font update

main.py

@@ -6,31 +6,66 @@ import plotly.graph_objects as go
 
 st.set_page_config(layout="wide")
 
-st.markdown("<h1 style='text-align: center;'><u>CapiPort</u></h1>", unsafe_allow_html=True)
-st.markdown("<h5 style='text-align: center; color: gray;'>Your Portfolio Optimisation Tool</h5>", unsafe_allow_html=True)
-st.divider()
-
+with open(r"style/style.css") as css:
+    st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)
+
+st.markdown(
+    "<h1 style='text-align: center;'><u>CapiPort</u></h1>", unsafe_allow_html=True
+)
+
+st.markdown(
+    "<h5 style='text-align: center; color: gray;'>Your Portfolio Optimisation Tool</h5>",
+    unsafe_allow_html=True,
+)
+st.header(
+    "",
+    divider="rainbow",
+)
+
+color = "Quest"
+st.markdown(
+    "<h1 style='text-align: center;'>🔍 Quest for financial excellence begins with meticulous portfolio optimization</u></h1>",
+    unsafe_allow_html=True,
+)
+
+st.header(
+    "",
+    divider="rainbow",
+)
 
 
 list_df = pd.read_csv("Data/Company List.csv")
 
 company_name = list_df["Name"].to_list()
-company_symbol = (list_df["Ticker"] + '.NS').to_list()
+company_symbol = (list_df["Ticker"] + ".NS").to_list()
 
 company_dict = dict()
 
 for CSymbol, CName in zip(company_symbol, company_name):
     company_dict[CName] = CSymbol
 
-com_sel_name = st.multiselect('Select Multiple Companies', company_name, default = None)
+st.markdown(
+    """ 
+    <style>
+        .big-font {
+        font-size:20px;
+        }
+    </style>""",
+    unsafe_allow_html=True,
+)
+
+st.markdown('<p class="big-font">Select Multiple Companies</p>', unsafe_allow_html=True)
+
+com_sel_name = st.multiselect("", company_name, default=None)
+
 com_sel = [company_dict[i] for i in com_sel_name]
 
 num_tick = len(com_sel)
 
 if num_tick > 1:
 
-    com_data = yf.download(com_sel, start="1900-01-01", end="2024-03-08")['Adj Close']
-    com_data.dropna(inplace = True)
+    com_data = yf.download(com_sel, start="1900-01-01", end="2024-03-08")["Adj Close"]
+    com_data.dropna(inplace=True)
 
     com_sel = com_data.columns.to_list()
     com_sel_name.sort()
@@ -50,40 +85,42 @@ if num_tick > 1:
     exp_ret = np.sum((log_return.mean() * rebal_weig) * 247)
 
     ## Calculate the Expected Volatility, Annualize it by * 247.0
-    exp_vol = np.sqrt(
-        np.dot(
-            rebal_weig.T,
-            np.dot(
-                log_return.cov() * 247,
-                rebal_weig
-            )
-        )
-    )
+    exp_vol = np.sqrt(np.dot(rebal_weig.T, np.dot(log_return.cov() * 247, rebal_weig)))
 
     ## Calculate the Sharpe Ratio.
     sharpe_ratio = exp_ret / exp_vol
 
     # Put the weights into a data frame to see them better.
-    weights_df = pd.DataFrame(data={
-        'company_name': com_sel_name,
-        'random_weights': rand_weig,
-        'rebalance_weights': rebal_weig
-    })
+    weights_df = pd.DataFrame(
+        data={
+            "company_name": com_sel_name,
+            "random_weights": rand_weig,
+            "rebalance_weights": rebal_weig,
+        }
+    )
 
     st.divider()
 
-    st.markdown("<h5 style='text-align: center;'>Random Portfolio Weights</h5>", unsafe_allow_html=True)
+    st.markdown(
+        "<h5 style='text-align: center;'>Random Portfolio Weights</h5>",
+        unsafe_allow_html=True,
+    )
     st.dataframe(weights_df, use_container_width=True)
 
-
     # Do the same with the other metrics.
-    metrics_df = pd.DataFrame(data={
-        'Expected Portfolio Returns': exp_ret,
-        'Expected Portfolio Volatility': exp_vol,
-        'Portfolio Sharpe Ratio': sharpe_ratio
-    }, index=[0])
+    metrics_df = pd.DataFrame(
+        data={
+            "Expected Portfolio Returns": exp_ret,
+            "Expected Portfolio Volatility": exp_vol,
+            "Portfolio Sharpe Ratio": sharpe_ratio,
+        },
+        index=[0],
+    )
 
-    st.markdown("<h5 style='text-align: center;'>Random Weights Metrics</h5>", unsafe_allow_html=True)
+    st.markdown(
+        "<h5 style='text-align: center;'>Random Weights Metrics</h5>",
+        unsafe_allow_html=True,
+    )
     st.dataframe(metrics_df, use_container_width=True)
 
     st.divider()
@@ -119,9 +156,7 @@ if num_tick > 1:
         ret_arr[ind] = np.sum((log_return.mean() * weig) * 247)
 
         ## Calculate and Append the Volatility to the Volatitlity Array
-        vol_arr[ind] = np.sqrt(
-            np.dot(weig.T, np.dot(log_return.cov() * 247, weig))
-        )
+        vol_arr[ind] = np.sqrt(np.dot(weig.T, np.dot(log_return.cov() * 247, weig)))
 
         ## Calculate and Append the Sharpe Ratio to Sharpe Ratio Array
         sharpe_arr[ind] = ret_arr[ind] / vol_arr[ind]
@@ -133,42 +168,50 @@ if num_tick > 1:
     sim_df = pd.DataFrame(data=sim_data).T
 
     ## Give the columns in Simulation Data Proper Names
-    sim_df.columns = [
-        'Returns',
-        'Volatility',
-        'Sharpe Ratio',
-        'Portfolio Weights'
-    ]
+    sim_df.columns = ["Returns", "Volatility", "Sharpe Ratio", "Portfolio Weights"]
 
     ## Make sure the Data Types are correct in the Data Frame
     sim_df = sim_df.infer_objects()
 
     # Print out the results.
     st.write("\n\n")
-    st.markdown("<h4 style='text-align: center;'>Simulation Results</h4>", unsafe_allow_html=True)
+    st.markdown(
+        "<h4 style='text-align: center;'>Simulation Results</h4>",
+        unsafe_allow_html=True,
+    )
     st.dataframe(sim_df.head(), use_container_width=True)
 
     # Return the Max Sharpe Ratio from the run.
-    max_sharpe_ratio = sim_df.loc[sim_df['Sharpe Ratio'].idxmax()]
+    max_sharpe_ratio = sim_df.loc[sim_df["Sharpe Ratio"].idxmax()]
 
     # Return the Min Volatility from the run.
-    min_volatility = sim_df.loc[sim_df['Volatility'].idxmin()]
+    min_volatility = sim_df.loc[sim_df["Volatility"].idxmin()]
 
-    max_sharpe_weights_df = pd.DataFrame(data={
-        'company_name': com_sel_name,
-        'random_weights': max_sharpe_ratio["Portfolio Weights"],
-    })
+    max_sharpe_weights_df = pd.DataFrame(
+        data={
+            "company_name": com_sel_name,
+            "random_weights": max_sharpe_ratio["Portfolio Weights"],
+        }
+    )
 
-    st.markdown("<h5 style='text-align: center;'>Portfolio with Max Sharpe Ratio</h5>", unsafe_allow_html=True)
+    st.markdown(
+        "<h5 style='text-align: center;'>Portfolio with Max Sharpe Ratio</h5>",
+        unsafe_allow_html=True,
+    )
     st.dataframe(max_sharpe_ratio, use_container_width=True)
     st.dataframe(max_sharpe_weights_df, use_container_width=True)
 
-    min_volatility_weights_df = pd.DataFrame(data={
-        'company_name': com_sel_name,
-        'random_weights': min_volatility["Portfolio Weights"],
-    })
+    min_volatility_weights_df = pd.DataFrame(
+        data={
+            "company_name": com_sel_name,
+            "random_weights": min_volatility["Portfolio Weights"],
+        }
+    )
 
-    st.markdown("<h5 style='text-align: center;'>Portfolio with Min Volatility</h5>", unsafe_allow_html=True)
+    st.markdown(
+        "<h5 style='text-align: center;'>Portfolio with Min Volatility</h5>",
+        unsafe_allow_html=True,
+    )
     st.dataframe(min_volatility, use_container_width=True)
     st.dataframe(min_volatility_weights_df, use_container_width=True)
 
@@ -176,57 +219,45 @@ if num_tick > 1:
 
     st.markdown("<h1 style='text-align: center;'>Plotting</h1>", unsafe_allow_html=True)
 
-    fig = go.Figure(data=go.Scatter(
-        x=sim_df['Volatility'],
-        y=sim_df['Returns'],
-        mode='markers',
-        marker=dict(
-            color=sim_df['Sharpe Ratio'],
-            colorscale='RdYlBu',
-            size=10
+    fig = go.Figure(
+        data=go.Scatter(
+            x=sim_df["Volatility"],
+            y=sim_df["Returns"],
+            mode="markers",
+            marker=dict(color=sim_df["Sharpe Ratio"], colorscale="RdYlBu", size=10),
         )
-    ))
+    )
 
     # Add color bar
-    fig.update_layout(
-        coloraxis_colorbar=dict(
-            title='Sharpe Ratio'
-        )
-    )
+    fig.update_layout(coloraxis_colorbar=dict(title="Sharpe Ratio"))
 
     # Add title and axis labels
     fig.update_layout(
-        title='Portfolio Returns Vs. Risk',
-        xaxis=dict(title='Standard Deviation / Volatility'),
-        yaxis=dict(title='Returns')
+        title="Portfolio Returns Vs. Risk",
+        xaxis=dict(title="Standard Deviation / Volatility"),
+        yaxis=dict(title="Returns"),
     )
 
     # Plot the Max Sharpe Ratio, using a `Red Star`.
-    fig.add_trace(go.Scatter(
-        x=[max_sharpe_ratio[1]],
-        y=[max_sharpe_ratio[0]],
-        mode='markers',
-        marker=dict(
-            color='red',
-            symbol='star',
-            size=20
-        ),
-        name='Max Sharpe Ratio'
-    ))
+    fig.add_trace(
+        go.Scatter(
+            x=[max_sharpe_ratio[1]],
+            y=[max_sharpe_ratio[0]],
+            mode="markers",
+            marker=dict(color="red", symbol="star", size=20),
+            name="Max Sharpe Ratio",
+        )
+    )
 
     # Plot the Min Volatility, using a `Blue Star`.
-    fig.add_trace(go.Scatter(
-        x=[min_volatility[1]],
-        y=[min_volatility[0]],
-        mode='markers',
-        marker=dict(
-            color='blue',
-            symbol='star',
-            size=20
-        ),
-        name='Min Volatility'
-    ))
+    fig.add_trace(
+        go.Scatter(
+            x=[min_volatility[1]],
+            y=[min_volatility[0]],
+            mode="markers",
+            marker=dict(color="blue", symbol="star", size=20),
+            name="Min Volatility",
+        )
+    )
 
     st.plotly_chart(fig, use_container_width=True)
-
-

style/style.css

@@ -0,0 +1,5 @@
+@import url('https://fonts.googleapis.com/css2?family=League+Spartan:wght@100..900&display=swap');
+
+body * {
+    font-family: "League Spartan";
+}