Update app.py

app.py

@@ -73,9 +73,16 @@ def get_options_merge(current_datetime):
 
     DF_options_origin = get_options_DF(current_datetime)
 
+    # To safely compute the Volume_OpenInterest_Ratio in your DataFrame without encountering division by zero or null value errors, you can utilize pandas' div() method combined with appropriate handling for infinite and missing values.
+    # DF_options_origin["Volume_OpenInterest_Ratio"] = (
+    #     DF_options_origin["volume"] / DF_options_origin["openInterest"]
+    # )
     DF_options_origin["Volume_OpenInterest_Ratio"] = (
-        DF_options_origin["volume"] / DF_options_origin["openInterest"]
-    )
+    DF_options_origin["volume"]
+    .div(DF_options_origin["openInterest"])
+    .replace([np.inf, -np.inf], 0)  # Replace infinite values resulting from division by zero
+    .fillna(0)  # Replace NaN values resulting from division by nulls
+)
 
     # Extract ticker from contractSymbol and merge dataframes
     DF_options_origin["Ticker"] = DF_options_origin["contractSymbol"].str.extract(
@@ -116,7 +123,8 @@ def get_options_merge(current_datetime):
     DFtotal = pd.merge(
         DF_options_merged, merged_df, left_on="Ticker", right_index=True, how="left"
     )
-
+    DFtotal["Moneyvolume"] = DFtotal["lastPrice"] * DFtotal["volume"] *100
+    DFtotal["MoneyopenInterest"] = DFtotal["lastPrice"] * DFtotal["openInterest"] * 100
     return DFtotal, tickerlst
 
 
@@ -125,16 +133,62 @@ def get_options_merge(current_datetime):
 
 DF_options, tickerlst = get_options_merge(current_datetime)
 
-
 # Title
 st.title("📊 Unusual Options Activity Dashboard")
 
-st.write(f"Number of avialable tickers: {len(tickerlst)}")
+st.write(f"Number of avialable tickers: **{len(tickerlst)}**")
+
+st.write(f"Number of options contract records: **{len(DF_options)}** with volume of **{round(DF_options['volume'].sum()/1e+6,2)}M** contracts and **{round(DF_options['openInterest'].sum()/1e+6,2)}M** open interest")
+
+
+
+
+if st.sidebar.button("Options Statistics", use_container_width=True):
+    st.header("Statistics of Options Data")
+    st.write(f"Value of Today's volume options contracts: **{round(DF_options["Moneyvolume"].sum()/1e+9,2)}$B**")
+    st.write(f"Value of open interest options contracts: **{round(DF_options["MoneyopenInterest"].sum()/1e+9,2)}$B**")
+
+    st.write(f"Maximum Volume {int( DF_options["volume"].max())} beloing to Ticker **{DF_options.loc[DF_options["volume"].idxmax(), "Ticker"]}** and contractSymbol {DF_options.loc[DF_options["volume"].idxmax(), "contractSymbol"]}")
+    st.write(f"Maximum Open Interest { int(DF_options["openInterest"].max())} beloing to Ticker **{DF_options.loc[DF_options["openInterest"].idxmax(), "Ticker"]}** and contractSymbol {DF_options.loc[DF_options["openInterest"].idxmax(), "contractSymbol"]}")
+    st.write(f"Maximum Implied Volatility { round(DF_options["impliedVolatility"].max(),2)} beloing to Ticker **{DF_options.loc[DF_options["impliedVolatility"].idxmax(), "Ticker"]}**")
+    st.write(f"Maximum Volume/Open Interest Ratio { DF_options["Volume_OpenInterest_Ratio"].max()} beloing to Ticker {DF_options.loc[DF_options["Volume_OpenInterest_Ratio"].idxmax(), "Ticker"]}")
+    st.write(f"Maximum Relative Volume { round(DF_options["Relative Volume"].max(),2)} beloing to Ticker {DF_options.loc[DF_options["Relative Volume"].idxmax(), "Ticker"]}")
+
+
+# plot top 10 volume and open interest; x is Ticker, y is volume and open interest
+    fig = px.bar(
+        DF_options.nlargest(100, "volume"),
+        x="Ticker",
+        y="volume",
+        color="Type",
+        title="Top Options by Volume",
+        color_discrete_map={"CALL": "green", "PUT": "red"},
+    )
+    fig.update_layout(
+        xaxis_title="Ticker",
+        yaxis_title="Volume",
+        autosize=True,
+        height=600,
+    )
+    st.plotly_chart(fig, use_container_width=True)
+    fig = px.bar(
+        DF_options.nlargest(100, "openInterest"),
+        x="Ticker",
+        y="openInterest",
+        color="Type",
+        title="Top Options by Open Interest",
+        color_discrete_map={"CALL": "green", "PUT": "red"},
+    )
+    fig.update_layout(
+        xaxis_title="Ticker",
+        yaxis_title="Open Interest",
+        autosize=True,
+        height=600,
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
 
-st.write(f"Number of options contract records: {len(DF_options)}")
 
-# Display options data
-st.header("Options Data")
 
 # Sidebar
 st.sidebar.header("Controls")
@@ -161,11 +215,11 @@ open_interest_range = st.sidebar.slider(
 vol_oi_ratio_range = st.sidebar.slider(
     "Volume/Open Interest Ratio Range",
     min_value=0.0,
-    max_value=100.0,  # np.nanmax(
-    #     DF_options["Volume_OpenInterest_Ratio"][
-    #         ~np.isinf(DF_options["Volume_OpenInterest_Ratio"])
-    #     ]
-    # ),
+    max_value= np.nanmax(
+        DF_options["Volume_OpenInterest_Ratio"][
+            ~np.isinf(DF_options["Volume_OpenInterest_Ratio"])
+        ]
+    ),
     value=(0.0, 10.0),  # (
     #     0.5,
     #     np.nanmax(
@@ -218,6 +272,9 @@ put_call_oi_range = st.sidebar.slider(
 )
 
 if st.sidebar.button("Filter", use_container_width=True):
+    # Display options data
+    st.header("Filtering Options Data")
+
     # Filter the dataframe with the range and expiry dates
     filtered_df = DF_options[
         (DF_options["volume"] >= volume_range[0])
@@ -230,7 +287,7 @@ if st.sidebar.button("Filter", use_container_width=True):
         & (DF_options["Put_Call_OI_Ratio"] >= put_call_oi_range[0])
         & (DF_options["Put_Call_OI_Ratio"] <= put_call_oi_range[1])
         & (DF_options["Volume_OpenInterest_Ratio"] >= vol_oi_ratio_range[0])
-        # & (DF_options["Volume_OpenInterest_Ratio"] <= vol_oi_ratio_range[1])
+        & (DF_options["Volume_OpenInterest_Ratio"] <= vol_oi_ratio_range[1])
         & (DF_options["expirationDate"].isin(selected_expiry))
     ]
 
@@ -286,7 +343,8 @@ selectedTicker = st.sidebar.selectbox(
 )
 
 if st.sidebar.button("Option Chain Visualization", use_container_width=True):
-    st.write(f"Ticker {selectedTicker}")
+    st.header(f"Options Chain Visualization for Ticker {selectedTicker}")
+    
     # filter DF_options for selectedTicker
     filtered_ticker_df = DF_options[DF_options["Ticker"] == selectedTicker]