Update app.py

app.py

@@ -31,71 +31,89 @@ dataset_name_YfOptions_output = os.getenv("dataset_name_YfOptions_output")
 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"
-)
+st.set_page_config(page_title="Option Data Screener App", page_icon="📊", layout="wide")
 
 
 @st.cache_data
 def get_TD_DF(current_datetime):
     # Load lastest TradingView DataSet from HuggingFace Dataset which is always america.csv
     # download_from_hf_dataset("america.csv", "AmirTrader/TradingViewData", HF_TOKEN_YfOptions)
-    DF = load_hf_dataset("america.csv", HF_TOKEN_YfOptions, dataset_name_TradingView_input)
+    DF = load_hf_dataset(
+        "america.csv", HF_TOKEN_YfOptions, dataset_name_TradingView_input
+    )
 
     # get ticker list by filtering only above 1 billion dollar company
     # DF = pd.read_csv(f'america_2024-03-01.csv')
     tickerlst = list(DF.query("`Market Capitalization`>10e9").Ticker)
-    # tickerlist = ['INDO', 'TSLA', 'AAPL', 'MSFT', 'GOOGL', 'AMZN', 'NFLX', 'META', 'NVDA', 'AMD', 'INTC', 'IBM', 'CSCO', 'ORCL', 'QCOM', 'TXN', 'AVGO', 'ADBE', 'CRM', 'NFLX', 'PYPL', 'SNAP']
+
     return DF, tickerlst
 
+
 @st.cache_data
 def get_options_DF(current_datetime):
-    DF = load_hf_dataset("optionchain.csv", HF_TOKEN_YfOptions, dataset_name_YfOptions_output)
+    DF = load_hf_dataset(
+        "optionchain.csv", HF_TOKEN_YfOptions, dataset_name_YfOptions_output
+    )
     return DF
 
+
 @st.cache_data
 def convert_df(df):
-    return df.to_csv().encode('utf-8')
+    return df.to_csv().encode("utf-8")
 
 
 @st.cache_data
 def get_options_merge(current_datetime):
-
     DF, tickerlst = get_TD_DF(current_datetime)
 
     DF_options_origin = get_options_DF(current_datetime)
-    
-    DF_options_origin['Volume_OpenInterest_Ratio'] = DF_options_origin['volume'] / DF_options_origin['openInterest']
 
-    # Extract ticker from contractSymbol and merge dataframes
-    DF_options_origin['Ticker'] = DF_options_origin['contractSymbol'].str.extract(r'([A-Z]+)')
-    TD_interestedColumns = ['Ticker', 'Market Capitalization', 'Relative Volume']
-    DF_options_merged = pd.merge(DF_options_origin, DF[TD_interestedColumns], on='Ticker', how='left')
+    DF_options_origin["Volume_OpenInterest_Ratio"] = (
+        DF_options_origin["volume"] / DF_options_origin["openInterest"]
+    )
 
+    # Extract ticker from contractSymbol and merge dataframes
+    DF_options_origin["Ticker"] = DF_options_origin["contractSymbol"].str.extract(
+        r"([A-Z]+)"
+    )
+    TD_interestedColumns = ["Ticker", "Market Capitalization", "Relative Volume"]
+    DF_options_merged = pd.merge(
+        DF_options_origin, DF[TD_interestedColumns], on="Ticker", how="left"
+    )
 
     # Pivot the DataFrame to separate 'Call' and 'Put' for volume
-    volume_pivot = DF_options_merged.groupby(['Ticker', 'Type'])['volume'].sum().unstack()
-    volume_pivot.columns = ['Call_Volume', 'Put_Volume']
-
+    volume_pivot = (
+        DF_options_merged.groupby(["Ticker", "Type"])["volume"].sum().unstack()
+    )
+    volume_pivot.columns = ["Call_Volume", "Put_Volume"]
 
     # Pivot the DataFrame to separate 'Call' and 'Put' for openInterest
-    openInterest_pivot = DF_options_merged.groupby(['Ticker', 'Type'])['openInterest'].sum().unstack()
-    openInterest_pivot.columns = ['Call_openInterest', 'Put_openInterest']
+    openInterest_pivot = (
+        DF_options_merged.groupby(["Ticker", "Type"])["openInterest"].sum().unstack()
+    )
+    openInterest_pivot.columns = ["Call_openInterest", "Put_openInterest"]
 
     # Merge the volume and open interest DataFrames
-    merged_df = volume_pivot.merge(openInterest_pivot, left_index=True, right_index=True)
+    merged_df = volume_pivot.merge(
+        openInterest_pivot, left_index=True, right_index=True
+    )
 
     # Calculate Put/Call Volume Ratio
-    merged_df['Put_Call_Volume_Ratio'] = merged_df['Put_Volume'] / merged_df['Call_Volume'] #.replace(0, pd.NA)
+    merged_df["Put_Call_Volume_Ratio"] = (
+        merged_df["Put_Volume"] / merged_df["Call_Volume"]
+    )  # .replace(0, pd.NA)
 
     # Calculate Put/Call Open Interest Ratio
-    merged_df['Put_Call_OI_Ratio'] = merged_df['Put_openInterest'] / merged_df['Call_openInterest'] #.replace(0, pd.NA)
+    merged_df["Put_Call_OI_Ratio"] = (
+        merged_df["Put_openInterest"] / merged_df["Call_openInterest"]
+    )  # .replace(0, pd.NA)
+
+    DFtotal = pd.merge(
+        DF_options_merged, merged_df, left_on="Ticker", right_index=True, how="left"
+    )
 
-    DFtotal = pd.merge(DF_options_merged, merged_df, left_on='Ticker', right_index=True, how='left')
+    return DFtotal, tickerlst
 
-    return  DFtotal, tickerlst
 
 DF_options, tickerlst = get_options_merge(current_datetime)
 
@@ -103,9 +121,9 @@ 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)}')
+st.write(f"Number of options contract records: {len(DF_options)}")
 
 # Display options data
 st.header("Options Data")
@@ -118,32 +136,46 @@ st.sidebar.markdown("### Filter Options Data")
 volume_range = st.sidebar.slider(
     "Volume Range",
     min_value=0,
-    max_value=DF_options['volume'].max().astype(int),
-    value=(0, DF_options['volume'].max().astype(int) ),
-    step=100
+    max_value=DF_options["volume"].max().astype(int),
+    value=(0, DF_options["volume"].max().astype(int)),
+    step=100,
 )
 
 open_interest_range = st.sidebar.slider(
-    "Open Interest Range", 
+    "Open Interest Range",
     min_value=0,
-    max_value=DF_options['openInterest'].max().astype(int),
-    value=(0, DF_options['openInterest'].max().astype(int) ),
-    step=100
+    max_value=DF_options["openInterest"].max().astype(int),
+    value=(0, DF_options["openInterest"].max().astype(int)),
+    step=100,
 )
 
 # Add range selector for Volume/Open Interest Ratio
 vol_oi_ratio_range = st.sidebar.slider(
     "Volume/Open Interest Ratio Range",
     min_value=0.0,
-    max_value=np.nanmax(DF_options['Volume_OpenInterest_Ratio'][~np.isinf(DF_options['Volume_OpenInterest_Ratio'])]),
-    value=(0.5, np.nanmax(DF_options['Volume_OpenInterest_Ratio'][~np.isinf(DF_options['Volume_OpenInterest_Ratio'])])/2),
-    step=0.1
+    max_value=100.0, #np.nanmax(
+    #     DF_options["Volume_OpenInterest_Ratio"][
+    #         ~np.isinf(DF_options["Volume_OpenInterest_Ratio"])
+    #     ]
+    # ),
+    value=(0.0,10.0), #(
+    #     0.5,
+    #     np.nanmax(
+    #         DF_options["Volume_OpenInterest_Ratio"][
+    #             ~np.isinf(DF_options["Volume_OpenInterest_Ratio"])
+    #         ]
+    #     )
+    #     / 2,
+    # ),
+    step=0.1,
 )
 
 st.sidebar.markdown("---")  # Add a horizontal line as a visual separator
 
 st.sidebar.markdown("### Filter Stock Data")
-min_relative_volume = st.sidebar.number_input("Minimum Relative Volume", min_value=0.0, value=1.5, step=0.1)
+min_relative_volume = st.sidebar.number_input(
+    "Minimum Relative Volume", min_value=0.0, value=1.5, step=0.1
+)
 
 # Changed to range sliders
 put_call_volume_range = st.sidebar.slider(
@@ -151,84 +183,97 @@ put_call_volume_range = st.sidebar.slider(
     min_value=0.0,
     max_value=10.0,
     value=(0.0, 0.6),
-    step=0.1
+    step=0.1,
 )
 
 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
+    "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"):
+# 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
-    )
+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])
+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',
-    )
-
-    st.write(f"Filtered Tickers: {filtered_df['Ticker'].unique()} ")
+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()} ")
 
 
 st.sidebar.markdown("---")  # Add a horizontal line as a visual separator
 st.sidebar.header("Ticker")
 
-selectedTicker = st.sidebar.selectbox("Select Ticker", options=tickerlst, index=tickerlst.index('AAPL') if 'AAPL' in tickerlst else 0)
+selectedTicker = st.sidebar.selectbox(
+    "Select Ticker",
+    options=tickerlst,
+    index=tickerlst.index("AAPL") if "AAPL" in tickerlst else 0,
+)
 
 if st.sidebar.button("Submit"):
     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')
+    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)
+        autosize=True, width=2560, height=600, margin=dict(l=50, r=50, b=50, t=50)
     )
-    st.plotly_chart(fig) #, use_container_width=True)
+    st.plotly_chart(fig)  # , use_container_width=True)
     # Display the filtered DataFrame
-    
-    
+
 
 st.sidebar.markdown("---")  # Add a horizontal line as a visual separator
 st.sidebar.header("Advanced")
-# **Daily Change in Open Interest**  
+# **Daily Change in Open Interest**
 # Monitoring the increase or decrease in Open Interest compared to the previous day can indicate the inflow (rising OI) or outflow (declining OI) of capital. This factor helps assess the strength of the current trend.
 st.sidebar.button("Daily Change in Open Interest ")
 # contractSymbol