Update app.py

app.py

@@ -9,7 +9,7 @@ import plotly.graph_objects as go
 
 from dateutil.relativedelta import relativedelta
 
-st.set_page_config(page_title="Congress Trading Activity", layout="wide")
+st.set_page_config(page_title="Congress Stock Trades", layout="wide")
 
 API_KEY = os.getenv("FMP_API_KEY")
 
@@ -85,6 +85,12 @@ run_button = st.sidebar.button("Run Analysis")
 st.title("Congress Trades Analysis")
 st.write("Analyze the latest trades reported by members of Congress. From the Senate and from the House.")
 
+# Function to standardize trade type
+def standardize_trade_type(t):
+    if "sale" in t or "sold" in t or "sell" in t:
+        return "sale"
+    return "purchase"
+
 if run_button:
     # Use asynchronous fetching for both Senate and House
     senate_data = load_data_async(SENATE_BASE_URL, pages=5)
@@ -107,6 +113,69 @@ if run_button:
             (house_data["disclosureDate"] >= pd.to_datetime(start_date))
         ]
 
+    # ---------------------------
+    # Time Series Chart Data Preparation
+    # ---------------------------
+    # Prepare Senate time series data
+    if not senate_data.empty:
+        senate_time = senate_data.copy()
+        senate_time["trade_date"] = senate_time["dateRecieved"]
+        senate_time["tradeType"] = senate_time["type"].str.lower().apply(standardize_trade_type)
+        senate_time["transactionVolume"] = 1
+        senate_time["parsed_amount"] = senate_time["amount"].apply(parse_amount_range)
+    else:
+        senate_time = pd.DataFrame()
+
+    # Prepare House time series data
+    if not house_data.empty:
+        house_time = house_data.copy()
+        house_time["trade_date"] = house_time["disclosureDate"]
+        house_time["tradeType"] = house_time["type"].str.lower().apply(standardize_trade_type)
+        house_time["transactionVolume"] = 1
+        house_time["parsed_amount"] = house_time["amount"].apply(parse_amount_range)
+    else:
+        house_time = pd.DataFrame()
+
+    time_series_data = pd.concat([senate_time, house_time], ignore_index=True)
+    time_series_data = time_series_data.dropna(subset=["trade_date", "transactionVolume", "parsed_amount"])
+
+    time_grouped = (
+        time_series_data
+        .groupby(["trade_date", "tradeType"], as_index=False)
+        .agg(total_transactions=("transactionVolume", "sum"),
+             avg_amount=("parsed_amount", "mean"))
+    )
+    time_grouped.sort_values("trade_date", inplace=True)
+
+    # Create Plotly time series bar chart
+    fig_time = go.Figure()
+    for trade in ["purchase", "sale"]:
+        df_subset = time_grouped[time_grouped["tradeType"] == trade]
+        if not df_subset.empty:
+            fig_time.add_trace(go.Bar(
+                x=df_subset["trade_date"],
+                y=df_subset["total_transactions"],
+                name=trade,
+                customdata=df_subset["avg_amount"],
+                hovertemplate="Date: %{x|%Y-%m-%d}<br>Total Transactions: %{y}<br>Avg Amount: %{customdata:.2f}<extra></extra>"
+            ))
+    fig_time.update_layout(
+        barmode="group",
+        xaxis_title="Date",
+        yaxis_title="Total Transaction Count",
+        title="Time Series of Trade Volume",
+        height=400
+    )
+
+    # ---------------------------
+    # Display Time Series Chart in Container
+    # ---------------------------
+    with st.container(border=True):
+        st.plotly_chart(fig_time, use_container_width=True)
+
+    # ---------------------------
+    # Chart: Total Amount per Ticker
+    # ---------------------------
     # Prepare chart data for Senate
     senate_chart_data = pd.DataFrame()
     if not senate_data.empty:
@@ -130,11 +199,6 @@ if run_button:
     combined_data.dropna(subset=["amount", "ticker"], inplace=True)
     combined_data = combined_data[combined_data["amount"] > 0]
 
-    # Standardize trade type
-    def standardize_trade_type(t):
-        if "sale" in t or "sold" in t or "sell" in t:
-            return "sale"
-        return "purchase"
     combined_data["tradeType"] = combined_data["rawType"].apply(standardize_trade_type)
     combined_data["count"] = 1
 
@@ -157,7 +221,6 @@ if run_button:
             .groupby(["ticker", "chamber", "tradeType"], as_index=False)
             .agg({"amount": "sum", "count": "sum"})
         )
-        # Create Plotly figure
         fig = go.Figure()
         for chamber in chart_data["chamber"].unique():
             for trade in ["purchase", "sale"]:
@@ -176,21 +239,25 @@ if run_button:
         fig.update_layout(
             barmode="stack",
             xaxis_tickangle=-45,
+            xaxis_title="Ticker",
+            yaxis_title="Total Amount",
+            title="Total Amount per Ticker",
             width=40 * len(top_tickers),
-            height=400,
-            title="Total Amount per Ticker"
+            height=400
         )
         with st.container(border=True):
             st.plotly_chart(fig, use_container_width=True)
 
-    # Reorder Senate columns
+    # ---------------------------
+    # Reorder and Display Senate Table
+    # ---------------------------
     if not senate_data.empty:
         desired_order_senate = [
-            "office",  
-            "dateRecieved",  
-            "symbol",        
-            "type",          
-            "amount",        
+            "office",
+            "dateRecieved",
+            "symbol",
+            "type",
+            "amount",
             "assetDescription"
         ]
         existing_senate_cols = [c for c in desired_order_senate if c in senate_data.columns]
@@ -198,14 +265,21 @@ if run_button:
         reordered_senate_cols = existing_senate_cols + remaining_senate_cols
         senate_data = senate_data[reordered_senate_cols]
 
-    # Reorder House columns
+    with st.container(border=True):
+        st.subheader("Senate Data")
+        st.write("Latest Transaction in Senate. Please sort by **`disclosureDate`** and/or **`dateRecieved`**.")
+        st.dataframe(senate_data, use_container_width=True)
+
+    # ---------------------------
+    # Reorder and Display House Table
+    # ---------------------------
     if not house_data.empty:
         desired_order_house = [
-            "representative",  
-            "disclosureDate",  
-            "ticker",          
-            "type",            
-            "amount",          
+            "representative",
+            "disclosureDate",
+            "ticker",
+            "type",
+            "amount",
             "assetDescription"
         ]
         existing_house_cols = [c for c in desired_order_house if c in house_data.columns]
@@ -213,14 +287,9 @@ if run_button:
         reordered_house_cols = existing_house_cols + remaining_house_cols
         house_data = house_data[reordered_house_cols]
 
-    with st.container(border=True):
-        st.subheader("Senate Data")
-        st.write("Latest Transaction in Senate. Please sort the table by **`disclosureDate`** and/or **`dateRecieved`** columns.")
-        st.dataframe(senate_data, use_container_width=True)
-
     with st.container(border=True):
         st.subheader("House Data")
-        st.write("Latest Transaction in House. Please sort the table by **`disclosureDate`** and/or **`transactionDate`** columns.")
+        st.write("Latest Transaction in House. Please sort by **`disclosureDate`** and/or **`transactionDate`**.")
         st.dataframe(house_data, use_container_width=True)
 else:
     st.write("Set filters and press Run to load data.")