Update app.py

app.py

@@ -17,7 +17,6 @@ from utils import upload_to_hf_dataset, download_from_hf_dataset, load_hf_datase
 # current_datetime = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
 current_datetime = datetime.now().strftime("%Y-%m-%d")
 
-
 # Load environment variables from .env file
 load_dotenv()
 
@@ -33,6 +32,9 @@ HF_TOKEN_YfOptions = os.getenv("HF_TOKEN_YfOptions")
 # Set page configuration
 st.set_page_config(page_title="Option Data Screener App", page_icon="📊", layout="wide")
 
+########################################################################################################
+# Functions
+
 
 @st.cache_data
 def get_TD_DF(current_datetime):
@@ -57,11 +59,14 @@ def get_options_DF(current_datetime):
     return DF
 
 
-@st.cache_data
 def convert_df(df):
     return df.to_csv().encode("utf-8")
 
 
+def convert_df_watchlist(df):
+    return ",".join(map(str, df["Ticker"].unique()))
+
+
 @st.cache_data
 def get_options_merge(current_datetime):
     DF, tickerlst = get_TD_DF(current_datetime)
@@ -115,6 +120,9 @@ def get_options_merge(current_datetime):
     return DFtotal, tickerlst
 
 
+########################################################################################################
+# Main
+
 DF_options, tickerlst = get_options_merge(current_datetime)
 
 
@@ -153,12 +161,12 @@ 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(
+    max_value=100.0,  # np.nanmax(
     #     DF_options["Volume_OpenInterest_Ratio"][
     #         ~np.isinf(DF_options["Volume_OpenInterest_Ratio"])
     #     ]
     # ),
-    value=(0.0,10.0), #(
+    value=(0.0, 10.0),  # (
     #     0.5,
     #     np.nanmax(
     #         DF_options["Volume_OpenInterest_Ratio"][
@@ -190,54 +198,61 @@ put_call_oi_range = st.sidebar.slider(
     "Put/Call OI Ratio Range", min_value=0.0, max_value=10.0, value=(0.0, 3.0), step=0.1
 )
 
-# if st.sidebar.button("Filter"):
-# Filter the dataframe with the range
-filtered_df = DF_options[
-    (DF_options["volume"] >= volume_range[0])
-    & (DF_options["volume"] <= volume_range[1])
-    & (DF_options["openInterest"] >= open_interest_range[0])
-    & (DF_options["openInterest"] <= open_interest_range[1])
-    & (DF_options["Relative Volume"] >= min_relative_volume)
-    & (DF_options["Put_Call_Volume_Ratio"] >= put_call_volume_range[0])
-    & (DF_options["Put_Call_Volume_Ratio"] <= put_call_volume_range[1])
-    & (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])
-]
-
-st.write(f"Filtered records: {len(filtered_df)} rows")
-
-interestedColumns = [
-    "contractSymbol",
-    "volume",
-    "openInterest",
-    "impliedVolatility",
-    "Volume_OpenInterest_Ratio",
-    "Relative Volume",
-    "Put_Call_Volume_Ratio",
-    "Put_Call_OI_Ratio",
-]
-
-selected_columns = st.multiselect(
-    "Select columns to display",
-    options=filtered_df.columns.tolist(),
-    default=interestedColumns,
-)
-
-if selected_columns:
-    st.dataframe(filtered_df[selected_columns])
-
-# Download button for the DataFrame
-csv = convert_df(filtered_df)
-st.download_button(
-    label="Download Options Data as CSV",
-    data=csv,
-    file_name=f"options_data_{current_datetime}.csv",
-    mime="text/csv",
-)
+if st.sidebar.button("Filter"):
+    # Filter the dataframe with the range
+    filtered_df = DF_options[
+        (DF_options["volume"] >= volume_range[0])
+        & (DF_options["volume"] <= volume_range[1])
+        & (DF_options["openInterest"] >= open_interest_range[0])
+        & (DF_options["openInterest"] <= open_interest_range[1])
+        & (DF_options["Relative Volume"] >= min_relative_volume)
+        & (DF_options["Put_Call_Volume_Ratio"] >= put_call_volume_range[0])
+        & (DF_options["Put_Call_Volume_Ratio"] <= put_call_volume_range[1])
+        & (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])
+    ]
+
+    st.write(f"Filtered records: {len(filtered_df)} rows")
+
+    interestedColumns = [
+        "contractSymbol",
+        "volume",
+        "openInterest",
+        "impliedVolatility",
+        "Volume_OpenInterest_Ratio",
+        "Relative Volume",
+        "Put_Call_Volume_Ratio",
+        "Put_Call_OI_Ratio",
+    ]
+
+    # selected_columns = st.sidebar.multiselect(
+    # "Select columns to display",
+    # options=DF_options.columns.tolist(),
+    # default=interestedColumns,
+    # )
+
+    if interestedColumns:
+        st.dataframe(filtered_df[interestedColumns].reset_index(drop=True))
+
+    # Download button for the DataFrame
+    csv = convert_df(filtered_df)
+    st.download_button(
+        label="Download Options Data as CSV",
+        data=csv,
+        file_name=f"options_data_{current_datetime}.csv",
+        mime="text/csv",
+    )
 
-st.write(f"Filtered Tickers: {filtered_df['Ticker'].unique()} ")
+    csv = convert_df_watchlist(filtered_df)
+    st.download_button(
+        label="Download Stock WatchList for TradingView",
+        data=csv,
+        file_name=f"filtered_options_data_{current_datetime}.txt",
+        mime="text/csv",
+    )
+    st.write(f"Unique Filtered Tickers: **{csv}** ")
 
 
 st.sidebar.markdown("---")  # Add a horizontal line as a visual separator
@@ -249,26 +264,169 @@ selectedTicker = st.sidebar.selectbox(
     index=tickerlst.index("AAPL") if "AAPL" in tickerlst else 0,
 )
 
-if st.sidebar.button("Submit"):
+if st.sidebar.button("Option Chain Visualization"):
     st.write(f"Ticker {selectedTicker}")
     # filter DF_options for selectedTicker
     filtered_ticker_df = DF_options[DF_options["Ticker"] == selectedTicker]
-    # plot plotly3D scatter plot for filtered_ticker_df for volume, openInterest and impliedVolatility
-    fig = px.scatter_3d(
-        filtered_ticker_df,
-        x="volume",
-        y="openInterest",
-        z="impliedVolatility",
-        color="Type",
-        title=f"3D Scatter Plot for {selectedTicker}",
-        hover_data=["strike", "lastPrice", "mark", "daysleft", "contractSymbol", "expirationDate"],
-        color_discrete_map={"CALL": "green", "PUT": "red"}
-    )
-    fig.update_layout(
-        autosize=True, width=2560, height=600, margin=dict(l=50, r=50, b=50, t=50)
+
+    # Create two columns for the charts
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        fig_3d = px.scatter_3d(
+            filtered_ticker_df,
+            x="volume",
+            y="openInterest",
+            z="impliedVolatility",
+            color="Type",
+            title=f"3D Scatter Plot for {selectedTicker}",
+            hover_data=[
+                "strike",
+                "lastPrice",
+                "mark",
+                "daysleft",
+                "contractSymbol",
+                "expirationDate",
+            ],
+            color_discrete_map={"CALL": "green", "PUT": "red"},
+        )
+        fig_3d.update_layout(
+            autosize=True, height=600, margin=dict(l=50, r=50, b=50, t=50)
+        )
+        st.plotly_chart(fig_3d, use_container_width=True)
+
+    with col2:
+        # 3D Volatility Surface: Implied Volatility by Strike and Days to Expiration
+        fig_surface = px.scatter_3d(
+            filtered_ticker_df,
+            x="strike",
+            y="daysleft",
+            z="impliedVolatility",
+            color="Type",
+            title=f"Implied Volatility Surface for {selectedTicker}",
+            hover_data=[
+                "volume",
+                "openInterest",
+                "lastPrice",
+                "mark",
+                "contractSymbol",
+            ],
+            color_discrete_map={"CALL": "green", "PUT": "red"},
+        )
+        fig_surface.update_traces(marker=dict(size=5))
+        fig_surface.update_layout(
+            scene=dict(
+                xaxis_title="Strike Price",
+                yaxis_title="Days to Expiration",
+                zaxis_title="Implied Volatility",
+            ),
+            autosize=True,
+            height=600,
+            margin=dict(l=50, r=50, b=50, t=50),
+        )
+        st.plotly_chart(fig_surface, use_container_width=True)
+
+    with col3:
+        # 3D Surface Plot: Option Price vs. Strike Price and Days to Expiration
+        fig_surface2 = px.scatter_3d(
+            filtered_ticker_df,
+            x="strike",
+            y="daysleft",
+            z="lastPrice",
+            color="Type",
+            title=f"Option Price Surface for {selectedTicker}",
+            hover_data=[
+                "volume",
+                "openInterest",
+                "impliedVolatility",
+                "mark",
+                "contractSymbol",
+            ],
+            color_discrete_map={"CALL": "green", "PUT": "red"},
+        )
+        fig_surface2.update_traces(marker=dict(size=5))
+        fig_surface2.update_layout(
+            scene=dict(
+                xaxis_title="Strike Price",
+                yaxis_title="Days to Expiration",
+                zaxis_title="Option Price",
+            ),
+            autosize=True,
+            height=600,
+            margin=dict(l=50, r=50, b=50, t=50),
+        )
+        st.plotly_chart(fig_surface2, use_container_width=True)
+
+        # Display the filtered DataFrame
+    st.dataframe(
+        filtered_ticker_df.query("Ticker ==@selectedTicker")[
+            [
+                "contractSymbol",
+                "daysleft",
+                "Type",
+                "strike",
+                "lastPrice",
+                "volume",
+                "openInterest",
+                "impliedVolatility",
+                "inTheMoney",
+            ]
+        ].reset_index(drop=True),
+        use_container_width=True,
+        hide_index=True,
+        height=600,
     )
-    st.plotly_chart(fig)  # , use_container_width=True)
-    # Display the filtered DataFrame
+
+    # Create two columns for the charts
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        # Bar chart for Volume by Strike Price
+        fig_bar = px.bar(
+            filtered_ticker_df,
+            x="strike",
+            y="volume",
+            color="Type",
+            title=f"Volume by Strike Price for {selectedTicker}",
+            barmode="group",
+            color_discrete_map={"CALL": "green", "PUT": "red"},
+        )
+        fig_bar.update_layout(
+            xaxis_title="Strike Price", yaxis_title="Volume", autosize=True, height=600
+        )
+        st.plotly_chart(fig_bar, use_container_width=True)
+
+    with col2:
+        # Bar chart for Open Interest by Expiration Date
+        fig_bar2 = px.bar(
+            filtered_ticker_df,
+            x="expirationDate",
+            y="openInterest",
+            color="Type",
+            title=f"Open Interest by Expiration Date for {selectedTicker}",
+            barmode="group",
+            color_discrete_map={"CALL": "green", "PUT": "red"},
+        )
+        fig_bar.update_layout(
+            xaxis_title="Expiration Date",
+            yaxis_title="Open Interest",
+            autosize=True,
+            height=600,
+        )
+        st.plotly_chart(fig_bar2, use_container_width=True)
+
+    with col3:
+        # mplied Volatility by Strike Price
+        fig_bar3 = px.bar(
+            filtered_ticker_df,
+            x="strike",
+            y="impliedVolatility",
+            color="Type",
+            title=f"Implied Volatility by Strike Price for {selectedTicker}",
+            barmode="group",
+            color_discrete_map={"CALL": "green", "PUT": "red"},
+        )
+        st.plotly_chart(fig_bar3, use_container_width=True)
 
 
 st.sidebar.markdown("---")  # Add a horizontal line as a visual separator