Create app.py

app.py

@@ -0,0 +1,187 @@
+import streamlit as st
+import requests
+import pandas as pd
+import altair as alt
+import datetime
+import re
+
+st.set_page_config(page_title="Congress Trades", layout="wide")
+
+API_KEY = "b431ec171262073909ebf8c0c4afba71"
+SENATE_BASE_URL = "https://financialmodelingprep.com/api/v4/senate-trading-rss-feed"
+HOUSE_BASE_URL = "https://financialmodelingprep.com/api/v4/senate-disclosure-rss-feed"
+
+def fetch_data(base_url, pages=5):
+    data = []
+    for page in range(pages):
+        url = f"{base_url}?page={page}&apikey={API_KEY}"
+        r = requests.get(url)
+        if r.status_code == 200:
+            data.extend(r.json())
+    return data
+
+def parse_amount_range(amount_str):
+    if not isinstance(amount_str, str):
+        return None
+    clean_str = amount_str.replace("$", "").replace(",", "")
+    if " - " in clean_str:
+        low, high = clean_str.split(" - ")
+        try:
+            return (int(low) + int(high)) / 2
+        except ValueError:
+            return None
+    match = re.match(r"\d+", clean_str)
+    return float(match.group()) if match else None
+
+def load_data(base_url):
+    raw_data = fetch_data(base_url, pages=5)
+    if not raw_data:
+        return pd.DataFrame()
+    df = pd.DataFrame(raw_data)
+    if "transactionDate" in df.columns:
+        df["transactionDate"] = pd.to_datetime(df["transactionDate"], errors="coerce")
+        df.sort_values(by="transactionDate", ascending=False, inplace=True)
+    return df
+
+st.sidebar.title("Filters")
+start_date = st.sidebar.date_input("Start transaction date", value=datetime.date(2025, 1, 1))
+top_n = st.sidebar.slider("Top N stocks", min_value=1, max_value=20, value=5)
+run_button = st.sidebar.button("Run")
+
+st.title("Congress Trades Dashboard")
+st.write("Analyze the latest trades reported by members of Congress.")
+
+if run_button:
+    senate_data = load_data(SENATE_BASE_URL)
+    house_data = load_data(HOUSE_BASE_URL)
+
+    if not senate_data.empty:
+        senate_data = senate_data[senate_data["transactionDate"] >= pd.to_datetime(start_date)]
+    if not house_data.empty:
+        house_data = house_data[house_data["transactionDate"] >= pd.to_datetime(start_date)]
+
+    # Prepare Senate
+    senate_chart_data = pd.DataFrame()
+    if not senate_data.empty:
+        senate_chart_data = pd.DataFrame({
+            "ticker": senate_data["symbol"],
+            "rawType": senate_data["type"].str.lower(),
+            "amount": senate_data["amount"].apply(parse_amount_range),
+            "chamber": "Senate"
+        })
+
+    # Prepare House
+    house_chart_data = pd.DataFrame()
+    if not house_data.empty:
+        house_chart_data = pd.DataFrame({
+            "ticker": house_data["ticker"],
+            "rawType": house_data["type"].str.lower(),
+            "amount": house_data["amount"].apply(parse_amount_range),
+            "chamber": "House"
+        })
+
+    combined_data = pd.concat([senate_chart_data, house_chart_data], ignore_index=True)
+    combined_data.dropna(subset=["amount", "ticker"], inplace=True)
+    combined_data = combined_data[combined_data["amount"] > 0]
+
+    # Convert types to "purchase" or "sale"
+    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
+
+    # Get top N by sum
+    sum_per_ticker = (
+        combined_data
+        .groupby("ticker", as_index=False)["amount"]
+        .sum()
+        .sort_values("amount", ascending=False)
+        .head(top_n)
+    )
+    top_tickers = sum_per_ticker["ticker"].unique()
+    filtered_data = combined_data[combined_data["ticker"].isin(top_tickers)]
+
+    if filtered_data.empty:
+        st.write("No data available for the selected filters.")
+    else:
+        chart_data = (
+            filtered_data
+            .groupby(["ticker", "chamber", "tradeType"], as_index=False)
+            .agg({"amount": "sum", "count": "sum"})
+        )
+
+        base = alt.Chart(chart_data).encode(
+            x=alt.X("ticker:N", axis=alt.Axis(labelAngle=-45)),
+            xOffset="chamber:N",
+            y=alt.Y("amount:Q", title="Total Amount", stack="zero"),
+            color=alt.Color("tradeType:N", scale=alt.Scale(domain=["purchase", "sale"], range=["green", "red"]))
+        )
+        bars = base.mark_bar()
+        text = base.mark_text(dy=-5, color="black").encode(text=alt.Text("count:Q"))
+        chart = alt.layer(bars, text).properties(width=40 * len(top_tickers), height=400)
+
+        st.altair_chart(chart, use_container_width=True)
+
+    # Reorder columns for Senate
+    if not senate_data.empty:
+        # The order you specified:
+        # 1) name
+        # 2) disclosure/received date
+        # 3) symbol
+        # 4) purchase/sale
+        # 5) amount
+        # 6) assetDescription
+        # We'll map "office" -> name, "dateRecieved" -> date, "symbol" -> symbol, 
+        # "type" -> purchase/sale, "amount" -> amount, "assetDescription" -> assetDescription
+        # Then we append remaining columns
+        desired_order_senate = [
+            "office",  # name
+            "dateRecieved",  
+            "symbol",        # symbol
+            "type",          # purchase/sale
+            "amount",        # amount
+            "assetDescription"
+        ]
+        # Create an ordered list of columns that exist
+        existing_senate_cols = [c for c in desired_order_senate if c in senate_data.columns]
+        # Append the rest that we didn't list
+        remaining_senate_cols = [c for c in senate_data.columns if c not in existing_senate_cols]
+        reordered_senate_cols = existing_senate_cols + remaining_senate_cols
+        senate_data = senate_data[reordered_senate_cols]
+
+    # Reorder columns for House
+    if not house_data.empty:
+        desired_order_house = [
+            "representative",  # name
+            "disclosureDate",  
+            "ticker",          # symbol
+            "type",            # purchase/sale
+            "amount",          # amount
+            "assetDescription"
+        ]
+        existing_house_cols = [c for c in desired_order_house if c in house_data.columns]
+        remaining_house_cols = [c for c in house_data.columns if c not in existing_house_cols]
+        reordered_house_cols = existing_house_cols + remaining_house_cols
+        house_data = house_data[reordered_house_cols]
+
+    st.subheader("Senate Data")
+    st.dataframe(senate_data, use_container_width=True)
+
+    st.subheader("House Data")
+    st.dataframe(house_data, use_container_width=True)
+
+else:
+    st.write("Set filters and press Run to load data.")
+
+
+hide_streamlit_style = """
+<style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)