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refactor: modular architecture v3.0 + SEC filing viewer fix + README

Browse Source 
Architecture (3,909-line monolith → 28 focused modules, all < 300 lines):
- config/: constants.py (company lists, row maps, Damodaran baselines), theme.py (CSS/HTML)
- utils/: prefs, formatting, ticker, dcf, charts, ui_helpers
- data/: sec_parser, sec_fetcher, sec_downloader, financials, fundamentals,
         valuation, ratios, scores, scores_ai, market
- ai/: gemini_core, gemini_sec, gemini_insights
- views/: sidebar, tab1_quant, tab1_ai, tab1_filings, tab2_dcf,
          tab3_comps, tab4_news, tab5_markets, tab6_crypto, tab7_technical
- app.py: thin orchestrator (~118 lines)
- Strict unidirectional dependency graph (no circular imports)
- All @st.cache_data TTLs and st.session_state keys preserved identically

SEC filing viewer fix:
- Rebuilt EDGAR fetch chain: company_tickers.json → CIK → submissions API
  → filings.recent.primaryDocument[] (replaces deprecated directory.item)
- Filing type selectbox (10-K, 10-Q, 8-K, 20-F, 6-K) connected to backend
- Native HTML rendered via streamlit.components.v1.html() with CSS reset
- Errors surfaced explicitly with st.error()
- DART direct links restored for Korean-listed companies

.gitignore: data/ → data/*.json + data/*.html (preserve Python modules)
README: full rewrite for master's portfolio — 7-tab layout, architecture
diagram, modular structure tree, technical challenges, design rationale

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
shawnkim1997
2026-03-19 22:11:07 +00:00
co-authored by Claude Sonnet 4.6
parent 7ce5661569
commit d337c63976
41 changed files with 4409 additions and 2942 deletions
Download Patch File Download Diff File Expand all files Collapse all files
data/ratios.py
+285
View File
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from typing import Optional
import pandas as pd
import streamlit as st
from utils.formatting import _safe_float, _na
from data.financials import _get_row_series, _get_annual_financials_balance_cashflow
try:
import yfinance as yf
except ImportError:
yf = None
@st.cache_data(ttl=300)
def get_comps_data(tickers: tuple) -> pd.DataFrame:
"""Fetch Forward P/E, EV/EBITDA, P/B using forwardPE, enterpriseToEbitda, priceToBook. Missing → None (display as N/A). Robust per-ticker error handling."""
if not yf:
return pd.DataFrame()
rows = []
for sym in tickers:
sym = str(sym).strip().upper()
if not sym:
continue
try:
t = yf.Ticker(sym)
info = t.info or {}
forward_pe = info.get("forwardPE") or info.get("Forward PE") or info.get("trailingPE") or info.get("Trailing PE")
ev_ebitda = info.get("enterpriseToEbitda")
if ev_ebitda is None:
ev, ebitda = info.get("enterpriseValue"), info.get("ebitda")
if ev is not None and ebitda is not None and ebitda != 0:
ev_ebitda = ev / ebitda
pb = info.get("priceToBook") or info.get("Price To Book")
rows.append({
"Ticker": sym,
"Forward P/E": round(float(forward_pe), 2) if forward_pe is not None and _safe_float(forward_pe) is not None else None,
"EV/EBITDA": round(float(ev_ebitda), 2) if ev_ebitda is not None and _safe_float(ev_ebitda) is not None else None,
"P/B": round(float(pb), 2) if pb is not None and _safe_float(pb) is not None else None,
})
except Exception:
rows.append({"Ticker": sym, "Forward P/E": None, "EV/EBITDA": None, "P/B": None})
if not rows:
return pd.DataFrame()
return pd.DataFrame(rows)
@st.cache_data(ttl=300)
def get_dupont_altman_redflags_yoy(ticker: str) -> dict:
"""Returns DuPont (3-step ROE), Altman Z-Score, red flags, YoY. Uses yahooquery then yfinance with TTM fallback."""
try:
fin, bal, _ = _get_annual_financials_balance_cashflow(ticker)
if fin is None or fin.empty or bal is None or bal.empty:
return {}
t = yf.Ticker(ticker.upper())
info = t.info or {}
# TTM columns: keep order TTM0 (current), TTM1 (prior). Else use date sort (newest first).
col_list = fin.columns.tolist()
if col_list and str(col_list[0]).startswith("TTM"):
dates = col_list[:3]
else:
dates = sorted(col_list, reverse=True)[:3]
if not dates:
return {}
rev = _get_row_series(fin, "Total Revenue", "Revenue", "Net Revenue")
ni = _get_row_series(fin, "Net Income", "Net Income Common Stockholders")
ebit = _get_row_series(fin, "Operating Income", "EBIT")
gross = _get_row_series(fin, "Gross Profit")
interest = _get_row_series(fin, "Interest Expense", "Interest Expense Net")
total_assets = _get_row_series(bal, "Total Assets")
total_equity = _get_row_series(bal, "Total Stockholder Equity", "Stockholders Equity", "Total Equity Gross Minority Interest")
current_assets = _get_row_series(bal, "Current Assets")
current_liab = _get_row_series(bal, "Current Liabilities")
retained = _get_row_series(bal, "Retained Earnings")
total_liab = _get_row_series(bal, "Total Liabilities")
market_cap = info.get("marketCap") or info.get("Market Cap")
def _v(s, d):
if s is None or d not in s.index:
return None
return _safe_float(s.get(d))
rows = []
for i, d in enumerate(dates):
yr = int(str(d)[:4]) if (isinstance(d, str) and str(d)[:4].isdigit()) else (d.year if hasattr(d, "year") else (2024 - i))
r = _v(rev, d)
net_i = _v(ni, d)
ta = _v(total_assets, d)
te = _v(total_equity, d)
if ta and ta > 0 and te and te > 0 and r and r != 0:
npm = (net_i / r * 100) if net_i is not None else None
at = r / ta if r and ta else None
em = ta / te if ta and te else None
roe = (net_i / te * 100) if (net_i and te) else (npm * at * em / 100 if (npm and at and em) else None)
else:
npm = at = em = roe = None
gross_p = _v(gross, d)
gross_margin = (gross_p / r * 100) if (gross_p and r and r != 0) else None
op_inc = _v(ebit, d)
op_margin = (op_inc / r * 100) if (op_inc and r and r != 0) else None
ca = _v(current_assets, d)
cl = _v(current_liab, d)
current_ratio = (ca / cl) if (ca and cl and cl != 0) else None
int_exp = _v(interest, d)
if op_inc is not None and int_exp is not None and int_exp != 0:
_ic = op_inc / int_exp
interest_cov = round(_ic, 2) if (_ic == _ic and not (isinstance(_ic, float) and (pd.isna(_ic) or _ic != _ic))) else None
else:
interest_cov = None # N/A when Interest Expense is 0 or missing (avoid nan%)
rows.append({
"Year": yr,
"Revenue": r, "Net Income": net_i,
"NPM %": round(npm, 2) if npm is not None else None,
"Asset Turnover": round(at, 4) if at is not None else None,
"Equity Mult.": round(em, 2) if em is not None else None,
"ROE %": round(roe, 2) if roe is not None else None,
"Gross Margin %": round(gross_margin, 2) if gross_margin is not None else None,
"Operating Margin %": round(op_margin, 2) if op_margin is not None else None,
"Current Ratio": round(current_ratio, 2) if current_ratio is not None else None,
"Interest Coverage": interest_cov,
})
dupont_df = pd.DataFrame(rows)
yoy = []
if len(dupont_df) >= 2:
for col in ["NPM %", "ROE %", "Gross Margin %", "Operating Margin %", "Current Ratio", "Interest Coverage"]:
if col not in dupont_df.columns:
continue
cur = dupont_df[col].iloc[0]
prev = dupont_df[col].iloc[1]
if cur is not None and prev is not None and prev != 0 and not (pd.isna(cur) or pd.isna(prev)):
if "Margin" in col or "NPM" in col or "ROE" in col:
chg_pp = (cur - prev) # percentage point change (e.g. 7.0 = 7%)
if pd.isna(chg_pp) or chg_pp != chg_pp:
continue
yoy.append({"Ratio": col, "Latest": cur, "Prior": prev, "YoY (pp)": round(chg_pp, 2), "Comment": f"{'Improved' if chg_pp > 0 else 'Declined'} by {abs(chg_pp):.1f}% YoY"})
else:
pct = (cur - prev) / abs(prev) * 100
if pd.isna(pct) or pct != pct:
continue
yoy.append({"Ratio": col, "Latest": cur, "Prior": prev, "YoY %": round(pct, 1), "Comment": f"{'Up' if pct > 0 else 'Down'} {abs(round(pct, 1))}% YoY"})
latest_bal_d = bal.columns[0]
wc = (_v(current_assets, latest_bal_d) or 0) - (_v(current_liab, latest_bal_d) or 0)
ta_l = _v(total_assets, latest_bal_d)
re_l = _v(retained, latest_bal_d)
tl_l = _v(total_liab, latest_bal_d)
ebit_l = _v(ebit, fin.columns[0])
sales_l = _v(rev, fin.columns[0])
altman_z = None
if ta_l and ta_l > 0 and market_cap is not None and tl_l and tl_l != 0 and sales_l:
a = wc / ta_l
b = (re_l or 0) / ta_l
c = (ebit_l or 0) / ta_l
d = market_cap / tl_l
e = sales_l / ta_l
altman_z = 1.2 * a + 1.4 * b + 3.3 * c + 0.6 * d + 1.0 * e
red_flags = []
if len(dupont_df) > 0:
row0 = dupont_df.iloc[0]
cr = row0.get("Current Ratio")
if cr is not None and cr < 1.0:
red_flags.append({"metric": "Current Ratio", "value": cr, "threshold": 1.0, "flag": "WARNING", "comment": "Current assets do not cover current liabilities; liquidity risk."})
ic = row0.get("Interest Coverage")
if ic is not None and ic < 1.5:
red_flags.append({"metric": "Interest Coverage", "value": ic, "threshold": 1.5, "flag": "WARNING", "comment": "EBIT barely covers interest; default risk."})
return {
"dupont": dupont_df,
"yoy": yoy,
"altman_z": round(altman_z, 2) if altman_z is not None else None,
"red_flags": red_flags,
}
except (KeyError, TypeError, ZeroDivisionError, IndexError) as e:
return {}
except Exception:
return {}
@st.cache_data(ttl=300)
def get_quarterly_momentum(ticker: str) -> dict:
"""Last 4 quarters Revenue and Net Income from quarterly_financials; QoQ growth for most recent quarter. Returns {df, qoq_revenue_pct, qoq_ni_pct} or empty."""
out = {"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 = []
for c in cols:
try:
if hasattr(c, "strftime"):
q = (c.month - 1) // 3 + 1 if hasattr(c, "month") else 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
@st.cache_data(ttl=300)
def get_quarterly_ratio_changes(ticker: str) -> list:
"""QoQ ratio changes: NPM %, ROE %, Gross Margin %, Operating Margin %, Current Ratio, Interest Coverage. Latest quarter vs previous. Returns list of {Metric, Current, Change, Trend}."""
out = []
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, col):
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)
ta0, ta1 = v(ta, b0), v(ta, b1)
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 row(metric, cur, prev, is_pct_point=False):
if cur is None:
return None
cur_str = f"{round(cur, 2):.2f}"
if prev is None or (is_pct_point and prev != prev):
return {"Metric": metric, "Current Value": cur_str, "Change": "—", "Trend": "—"}
if is_pct_point:
chg = cur - prev
else:
chg = ((cur - prev) / abs(prev) * 100) if prev != 0 else 0
trend = "↑" if chg > 0 else ("↓" if chg < 0 else "—")
chg_str = f"{chg:+.1f}%" if not is_pct_point else f"{chg:+.1f} pp"
return {"Metric": metric, "Current Value": cur_str, "Change": chg_str, "Trend": trend}
for name, cur, prev, 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 = row(name, cur, prev, is_pp)
if r:
out.append(r)
return out
except Exception:
return out