Organize financial statements into grouped views

- add Organized/Raw toggle for income statement, balance sheet, and cash flow
- group line items into expandable sections for easier navigation
- add Prism Net Debt row to annual balance sheet using valuation bridge logic
- keep full raw statement tables and CSV download support

1 files changed, 222 insertions, 56 deletions

diff --git a/components/financials.py b/components/financials.py
index bc2a482..83e9a16 100644
--- a/components/financials.py
+++ b/components/financials.py
@@ -1,8 +1,7 @@
-"""Financial statements — Income Statement, Balance Sheet, Cash Flow."""
-import numpy as np
+"""Financial statements — organized and raw views for Income Statement, Balance Sheet, Cash Flow."""
 import pandas as pd
 import streamlit as st
-from services.data_service import get_income_statement, get_balance_sheet, get_cash_flow
+from services.data_service import get_income_statement, get_balance_sheet, get_cash_flow, get_balance_sheet_bridge_items
 from utils.formatters import fmt_large
 
 # Rows where an increase is bad (decline = green, increase = red).
@@ -18,10 +17,11 @@ _INVERSE_ROWS = {
     "interest expense",
     "interest expense non operating",
     "tax provision",
-    "reconciled depreciation",          # non-cash expense; higher = lower reported income
+    "reconciled depreciation",
 
     # ── Balance sheet ─────────────────────────────────────────────────────────
     "net debt",
+    "prism net debt",
     "total debt",
     "long term debt",
     "long term debt and capital lease obligation",
@@ -46,26 +46,139 @@ _INVERSE_ROWS = {
     "tradeand other payables non current",
 
     # ── Cash flow ─────────────────────────────────────────────────────────────
-    # Debt issuance: positive value = new borrowing = bad
     "issuance of debt",
     "long term debt issuance",
     "net long term debt issuance",
     "net short term debt issuance",
     "net issuance payments of debt",
-    # Equity dilution: positive value = new shares issued = bad for holders
     "issuance of capital stock",
     "common stock issuance",
     "net common stock issuance",
-    # Taxes & interest paid: higher outflow = bad
     "income tax paid supplemental data",
     "interest paid supplemental data",
-    # Buybacks shown as negative outflow — more negative = more buybacks = good,
-    # so INVERSE: decline (more negative) → green
     "repurchase of capital stock",
     "common stock payments",
 }
 
 
+INCOME_GROUPS = {
+    "Revenue": [
+        "Total Revenue",
+        "Cost Of Revenue",
+        "Reconciled Cost Of Revenue",
+        "Gross Profit",
+    ],
+    "Operating Expenses": [
+        "Research And Development",
+        "Selling General And Administration",
+        "Operating Expense",
+        "Total Expenses",
+    ],
+    "Profitability": [
+        "Operating Income",
+        "EBIT",
+        "EBITDA",
+        "Normalized EBITDA",
+        "Pretax Income",
+        "Tax Provision",
+        "Net Income",
+    ],
+    "Other": [
+        "Interest Expense",
+        "Interest Expense Non Operating",
+        "Diluted EPS",
+        "Basic EPS",
+    ],
+}
+
+BALANCE_GROUPS = {
+    "Prism Bridge Metrics": [
+        "Prism Net Debt",
+        "Net Debt",
+        "Total Debt",
+        "Current Debt",
+        "Long Term Debt",
+        "Cash And Cash Equivalents",
+        "Cash Cash Equivalents And Short Term Investments",
+        "Cash",
+        "Preferred Stock",
+        "Minority Interest",
+        "Minority Interests",
+    ],
+    "Assets": [
+        "Cash And Cash Equivalents",
+        "Cash Cash Equivalents And Short Term Investments",
+        "Cash",
+        "Short Term Investments",
+        "Receivables",
+        "Inventory",
+        "Current Assets",
+        "Net PPE",
+        "Gross PPE",
+        "Goodwill",
+        "Other Intangible Assets",
+        "Total Assets",
+    ],
+    "Liabilities": [
+        "Accounts Payable",
+        "Payables And Accrued Expenses",
+        "Payables",
+        "Current Liabilities",
+        "Current Debt",
+        "Current Debt And Capital Lease Obligation",
+        "Long Term Debt",
+        "Long Term Debt And Capital Lease Obligation",
+        "Total Debt",
+        "Total Liabilities Net Minority Interest",
+        "Total Non Current Liabilities Net Minority Interest",
+    ],
+    "Equity": [
+        "Common Stock Equity",
+        "Stockholders Equity",
+        "Retained Earnings",
+        "Total Equity Gross Minority Interest",
+        "Preferred Stock",
+        "Minority Interest",
+        "Minority Interests",
+    ],
+}
+
+CASHFLOW_GROUPS = {
+    "Operating Cash Flow": [
+        "Operating Cash Flow",
+        "Net Income From Continuing Operations",
+        "Depreciation And Amortization",
+        "Deferred Tax",
+        "Stock Based Compensation",
+        "Change In Working Capital",
+    ],
+    "Investing Cash Flow": [
+        "Investing Cash Flow",
+        "Capital Expenditure",
+        "Net Business Purchase And Sale",
+        "Purchase Of Investment",
+        "Sale Of Investment",
+    ],
+    "Financing Cash Flow": [
+        "Financing Cash Flow",
+        "Issuance Of Debt",
+        "Repayment Of Debt",
+        "Net Issuance Payments Of Debt",
+        "Issuance Of Capital Stock",
+        "Repurchase Of Capital Stock",
+        "Cash Dividends Paid",
+        "Common Stock Dividend Paid",
+    ],
+    "Free Cash Flow & Returns": [
+        "Free Cash Flow",
+        "Operating Cash Flow",
+        "Capital Expenditure",
+        "Repurchase Of Capital Stock",
+        "Cash Dividends Paid",
+    ],
+}
+
+
 def _is_inverse(label: str) -> bool:
     return label.strip().lower() in _INVERSE_ROWS
 
@@ -79,7 +192,6 @@ def _fmt_cell(value) -> str:
 
 
 def _yoy_raw(current, previous):
-    """Return raw float YoY % change, or None."""
     try:
         c, p = float(current), float(previous)
         if p == 0:
@@ -97,11 +209,6 @@ def _yoy_str(pct) -> str:
 
 
 def _build_statement(df: pd.DataFrame):
-    """
-    Returns (display_df, color_df).
-    display_df: formatted string DataFrame for rendering.
-    color_df:   same shape, cells are 'green', 'red', or '' for styling.
-    """
     df = df.copy()
     df.columns = [str(c)[:10] for c in df.columns]
     cols = list(df.columns)
@@ -116,7 +223,6 @@ def _build_statement(df: pd.DataFrame):
         if i + 1 < len(cols):
             prev_col = cols[i + 1]
             yoy_label = f"YoY {col[:4]}"
-
             raw_yoy = df.apply(lambda row: _yoy_raw(row[col], row[prev_col]), axis=1)
             display[yoy_label] = raw_yoy.apply(_yoy_str)
 
@@ -138,7 +244,7 @@ def _build_statement(df: pd.DataFrame):
 
 def _style(display: pd.DataFrame, colors: pd.DataFrame):
     GREEN_BG = "background-color: rgba(46, 204, 113, 0.18); color: #7ce3a1;"
-    RED_BG   = "background-color: rgba(231, 76,  60,  0.18); color: #ff8a8a;"
+    RED_BG = "background-color: rgba(231, 76, 60, 0.18); color: #ff8a8a;"
 
     def apply_colors(row):
         return [
@@ -151,57 +257,117 @@ def _style(display: pd.DataFrame, colors: pd.DataFrame):
     return display.style.apply(apply_colors, axis=1)
 
 
+def _augment_balance_sheet(df: pd.DataFrame, ticker: str, quarterly: bool) -> pd.DataFrame:
+    if quarterly or df is None or df.empty:
+        return df
+
+    bridge = get_balance_sheet_bridge_items(ticker)
+    prism_net_debt = bridge.get("net_debt")
+    if prism_net_debt is None:
+        return df
+
+    out = df.copy()
+    if "Prism Net Debt" not in out.index:
+        out.loc["Prism Net Debt"] = [float("nan")] * len(out.columns)
+    out.loc["Prism Net Debt", out.columns[0]] = prism_net_debt
+    return out
+
+
+def _pick_existing_rows(df: pd.DataFrame, candidates: list[str]) -> list[str]:
+    return [row for row in candidates if row in df.index]
+
+
+def _render_grouped_statement(df: pd.DataFrame, groups: dict[str, list[str]], empty_msg: str):
+    grouped_rows = []
+    for section, candidates in groups.items():
+        rows = _pick_existing_rows(df, candidates)
+        if not rows:
+            continue
+        grouped_rows.extend(rows)
+        with st.expander(section, expanded=(section == list(groups.keys())[0])):
+            section_df = df.loc[rows]
+            display, colors = _build_statement(section_df)
+            st.dataframe(_style(display, colors), use_container_width=True)
+
+    remaining = [row for row in df.index if row not in grouped_rows]
+    if remaining:
+        with st.expander("Other Reported Line Items", expanded=False):
+            display, colors = _build_statement(df.loc[remaining])
+            st.dataframe(_style(display, colors), use_container_width=True)
+    elif not grouped_rows:
+        st.info(empty_msg)
+
+
+def _render_statement_block(title: str, df: pd.DataFrame, groups: dict[str, list[str]] | None, download_name: str, raw_key: str, empty_msg: str):
+    if df.empty:
+        st.info(empty_msg)
+        return
+
+    view = st.radio(
+        f"{title} view",
+        ["Organized", "Raw"],
+        horizontal=True,
+        key=f"view_{raw_key}",
+        label_visibility="collapsed",
+    )
+
+    if view == "Organized" and groups:
+        _render_grouped_statement(df, groups, empty_msg)
+    else:
+        display, colors = _build_statement(df)
+        st.dataframe(_style(display, colors), use_container_width=True)
+
+    st.download_button(
+        "Download CSV",
+        df.to_csv().encode(),
+        file_name=download_name,
+        mime="text/csv",
+        key=f"dl_{raw_key}",
+    )
+
+
 def render_financials(ticker: str):
     col1, col2 = st.columns([1, 3])
     with col1:
         freq = st.radio("Frequency", ["Annual", "Quarterly"], horizontal=False)
     quarterly = freq == "Quarterly"
 
-    tab_income, tab_balance, tab_cashflow = st.tabs(
-        ["Income Statement", "Balance Sheet", "Cash Flow"]
-    )
+    tab_income, tab_balance, tab_cashflow = st.tabs([
+        "Income Statement", "Balance Sheet", "Cash Flow"
+    ])
 
     with tab_income:
         df = get_income_statement(ticker, quarterly=quarterly)
-        if df.empty:
-            st.info("Income statement data unavailable.")
-        else:
-            display, colors = _build_statement(df)
-            st.dataframe(_style(display, colors), use_container_width=True)
-            st.download_button(
-                "Download CSV",
-                df.to_csv().encode(),
-                file_name=f"{ticker.upper()}_income_{'quarterly' if quarterly else 'annual'}.csv",
-                mime="text/csv",
-                key=f"dl_income_{ticker}_{quarterly}",
-            )
+        _render_statement_block(
+            "Income Statement",
+            df,
+            INCOME_GROUPS,
+            f"{ticker.upper()}_income_{'quarterly' if quarterly else 'annual'}.csv",
+            f"income_{ticker}_{quarterly}",
+            "Income statement data unavailable.",
+        )
 
     with tab_balance:
         df = get_balance_sheet(ticker, quarterly=quarterly)
-        if df.empty:
-            st.info("Balance sheet data unavailable.")
-        else:
-            display, colors = _build_statement(df)
-            st.dataframe(_style(display, colors), use_container_width=True)
-            st.download_button(
-                "Download CSV",
-                df.to_csv().encode(),
-                file_name=f"{ticker.upper()}_balance_{'quarterly' if quarterly else 'annual'}.csv",
-                mime="text/csv",
-                key=f"dl_balance_{ticker}_{quarterly}",
-            )
+        df = _augment_balance_sheet(df, ticker, quarterly)
+        if not quarterly and not df.empty:
+            st.caption("Prism Net Debt is calculated as Total Debt - Cash & Equivalents using the same bridge logic as Valuation.")
+        _render_statement_block(
+            "Balance Sheet",
+            df,
+            BALANCE_GROUPS,
+            f"{ticker.upper()}_balance_{'quarterly' if quarterly else 'annual'}.csv",
+            f"balance_{ticker}_{quarterly}",
+            "Balance sheet data unavailable.",
+        )
 
     with tab_cashflow:
         df = get_cash_flow(ticker, quarterly=quarterly)
-        if df.empty:
-            st.info("Cash flow data unavailable.")
-        else:
-            display, colors = _build_statement(df)
-            st.dataframe(_style(display, colors), use_container_width=True)
-            st.download_button(
-                "Download CSV",
-                df.to_csv().encode(),
-                file_name=f"{ticker.upper()}_cashflow_{'quarterly' if quarterly else 'annual'}.csv",
-                mime="text/csv",
-                key=f"dl_cashflow_{ticker}_{quarterly}",
-            )
+        _render_statement_block(
+            "Cash Flow",
+            df,
+            CASHFLOW_GROUPS,
+            f"{ticker.upper()}_cashflow_{'quarterly' if quarterly else 'annual'}.csv",
+            f"cashflow_{ticker}_{quarterly}",
+            "Cash flow data unavailable.",
+        )