meeting updates

pages/2_Portfolio_Builder.py

@@ -69,18 +69,14 @@ obb.account.login(pat=os.environ['open_bb_pat'])
 
 if 'tickers' not in st.session_state:
     tickers = [
-        "XLE", "XLF", "XLU", "XLI", "GDX", 
-        "XLK", "XLV", "XLY", "XLP", "XLB", 
-        "XOP", "IYR", "XHB", "ITB", "VNQ", 
-        "GDXJ", "IYE", "OIH", "XME", "XRT", 
-        "SMH", "IBB", "KBE", "KRE", "XTL", 
+        "SPY", "QQQ","BND","GLD","VTI" 
     ]
     st.session_state['tickers'] = tickers
 
 if not platform.processor(): # take inputs from the user for hosted application
     tickers = st.session_state['tickers']
 
-elif re.search('AuthenticAMD', platform.processor()): # use live ibkr portfolio if ran from local machine
+elif not re.search('AuthenticAMD', platform.processor()): # use live ibkr portfolio if ran from local machine
     try:
         # Initialize IB connection here
         open_nested_event_loop()
@@ -132,8 +128,8 @@ with st.form(key="selecting columns"):
         # Step 2: Set portfolio optimization model
         port.assets_stats(model='hist')  # Using historical data for estimation 
         # Step 3: Configure the optimization model and calculate the efficient frontier
-        ef = port.efficient_frontier(model='Classic', rm='MSV', points=50, rf=0.0406, hist=True)
-        w1 = port.optimization(model='Classic', rm='MSV', obj='Sharpe', rf=0.0, hist=True)
+        ef = port.efficient_frontier(model='Classic', rm='MV', points=50, rf=0.0406, hist=True)
+        w1 = port.optimization(model='Classic', rm='MV', obj='Sharpe', rf=0.0, hist=True)
 
         mu = port.mu  # Expected returns
         cov = port.cov  # Covariance matrix
@@ -148,7 +144,7 @@ with st.form(key="selecting columns"):
         # Ensure you're using the adjusted close prices for accurate return calculation
         benchmark_returns = spy_prices['close'].pct_change().dropna()
 
-        port.rf = 0.0406  # Risk-free rate
+        port.rf = 0.000406  # Risk-free rate
         portfolio_return = np.dot(w1, mu)
 
         # market return
@@ -225,12 +221,13 @@ with st.form(key="selecting columns"):
 
         st.pyplot(fig1)
 
-        port = rp.HCPortfolio(returns=returns)
+        port = rp.HCPortfolio(returns=returns)#, w_max=0.3, w_min=0.05)
         w = port.optimization(
             model="HRP",
             codependence="pearson",
-            rm="MV", # minimum variance
-            rf=0.05,
+            obj='Sharpe',
+            rm='vol', # minimum variance
+            rf=0.000406,
             linkage="single",
             max_k=10,
             leaf_order=True,
@@ -273,7 +270,7 @@ with st.form(key="selecting columns"):
         # Ensure you're using the adjusted close prices for accurate return calculation
         benchmark_returns = spy_prices['close'].pct_change().dropna()
 
-        port.rf = 0.0406  # Risk-free rate
+        port.rf = 0.000406  # Risk-free rate
         portfolio_return = np.dot(w, mu)
 
         # market return
@@ -308,3 +305,13 @@ with st.form(key="selecting columns"):
             w=w,
         )
         st.write('Sharpe Ratio: ', np.round(sharpe_ratio, 3))
+
+        # -------------------------- (Report) -------------------------------- #
+        # fig_report = rp.jupyter_report(returns,
+        #                w=w1,
+        #                rm='MV',
+        #                rf=0.0406,
+        #                nrow=25
+        #                )
+
+        # st.pyplot(fig_report)