st.expander(matched_pattern):
st.write("Will add description here")
else:
with pattern_expander_col:
with st.expander("No Pattern Matched"):
st.write("It seems there were no pattern formations for last 10 days.")
with st.sidebar:
vix_container = st.container()
with vix_container:
st.subheader("VIX Analysis")
with st.spinner(text='Loading VIX Analysis...'):
vix_df = get_history(symbol="India VIX", start=today-timedelta(30), end=today, index=True)
vix_analysis_df = ProcessVixData(vix_df)
value = vix_analysis_df.Norm_Class[-30:].mean()
st.metric("Current Market Mood", GetMood(value), '{:.2f}'.format(value))
mood_dict = {'Greed': 'Markets are in bullish state. Good time to book profits.',
'Holding Steady': 'Markets are indecisive. its good to hold onto existing portfolio.',
'Fear': "Markets are in bearish state. Good time to pick good stocks for cheap prices."}
with st.expander(GetMood(value)):
st.write(mood_dict[GetMood(value)])
|