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All-in-one-Financial-Analysis/atlas-terminal/server/services/financial_metrics_ext.py
T
shawnkim1997andClaude Opus 4.6 b2acda81ee feat: add Atlas Terminal — Next.js 14 + FastAPI full-stack migration 
Complete migration from Streamlit to Next.js 14 App Router + FastAPI backend.

Frontend (Next.js 14):
- 10 pages: Overview, Research, Valuation, Technical, Markets, Earnings, News, Portfolio, Filings, Settings
- Terminal Noir dark theme with custom Tailwind config
- TradingView Lightweight Charts for candlestick/volume
- Valuation: DCF, Sensitivity Matrix, Monte Carlo, Tornado, Reverse DCF
- Financial Statements table with YoY growth badges and margin rows
- SEC EDGAR inline filing viewer with section tabs
- News split-view with iframe article embedding
- Technical Analysis with RSI, MACD, Bollinger, Fibonacci, Moving Averages
- Earnings beat/miss visualization
- AI Copilot chat panel with Gemini integration

Backend (FastAPI):
- 13 routers: market_data, financials, valuation, technical, earnings, insider, edgar, news, portfolio, analysis, chat, estimates, fx
- Services: DCF engine, Monte Carlo simulation, sensitivity analysis, risk metrics, SEC parser, technical indicators
- yfinance + yahooquery data sources with fallback pattern
- SQLite caching layer

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-21 02:10:10 +00:00

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"""Extended financial metrics: Piotroski F-Score, Sankey, radar, sector-specific, quarterly.
Complements :mod:`server.services.financial_metrics` with scoring models,
income-statement flow data, and quarterly momentum indicators.
"""
from typing import Any, Dict, List, Optional
import pandas as pd
from server.utils.safe_float import _safe_float
from server.services.market_fetcher import (
_get_annual_financials_balance_cashflow,
_get_row_series,
)
from server.services.financial_metrics import (
_radar_norm,
get_dupont_altman_redflags_yoy,
)
try:
import yfinance as yf
except ImportError:
yf = None # type: ignore[assignment]
# ---------------------------------------------------------------------------
# Income Statement Sankey
# ---------------------------------------------------------------------------
def get_income_statement_sankey_data(ticker: str) -> Dict[str, float]:
"""Revenue -> COGS -> Gross Profit -> OpEx -> OpIncome -> Net Income."""
out: Dict[str, float] = {"revenue": 0, "cogs": 0, "gross_profit": 0, "opex": 0, "operating_income": 0, "tax_interest_other": 0, "net_income": 0}
fin, _, _ = _get_annual_financials_balance_cashflow(ticker)
if fin is None or fin.empty:
return out
try:
rev = _get_row_series(fin, "Total Revenue", "Revenue", "Net Revenue")
cogs = _get_row_series(fin, "Cost Of Revenue", "Cost Of Goods Sold")
gross = _get_row_series(fin, "Gross Profit")
op_inc = _get_row_series(fin, "Operating Income", "EBIT")
ni = _get_row_series(fin, "Net Income", "Net Income Common Stockholders")
if rev is None or len(rev) == 0:
return out
d = rev.index[0]
revenue = abs(_safe_float(rev.get(d)) or 0)
cogs_val = abs(_safe_float(cogs.get(d)) if cogs is not None and d in cogs.index else 0) or 0
gross_val = _safe_float(gross.get(d)) if gross is not None and d in gross.index else None
if gross_val is None:
gross_val = (revenue - cogs_val) if revenue and cogs_val is not None else revenue
gross_val = abs(gross_val) if gross_val is not None else 0
op_inc_val = _safe_float(op_inc.get(d)) if op_inc is not None and d in op_inc.index else 0
ni_val = _safe_float(ni.get(d)) if ni is not None and d in ni.index else 0
opex_val = max(0, gross_val - op_inc_val) if gross_val >= op_inc_val else 0
tax_interest_other = max(0, op_inc_val - ni_val) if (op_inc_val - ni_val) > 0 else abs(min(0, op_inc_val - ni_val))
return {"revenue": max(revenue, 1), "cogs": min(cogs_val, revenue - 1e-6), "gross_profit": gross_val,
"opex": opex_val, "operating_income": op_inc_val, "tax_interest_other": tax_interest_other, "net_income": ni_val}
except Exception:
return out
def sankey_data_from_ai(ai_dict: Dict[str, Any]) -> Dict[str, float]:
"""Build Sankey input from ``get_sec_financials_llm`` result."""
cur = (ai_dict or {}).get("current_yr") or {}
revenue = max(0, (cur.get("Revenue") or 0))
cogs = max(0, min(cur.get("CostOfRevenue") or 0, revenue - 1e-6))
gross_profit = revenue - cogs
opex = max(0, cur.get("OperatingExpenses") or 0)
operating_income = gross_profit - opex
net_income = cur.get("NetIncome") or 0
tax_interest_other = max(0, operating_income - net_income) if operating_income > net_income else abs(min(0, operating_income - net_income))
return {"revenue": max(revenue, 1), "cogs": cogs, "gross_profit": gross_profit, "opex": opex,
"operating_income": operating_income, "tax_interest_other": tax_interest_other, "net_income": net_income}
# ---------------------------------------------------------------------------
# Piotroski F-Score
# ---------------------------------------------------------------------------
def piotroski_from_ai(ai_dict: Dict[str, Any]) -> Dict[str, Any]:
"""Piotroski F-Score (0-9) from AI-extracted current/previous year."""
out: Dict[str, Any] = {"score": 0, "criteria": [], "used_ttm": True}
cur = (ai_dict or {}).get("current_yr") or {}
prev = (ai_dict or {}).get("previous_yr") or {}
if not cur:
return out
def v(d: dict, k: str) -> float:
return d.get(k) or 0
ni0, ni1 = v(cur, "NetIncome"), v(prev, "NetIncome")
ocf0 = v(cur, "OperatingCashFlow")
ta0, ta1 = v(cur, "TotalAssets"), v(prev, "TotalAssets")
roa0 = (ni0 / ta0 * 100) if ta0 and ta0 != 0 else None
roa1 = (ni1 / ta1 * 100) if ta1 and ta1 != 0 else None
lt0, lt1 = v(cur, "LongTermDebt"), v(prev, "LongTermDebt")
ca0, cl0 = v(cur, "CurrentAssets"), v(cur, "CurrentLiabilities")
ca1, cl1 = v(prev, "CurrentAssets"), v(prev, "CurrentLiabilities")
cr0 = (ca0 / cl0) if cl0 and cl0 != 0 else None
cr1 = (ca1 / cl1) if cl1 and cl1 != 0 else None
sh0, sh1 = v(cur, "SharesOutstanding"), v(prev, "SharesOutstanding")
rev0, rev1 = v(cur, "Revenue"), v(prev, "Revenue")
gm0 = ((rev0 - v(cur, "CostOfRevenue")) / rev0 * 100) if rev0 and rev0 != 0 else None
gm1 = ((rev1 - v(prev, "CostOfRevenue")) / rev1 * 100) if rev1 and rev1 != 0 else None
at0 = (rev0 / ta0) if rev0 and ta0 and ta0 != 0 else None
at1 = (rev1 / ta1) if rev1 and ta1 and ta1 != 0 else None
criteria: List[tuple] = [
("Net Income > 0 (profitability)", ni0 > 0),
("Operating Cash Flow > 0 (cash generative)", ocf0 > 0),
("ROA increased vs prior period (improving returns)", roa0 is not None and roa1 is not None and roa0 > roa1),
("OCF > Net Income (earnings quality, less accruals)", ocf0 > ni0),
("Leverage decreased: LT Debt/Assets lower (less debt)", ta0 and ta1 and (lt0 / ta0) < (lt1 / ta1) if ta0 and ta1 else False),
("Current Ratio improved (better liquidity)", cr0 is not None and cr1 is not None and cr0 > cr1),
("No dilution: shares unchanged or lower (no equity raise)", (sh0 <= sh1) if (sh0 and sh1) else True),
("Gross Margin improved (pricing power)", gm0 is not None and gm1 is not None and gm0 > gm1),
("Asset Turnover improved (efficiency)", at0 is not None and at1 is not None and at0 > at1),
]
out["score"] = sum(1 for _, p in criteria if p)
out["criteria"] = criteria
return out
def get_piotroski_fscore(ticker: str) -> Dict[str, Any]:
"""Piotroski F-Score from yahooquery/yfinance data."""
out: Dict[str, Any] = {"score": 0, "criteria": [], "used_ttm": False}
fin, bal, cf = _get_annual_financials_balance_cashflow(ticker)
if fin is None or fin.empty or bal is None or bal.empty:
return out
if cf is None or cf.empty:
cf = pd.DataFrame()
try:
ncol = min(2, len(fin.columns))
rev = _get_row_series(fin, "Total Revenue", "Revenue")
ni = _get_row_series(fin, "Net Income", "Net Income Common Stockholders")
gross = _get_row_series(fin, "Gross Profit")
ta = _get_row_series(bal, "Total Assets")
lt_debt = _get_row_series(bal, "Long Term Debt")
ca = _get_row_series(bal, "Current Assets")
cl = _get_row_series(bal, "Current Liabilities")
ocf = _get_row_series(cf, "Operating Cash Flow", "Cash From Operating Activities") if not cf.empty else None
shares = _get_row_series(bal, "Share Issued") or _get_row_series(bal, "Ordinary Shares Number")
if shares is None and yf:
ti = yf.Ticker(ticker.upper())
info = getattr(ti, "info", None) or {}
sh_info = info.get("sharesOutstanding") or info.get("Shares Outstanding")
if sh_info is not None:
try:
shares = pd.Series([float(sh_info)] * ncol, index=fin.columns[:ncol])
except (TypeError, ValueError):
pass
def v0(s: Optional[pd.Series]) -> Optional[float]:
if s is None or len(s) == 0:
return None
x = _safe_float(s.iloc[0])
return x if (x is not None and x == x and not pd.isna(x)) else None
def v1(s: Optional[pd.Series]) -> Optional[float]:
if s is None or len(s) < 2:
return None
x = _safe_float(s.iloc[1])
return x if (x is not None and x == x and not pd.isna(x)) else None
ni0, ni1 = v0(ni), v1(ni)
ocf0 = v0(ocf) if ocf is not None else None
ta0, ta1 = v0(ta), v1(ta)
roa0 = (ni0 / ta0 * 100) if (ni0 is not None and ta0 and ta0 != 0) else None
roa1 = (ni1 / ta1 * 100) if (ni1 is not None and ta1 and ta1 != 0) else None
lt0 = v0(lt_debt) or 0
lt1 = v1(lt_debt) or 0
cl0, cl1 = v0(cl), v1(cl)
ca0, ca1 = v0(ca), v1(ca)
cr0 = (ca0 / cl0) if (ca0 is not None and cl0 and cl0 != 0) else None
cr1 = (ca1 / cl1) if (ca1 is not None and cl1 and cl1 != 0) else None
sh0, sh1 = v0(shares), v1(shares)
rev0, rev1 = v0(rev), v1(rev)
gm0 = (v0(gross) / rev0 * 100) if (gross is not None and rev0 and rev0 != 0) else None
gm1 = (v1(gross) / rev1 * 100) if (gross is not None and rev1 and rev1 != 0) else None
at0 = (rev0 / ta0) if (rev0 and ta0 and ta0 != 0) else None
at1 = (rev1 / ta1) if (rev1 and ta1 and ta1 != 0) else None
criteria = [
("Net Income > 0 (profitability)", ni0 is not None and ni0 > 0),
("Operating Cash Flow > 0 (cash generative)", ocf0 is not None and ocf0 > 0),
("ROA increased vs prior period (improving returns)", roa0 is not None and roa1 is not None and roa0 > roa1),
("OCF > Net Income (earnings quality, less accruals)", ocf0 is not None and ni0 is not None and ocf0 > ni0),
("Leverage decreased: LT Debt/Assets lower (less debt)", ta0 and ta0 != 0 and ta1 and ta1 != 0 and (lt0 / ta0) < (lt1 / ta1)),
("Current Ratio improved (better liquidity)", cr0 is not None and cr1 is not None and cr0 > cr1),
("No dilution: shares unchanged or lower (no equity raise)", (sh0 is not None and sh1 is not None and sh0 <= sh1) if (sh0 is not None and sh1 is not None) else True),
("Gross Margin improved (pricing power)", gm0 is not None and gm1 is not None and gm0 > gm1),
("Asset Turnover improved (efficiency)", at0 is not None and at1 is not None and at0 > at1),
]
out["score"] = sum(1 for _, p in criteria if p)
out["criteria"] = criteria
out["used_ttm"] = bool(any(str(c).startswith("TTM") for c in fin.columns))
return out
except Exception:
return out
# ---------------------------------------------------------------------------
# Radar metrics
# ---------------------------------------------------------------------------
def radar_metrics_from_ai(ai_dict: Dict[str, Any]) -> Dict[str, Any]:
"""Build radar chart data from AI-extracted financials."""
cur = (ai_dict or {}).get("current_yr") or {}
prev = (ai_dict or {}).get("previous_yr") or {}
if not cur:
return {}
eq0 = (cur.get("TotalAssets") or 0) - (cur.get("CurrentLiabilities") or 0) - (cur.get("LongTermDebt") or 0)
if eq0 <= 0:
eq0 = (cur.get("TotalAssets") or 0) * 0.5
roe = (cur.get("NetIncome") or 0) / eq0 * 100 if eq0 else 0
ca, cl = cur.get("CurrentAssets") or 0, cur.get("CurrentLiabilities") or 0
current_ratio = (ca / cl) if cl and cl != 0 else 0
ta = cur.get("TotalAssets") or 1
asset_turnover = (cur.get("Revenue") or 0) / ta
equity_mult = (cur.get("TotalAssets") or 0) / eq0 if eq0 else 0
rev0, rev1 = cur.get("Revenue") or 0, prev.get("Revenue") or 0
rev_yoy = ((rev0 - rev1) / rev1 * 100) if rev1 and rev1 != 0 else 0
theta = ["Profitability (ROE)", "Liquidity (Curr.Ratio)", "Efficiency (Asset Turn.)", "Solvency (Equity Mult.)", "Growth (Rev YoY)"]
return {"theta": theta, "r": _radar_norm(roe, current_ratio, asset_turnover, equity_mult, rev_yoy), "labels": theta}
def get_radar_metrics_normalized(ticker: str) -> Dict[str, Any]:
"""ROE, Current Ratio, Asset Turnover, Equity Mult, Revenue YoY normalised 0-100."""
if not ticker:
return {}
q = get_dupont_altman_redflags_yoy(ticker)
if not q:
return {}
dupont_df = q.get("dupont")
if dupont_df is None or dupont_df.empty or len(dupont_df) < 2:
return {}
row0 = dupont_df.iloc[0]
roe = row0.get("ROE %") or 0
cr = row0.get("Current Ratio") or 0
at = row0.get("Asset Turnover") or 0
em = row0.get("Equity Mult.") or 0
rev0 = dupont_df["Revenue"].iloc[0] if "Revenue" in dupont_df.columns else None
rev1 = dupont_df["Revenue"].iloc[1] if "Revenue" in dupont_df.columns else None
rev_yoy = ((rev0 - rev1) / rev1 * 100) if (rev0 and rev1 and rev1 != 0) else 0
theta = ["Profitability (ROE)", "Liquidity (Curr.Ratio)", "Efficiency (Asset Turn.)", "Solvency (Equity Mult.)", "Growth (Rev YoY)"]
return {"theta": theta, "r": _radar_norm(roe, cr, at, em, rev_yoy), "labels": theta}
# ---------------------------------------------------------------------------
# Sector-specific metrics
# ---------------------------------------------------------------------------
def get_sector_specific_metrics(ticker: str, sector: str) -> Dict[str, Any]:
"""Technology: Rule of 40, R&D %. Retail: Inventory Turnover. Financials: ROE/ROA."""
if not yf:
return {}
try:
t = yf.Ticker(ticker.upper())
fin = t.financials
bal = t.balance_sheet
if fin is None or fin.empty:
fin = getattr(t, "quarterly_financials", None)
if fin is not None and not fin.empty:
fin = fin.iloc[:, :4].sum(axis=1).to_frame()
if bal is None or bal.empty:
bal = getattr(t, "quarterly_balance_sheet", None)
out: Dict[str, Any] = {}
sector_lower = (sector or "").lower()
if "technology" in sector_lower or "software" in sector_lower or "tech" in sector_lower:
rev = _get_row_series(fin, "Total Revenue", "Revenue", "Net Revenue")
cf_source = t.cashflow or getattr(t, "quarterly_cashflow", None)
ocf = _get_row_series(cf_source, "Operating Cash Flow", "Cash From Operating Activities")
capx = _get_row_series(cf_source, "Capital Expenditure", "Capital Expenditures")
rd = _get_row_series(fin, "Research And Development", "Research And Development Expense")
if rev is not None and len(rev) > 0:
r0 = _safe_float(rev.iloc[0])
if ocf is not None and len(ocf) > 0 and capx is not None and len(capx) > 0:
fcf = _safe_float(ocf.iloc[0]) - _safe_float(capx.iloc[0])
out["FCF Margin %"] = round(fcf / r0 * 100, 2) if r0 and fcf is not None else None
if rd is not None and len(rd) > 0:
out["R&D % of Revenue"] = round(_safe_float(rd.iloc[0]) / r0 * 100, 2) if r0 else None
if len(rev) >= 2:
cur_r, prev_r = _safe_float(rev.iloc[0]), _safe_float(rev.iloc[1])
rev_growth = ((cur_r - prev_r) / prev_r * 100) if prev_r and prev_r != 0 else None
if rev_growth is not None and "FCF Margin %" in out and out["FCF Margin %"] is not None:
out["Rule of 40 (Rev Growth + FCF Margin)"] = round(rev_growth + out["FCF Margin %"], 1)
if "consumer" in sector_lower or "retail" in sector_lower or "cyclical" in sector_lower:
inv = _get_row_series(bal, "Inventory", "Total Inventory")
cogs = _get_row_series(fin, "Cost Of Revenue", "Cost Of Goods Sold")
rev = _get_row_series(fin, "Total Revenue", "Revenue", "Net Revenue")
op_inc = _get_row_series(fin, "Operating Income", "EBIT")
if inv is not None and len(inv) > 0 and cogs is not None and len(cogs) > 0:
out["Inventory Turnover"] = round(_safe_float(cogs.iloc[0]) / _safe_float(inv.iloc[0]), 2) if _safe_float(inv.iloc[0]) else None
if rev is not None and len(rev) > 0 and op_inc is not None and len(op_inc) > 0:
out["Operating Margin %"] = round(_safe_float(op_inc.iloc[0]) / _safe_float(rev.iloc[0]) * 100, 2) if _safe_float(rev.iloc[0]) else None
if "financial" in sector_lower or "bank" in sector_lower or "insurance" in sector_lower:
ni = _get_row_series(fin, "Net Income", "Net Income Common Stockholders")
te = _get_row_series(bal, "Total Stockholder Equity", "Stockholders Equity", "Total Equity Gross Minority Interest")
ta_s = _get_row_series(bal, "Total Assets")
if ni is not None and te is not None and len(ni) > 0 and len(te) > 0:
out["ROE %"] = round(_safe_float(ni.iloc[0]) / _safe_float(te.iloc[0]) * 100, 2) if _safe_float(te.iloc[0]) else None
if ni is not None and ta_s is not None and len(ni) > 0 and len(ta_s) > 0:
out["ROA %"] = round(_safe_float(ni.iloc[0]) / _safe_float(ta_s.iloc[0]) * 100, 2) if _safe_float(ta_s.iloc[0]) else None
return out
except Exception:
return {}
# ---------------------------------------------------------------------------
# Quarterly momentum
# ---------------------------------------------------------------------------
def get_quarterly_momentum(ticker: str) -> Dict[str, Any]:
"""Last 4 quarters Revenue/NI with QoQ growth for the most recent."""
out: Dict[str, Any] = {"df": None, "qoq_revenue_pct": None, "qoq_ni_pct": None}
if not yf or not ticker:
return out
try:
t = yf.Ticker(ticker.upper())
qfin = getattr(t, "quarterly_financials", None)
if qfin is None or qfin.empty or len(qfin.columns) < 2:
return out
rev = _get_row_series(qfin, "Total Revenue", "Revenue", "Net Revenue")
ni = _get_row_series(qfin, "Net Income", "Net Income Common Stockholders")
if rev is None and ni is None:
return out
cols = list(qfin.columns)[:4]
rows: List[Dict[str, Any]] = []
for c in cols:
try:
if hasattr(c, "strftime"):
q = (c.month - 1) // 3 + 1
label = c.strftime("%Y") + f"-Q{q}"
else:
label = str(c)[:12]
except Exception:
label = str(c)[:12]
r_val = _safe_float(rev.loc[c]) if rev is not None and c in rev.index else None
n_val = _safe_float(ni.loc[c]) if ni is not None and c in ni.index else None
rows.append({"Quarter": label, "Revenue": r_val, "Net Income": n_val})
out["df"] = pd.DataFrame(rows)
if len(rows) >= 2:
r0, r1 = rows[0].get("Revenue"), rows[1].get("Revenue")
n0, n1 = rows[0].get("Net Income"), rows[1].get("Net Income")
if r0 is not None and r1 is not None and r1 != 0:
out["qoq_revenue_pct"] = round((r0 - r1) / abs(r1) * 100, 1)
if n0 is not None and n1 is not None and n1 != 0:
out["qoq_ni_pct"] = round((n0 - n1) / abs(n1) * 100, 1)
return out
except Exception:
return out
def get_quarterly_ratio_changes(ticker: str) -> List[Dict[str, Any]]:
"""QoQ ratio changes for NPM, ROE, Gross/Operating Margin, Current Ratio, Interest Coverage."""
out: List[Dict[str, Any]] = []
if not yf or not ticker:
return out
try:
t = yf.Ticker(ticker.upper())
qf = getattr(t, "quarterly_financials", None)
qb = getattr(t, "quarterly_balance_sheet", None)
if qf is None or qf.empty or qb is None or qb.empty or len(qf.columns) < 2 or len(qb.columns) < 2:
return out
rev = _get_row_series(qf, "Total Revenue", "Revenue", "Net Revenue")
ni = _get_row_series(qf, "Net Income", "Net Income Common Stockholders")
gross = _get_row_series(qf, "Gross Profit")
ebit = _get_row_series(qf, "Operating Income", "EBIT")
interest = _get_row_series(qf, "Interest Expense", "Interest Expense Net")
ta = _get_row_series(qb, "Total Assets")
te = _get_row_series(qb, "Total Stockholder Equity", "Stockholders Equity", "Total Equity Gross Minority Interest")
ca = _get_row_series(qb, "Current Assets")
cl = _get_row_series(qb, "Current Liabilities")
def v(s: Optional[pd.Series], col: Any) -> Optional[float]:
if s is None or col not in s.index:
return None
return _safe_float(s.get(col))
c0, c1 = qf.columns[0], qf.columns[1]
b0, b1 = qb.columns[0], qb.columns[1]
r0, r1 = v(rev, c0), v(rev, c1)
n0, n1 = v(ni, c0), v(ni, c1)
g0, g1 = v(gross, c0), v(gross, c1)
e0, e1 = v(ebit, c0), v(ebit, c1)
i0, i1 = v(interest, c0), v(interest, c1)
te0, te1 = v(te, b0), v(te, b1)
ca0, ca1 = v(ca, b0), v(ca, b1)
cl0, cl1 = v(cl, b0), v(cl, b1)
npm0 = (n0 / r0 * 100) if (n0 is not None and r0 and r0 != 0) else None
npm1 = (n1 / r1 * 100) if (n1 is not None and r1 and r1 != 0) else None
roe0 = (n0 / te0 * 100) if (n0 is not None and te0 and te0 != 0) else None
roe1 = (n1 / te1 * 100) if (n1 is not None and te1 and te1 != 0) else None
gm0 = (g0 / r0 * 100) if (g0 is not None and r0 and r0 != 0) else None
gm1 = (g1 / r1 * 100) if (g1 is not None and r1 and r1 != 0) else None
om0 = (e0 / r0 * 100) if (e0 is not None and r0 and r0 != 0) else None
om1 = (e1 / r1 * 100) if (e1 is not None and r1 and r1 != 0) else None
cr0 = (ca0 / cl0) if (ca0 is not None and cl0 and cl0 != 0) else None
cr1 = (ca1 / cl1) if (ca1 is not None and cl1 and cl1 != 0) else None
ic0 = (e0 / i0) if (e0 is not None and i0 and i0 != 0) else None
ic1 = (e1 / i1) if (e1 is not None and i1 and i1 != 0) else None
def make_row(metric: str, cur: Optional[float], prev: Optional[float], is_pct_point: bool = False) -> Optional[Dict[str, Any]]:
if cur is None:
return None
if prev is None:
return {"Metric": metric, "Current Value": round(cur, 2), "Change": "-", "Trend": "-"}
chg = (cur - prev) if is_pct_point else (((cur - prev) / abs(prev) * 100) if prev != 0 else 0)
trend = "up" if chg > 0 else ("down" if chg < 0 else "flat")
chg_str = f"{chg:+.1f}%" if not is_pct_point else f"{chg:+.1f} pp"
return {"Metric": metric, "Current Value": round(cur, 2), "Change": chg_str, "Trend": trend}
for name, cur_v, prev_v, is_pp in [
("NPM %", npm0, npm1, True), ("ROE %", roe0, roe1, True), ("Gross Margin %", gm0, gm1, True),
("Operating Margin %", om0, om1, True), ("Current Ratio", cr0, cr1, False), ("Interest Coverage", ic0, ic1, False),
]:
r = make_row(name, cur_v, prev_v, is_pp)
if r:
out.append(r)
return out
except Exception:
return out
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