Commit for huggingface space

PortfolioAnalytics.py

@@ -0,0 +1,26 @@
+import yfinance as yf
+import pandas as pd
+from pypfopt.hierarchical_portfolio import HRPOpt
+
+
+def get_data_for_HRP(asset_list, n=10):
+    df = yf.download(' '.join(asset_list), period='5y', interval='1d')['Adj Close']
+    df = df.dropna(axis=1, how='all')
+    df = df.pct_change().dropna()
+    df = df[df.columns[:n]]
+    return df
+
+def get_HRP_weights(asset_list):
+    prices_data = get_data_for_HRP(asset_list)
+
+    hrp = HRPOpt(prices_data)
+    res = hrp.optimize()
+    df = pd.DataFrame({'Stock': [key for key in res.keys()], 'Weights': [round(res[key], 2) for key in res.keys()]})
+    return df, hrp
+
+def get_portfolio_performance(hrp):
+    five_yr_benchmk = yf.download('^NSEI', period='5y', interval='1d')[['Adj Close']]\
+        .pct_change().dropna().mean()['Adj Close']
+    exp_ret, vol, sharpe = hrp.portfolio_performance(five_yr_benchmk, frequency=252)
+    return exp_ret, vol, sharpe
+

Stock_Static_Data.py

@@ -0,0 +1,258 @@
+nse_stock_sectors = {
+    'RELIANCE': 'Energy',
+    'HDFCBANK': 'Financial Services',
+    'INFY': 'IT',
+    'ICICIBANK': 'Financial Services',
+    'TCS': 'IT',
+    'HINDUNILVR': 'Consumer Goods',
+    'KOTAKBANK': 'Financial Services',
+    'HDFC': 'Financial Services',
+    'BAJFINANCE': 'Financial Services',
+    'AXISBANK': 'Financial Services',
+    'ASIANPAINT': 'Consumer Goods',
+    'ITC': 'Consumer Goods',
+    'LT': 'Infrastructure',
+    'HCLTECH': 'IT',
+    'SBI': 'Financial Services',
+    'BAJAJFINSV': 'Financial Services',
+    'TECHM': 'IT',
+    'NTPC': 'Energy',
+    'BANKBARODA': 'Financial Services',
+    'IOC': 'Energy',
+    'NESTLEIND': 'Consumer Goods',
+    'ONGC': 'Energy',
+    'BPCL': 'Energy',
+    'POWERGRID': 'Infrastructure',
+    'SBIN': 'Financial Services',
+    'BHARTIARTL': 'Telecom',
+    'MARUTI': 'Automobile',
+    'ULTRACEMCO': 'Cement',
+    'BAJAJ-AUTO': 'Automobile',
+    'WIPRO': 'IT',
+    'JSWSTEEL': 'Metals',
+    'TATASTEEL': 'Metals',
+    'HINDZINC': 'Metals',
+    'SUNPHARMA': 'Pharmaceuticals',
+    'TATAMOTORS': 'Automobile',
+    'DMART': 'Retail',
+    'BRITANNIA': 'Consumer Goods',
+    'TITAN': 'Consumer Goods',
+    'GAIL': 'Energy',
+    'DRREDDY': 'Pharmaceuticals',
+    'HINDPETRO': 'Energy',
+    'CIPLA': 'Pharmaceuticals',
+    'BHARTIINFR': 'Telecom',
+    'EICHERMOT': 'Automobile',
+    'M&M': 'Automobile',
+    'ULTRACEMC': 'Cement',
+    'INDUSINDBK': 'Financial Services',
+    'HEROMOTOCO': 'Automobile',
+    'SHREECEM': 'Cement',
+    'GRASIM': 'Cement',
+    'ONGCORP': 'Energy',
+    'ADANIPORTS': 'Infrastructure',
+    'BAJAJHLDNG': 'Financial Services',
+    'DIVISLAB': 'Pharmaceuticals',
+    'TATAPOWER': 'Energy',
+    'ZEEL': 'Media',
+    'NTPC_': 'Energy',
+    'ICICIPRULI': 'Financial Services',
+    'MRF': 'Automobile',
+    'MOTHERSUMI': 'Automobile',
+    'COALINDIA': 'Mining',
+    'SBILIFE': 'Financial Services',
+    'PIDILITIND': 'Consumer Goods',
+    'TATACONSUM': 'Consumer Goods',
+    'SBICARD': 'Financial Services',
+    'DABUR': 'Consumer Goods',
+    'PNB': 'Financial Services',
+    'LUPIN': 'Pharmaceuticals',
+    'UBL': 'Consumer Goods',
+    'BHEL': 'Infrastructure',
+    'ACC': 'Cement',
+    'HINDALCO': 'Metals',
+    'TORNTPHARM': 'Pharmaceuticals',
+    'BOSCHLTD': 'Automobile',
+    'DLF': 'Real Estate',
+    'AMBUJACEM': 'Cement',
+    'SAIL': 'Metals',
+    'MUTHOOTFIN': 'Financial Services',
+    'MCDOWELL-N': 'Consumer Goods',
+    'PFC': 'Financial Services',
+    'BEL': 'Defense',
+    'BANDHANBNK': 'Financial Services',
+    'PEL': 'Conglomerate',
+    'TORNTPOWER': 'Energy',
+    'HAVELLS': 'Consumer Goods',
+    'FEDERALBNK': 'Financial Services',
+    'BERGEPAINT': 'Consumer Goods',
+    'RBLBANK': 'Financial Services',
+    'INDIGO': 'Airlines',
+    'RAMCOCEM': 'Cement',
+    'EXIDEIND': 'Automobile',
+    'CHOLAFIN': 'Financial Services',
+    'ICICIGI': 'Financial Services',
+    'BANKINDIA': 'Financial Services',
+    'ADANIGREEN': 'Energy',
+    'HDFCLIFE': 'Financial Services',
+    'APOLLOHOSP': 'Healthcare',
+    'AUROPHARMA': 'Pharmaceuticals',
+    'IGL': 'Energy',
+    'TVSMOTOR': 'Automobile',
+    'GODREJCP': 'Consumer Goods',
+    'MGL': 'Energy',
+    'BATAINDIA': 'Retail',
+    'M&MFIN': 'Financial Services',
+    'NIACL': 'Financial Services',
+    'ADANIENT': 'Conglomerate',
+    'JINDALSTEL': 'Metals',
+    'BANKNIFTY': 'Index',
+    'COLPAL': 'Consumer Goods',
+    'UBL_': 'Consumer Goods',
+    'INDIANB': 'Financial Services',
+    'BANKBAROD': 'Financial Services',
+    'ASHOKLEY': 'Automobile',
+    'SRTRANSFIN': 'Financial Services',
+    'ACC_': 'Cement',
+    'SIEMENS': 'Infrastructure',
+    'HDFCAMC': 'Financial Services',
+    'AMARAJABAT': 'Automobile',
+    'BSE': 'Index',
+    'MGL_': 'Energy',
+    'BAJAJHLDNG_': 'Financial Services',
+    'AMBUJACEM_': 'Cement',
+    'BPCL_': 'Energy',
+    'IDFCFIRSTB': 'Financial Services',
+    'IDEA': 'Telecom',
+    'PFIZER': 'Pharmaceuticals',
+    'BANDHANBNK_': 'Financial Services',
+    'HCLTECH_': 'IT',
+    'MINDTREE': 'IT',
+    'HDFCBANK_': 'Financial Services',
+    'ASHOKLEY_': 'Automobile',
+    'PNBHOUSING': 'Real Estate',
+    'GRASIM_': 'Cement',
+    'M&MFIN_': 'Financial Services',
+    'PVR': 'Entertainment',
+    'RPOWER': 'Energy',
+    'TVTODAY': 'Media',
+    'APLLTD': 'Pharmaceuticals',
+    'IDBI': 'Financial Services',
+    'IRCTC': 'Travel',
+    'JINDALSTEL_': 'Metals',
+    'L&TFH': 'Financial Services',
+    'NIITTECH': 'IT',
+    'INDIGO_': 'Airlines',
+    'BANKINDIA_': 'Financial Services',
+    'MINDACORP': 'Retail',
+    'FEDERALBNK_': 'Financial Services',
+    'GLENMARK': 'Pharmaceuticals',
+    'TV18BRDCST': 'Media',
+    'UJJIVAN': 'Financial Services',
+    'CENTRALBK': 'Financial Services',
+    'NCC': 'Infrastructure',
+    'HDFCLIFE_': 'Financial Services',
+    'SYNGENE': 'Pharmaceuticals',
+    'BALKRISIND': 'Automobile',
+    'CHOLAFIN_': 'Financial Services',
+    'COFORGE': 'IT',
+    'CRISIL': 'Financial Services',
+    'DEEPAKNTR': 'Consumer Goods',
+    'JUBLFOOD': 'Retail',
+    'PHOENIXLTD': 'Real Estate',
+    'EQUITAS': 'Financial Services',
+    'LTI': 'IT',
+    'RBLBANK_': 'Financial Services',
+    'CANBK': 'Financial Services',
+    'MOTILALOFS': 'Financial Services',
+    'PNCINFRA': 'Infrastructure',
+    'SUNTV': 'Media',
+    'AMBER': 'Consumer Goods',
+    'BLUESTARCO': 'Consumer Goods',
+    'CUB': 'Financial Services',
+    'GREENPLY': 'Consumer Goods',
+    'KOLTEPATIL': 'Real Estate',
+    'L&TFH_': 'Financial Services',
+    'METROPOLIS': 'Healthcare',
+    'RCOM': 'Telecom',
+    'SYMPHONY': 'Consumer Goods',
+    'VBL': 'Consumer Goods',
+    'VENKEYS': 'Consumer Goods',
+    'COROMANDEL': 'Fertilizers',
+    'GODREJAGRO': 'Consumer Goods',
+    'HAL': 'Defense',
+    'JINDALSAW': 'Metals',
+    'KEC': 'Infrastructure',
+    'MINDACORP_': 'Retail',
+    'NMDC': 'Metals',
+    'PGHL': 'Consumer Goods',
+    'PRAJIND': 'Infrastructure',
+    'RITES': 'Infrastructure',
+    'BANKBAROD_': 'Financial Services',
+    'BATAINDIA_': 'Retail',
+    'BEML': 'Infrastructure',
+    'CAREERP': 'IT',
+    'CENTURYTEX': 'Cement',
+    'DCBBANK': 'Financial Services',
+    'DHFL': 'Financial Services',
+    'FSL': 'Financial Services',
+    'FUTURECON': 'Retail',
+    'GEPIL': 'Pharmaceuticals',
+    'GESHIP': 'Shipping',
+    'GUJALKALI': 'Fertilizers',
+    'HATHWAY': 'Media',
+    'HERCULES': 'Pharmaceuticals',
+    'HEXAWARE': 'IT',
+    'IDFC': 'Financial Services',
+    'IDFCFIRSTB_': 'Financial Services',
+    'IFCI': 'Financial Services',
+    'IIFL': 'Financial Services',
+    'INDIANB_': 'Financial Services',
+    'IRCON': 'Infrastructure',
+    'JETAIRWAYS': 'Airlines',
+    'LUPIN': 'Pharmaceuticals',
+    'M&M': 'Automobile',
+    'MAHLOG': 'Automobile',
+    'MANAPPURAM': 'Financial Services',
+    'MAXFIN': 'Financial Services',
+    'MCX': 'Financial Services',
+    'MOTHERSUMI': 'Automobile',
+    'NATCOPHARM': 'Pharmaceuticals',
+    'NATIONALUM': 'Metals',
+    'NH': 'Hospitality',
+    'NLCINDIA': 'Power',
+    'NTPC': 'Power',
+    'PCJEWELLER': 'Retail',
+    'PNBHOUSING': 'Financial Services',
+    'PVR': 'Media',
+    'RAYMOND': 'Textiles',
+    'RELCAPITAL': 'Financial Services',
+    'RELIANCE': 'Oil & Gas',
+    'RIIL': 'Infrastructure',
+    'SADBHAV': 'Infrastructure',
+    'SAIL': 'Metals',
+    'SCHAEFFLER': 'Automobile',
+    'SFL': 'Retail',
+    'SOUTHBANK': 'Financial Services',
+    'SPARC': 'Pharmaceuticals',
+    'SUZLON': 'Power',
+    'SYNDIBANK': 'Financial Services',
+    'TATAELXSI': 'IT',
+    'TATAMOTORS': 'Automobile',
+    'TECHM': 'IT',
+    'THERMAX': 'Engineering',
+    'TITAN': 'Retail',
+    'TRIDENT': 'Textiles',
+    'UFO': 'Media',
+    'UPL': 'Chemicals',
+    'VAKRANGEE': 'IT',
+    'WOCKPHARMA': 'Pharmaceuticals',
+    'YESBANK': 'Financial Services'
+}
+
+def get_sector_list():
+    return list(set(nse_stock_sectors.values()))
+
+def get_stock_list_by_sector(sector='IT'):
+    return [key.strip()+'.NS' for key, val in nse_stock_sectors.items() if val == sector]
+

main.py

@@ -2,7 +2,7 @@ import streamlit as st
 from nsetools import Nse
 from nsepy import get_history
 from datetime import date, timedelta
-
+import plotly.express as px
 import CS_pattern_rankings
 from PatternRecognition import *
 from VixAnalysis import *
@@ -15,6 +15,8 @@ import pandas as pd
 import yfinance as yf
 from plotly.subplots import make_subplots
 from NewsAnalyzer import *
+from Stock_Static_Data import *
+from PortfolioAnalytics import *
 
 buy_sell_color_dict = {"Buy": "green", "Sell": "red", "Hold": "yellow"}
 
@@ -27,7 +29,7 @@ del all_stock_names['NAME OF COMPANY']
 st.set_page_config(page_title='Jarvis', page_icon=':money_with_wings:', layout='wide', initial_sidebar_state='auto')
 pd.options.plotting.backend = "plotly"
 st.title('Jarvis')
-stock_tab, mf_tab, intraday_tab = st.tabs(['Stocks', 'Mutual Funds', 'Intraday'])
+stock_tab, portfolio_tab, intraday_tab = st.tabs(['Stocks', 'Portfolio', 'Intraday'])
 
 main_sidebar = st.sidebar
 with main_sidebar:
@@ -219,6 +221,50 @@ with stock_tab:
         st.subheader(f"Live sentiment for {ticker_yf.split('.')[0]}:")
         st.markdown(f"**{sentiment}**")
 
+with portfolio_tab:
+    portfolio_main_container = st.container()
+    with portfolio_main_container:
+        create_portfolio_for_me, diy_portfolio = st.columns(2)
+        with create_portfolio_for_me:
+            sector_list = get_sector_list()
+            sector_list.append('Surprise me!')
+            select_portfolio_sector = st.selectbox(
+                "Choose your sector of preference?",
+                 tuple(sector_list))
+
+            if st.button('Build Portfolio'):
+                with st.spinner(text='Creating your portfolio! Please wait!'):
+                    asset_list = get_stock_list_by_sector(select_portfolio_sector)
+                    portfolio_df, hrp = get_HRP_weights(asset_list)
+                    fig = px.pie(portfolio_df, values='Weights', names='Stock', title=f'Portfolio allocation for'
+                                                                                      f' {select_portfolio_sector} sector')
+                    st.plotly_chart(fig)
+                    exp_ret, vol, sharpe = get_portfolio_performance(hrp)
+                    st.write(f'Expected Returns {exp_ret:.0%}')
+                    st.write(f'Annualized Volatility {vol:.0%}')
+                    st.write(f'Sharpe Ratio {sharpe:.0%}')
+            else:
+                pass
+
+        with diy_portfolio:
+            asset_list_diy = st.multiselect(
+                'Choose stocks you want to add to your portfolio',
+                list(nse_stock_sectors.keys()),
+                list(nse_stock_sectors.keys())[:3])
+            asset_list_diy = [i.strip()+'.NS' for i in asset_list_diy]
+            if st.button('Get Weights'):
+                with st.spinner(text='Creating your portfolio! Please wait!'):
+                    portfolio_df, hrp = get_HRP_weights(asset_list_diy)
+                    fig = px.pie(portfolio_df, values='Weights', names='Stock', title=f'Portfolio allocation for'
+                                                                                      f' selected stocks')
+                    st.plotly_chart(fig)
+                    exp_ret, vol, sharpe = get_portfolio_performance(hrp)
+                    st.write(f'Expected Returns {exp_ret:.0%}')
+                    st.write(f'Annualized Volatility {vol:.0%}')
+                    st.write(f'Sharpe Ratio {sharpe:.0%}')
+            else:
+                pass
+
 with intraday_tab:
 
     stock_selector_container_intraday = st.container()
@@ -308,9 +354,5 @@ with intraday_tab:
 
         time.sleep(5)
 
-with portfolio_tab:
 
-# with mf_tab:
-#     test_tab1, test_tab2 = st.tabs(['tab1', 'tab2'])
-#     with test_tab1:
-#         st.write("Coming soon...")
+