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

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>

data/scores_ai.py

@@ -0,0 +1,98 @@
+"""
+AI-derived score/chart helper functions: build Sankey, Piotroski, Radar data from Gemini-extracted financials.
+"""
+from utils.charts import _radar_norm
+
+
+def sankey_data_from_ai(ai_dict: dict) -> dict:
+    """Build Sankey input dict from get_sec_financials_llm result (current_yr). Gross Profit = Revenue - CostOfRevenue; Operating Income = Gross Profit - OperatingExpenses."""
+    out = {"revenue": 0, "cogs": 0, "gross_profit": 0, "opex": 0, "operating_income": 0, "tax_interest_other": 0, "net_income": 0}
+    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))
+    out["revenue"] = max(revenue, 1)
+    out["cogs"] = cogs
+    out["gross_profit"] = gross_profit
+    out["opex"] = opex
+    out["operating_income"] = operating_income
+    out["tax_interest_other"] = tax_interest_other
+    out["net_income"] = net_income
+    return out
+
+
+def piotroski_from_ai(ai_dict: dict) -> dict:
+    """Piotroski F-Score (0-9) from AI-extracted current_yr vs previous_yr. Returns {score, criteria, used_ttm: True}."""
+    out = {"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, k): 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
+    c1 = ni0 > 0
+    c2 = ocf0 > 0
+    c3 = (roa0 is not None and roa1 is not None and roa0 > roa1)
+    c4 = ocf0 > ni0
+    lt0, lt1 = v(cur, "LongTermDebt"), v(prev, "LongTermDebt")
+    c5 = (ta0 and ta1 and (lt0 / ta0) < (lt1 / ta1)) if ta0 and ta1 else False
+    ca0, ca1 = v(cur, "CurrentAssets"), v(prev, "CurrentAssets")
+    cl0, cl1 = v(cur, "CurrentLiabilities"), v(prev, "CurrentLiabilities")
+    cr0 = (ca0 / cl0) if cl0 and cl0 != 0 else None
+    cr1 = (ca1 / cl1) if cl1 and cl1 != 0 else None
+    c6 = (cr0 is not None and cr1 is not None and cr0 > cr1)
+    sh0, sh1 = v(cur, "SharesOutstanding"), v(prev, "SharesOutstanding")
+    c7 = (sh0 <= sh1) if (sh0 and sh1) else True
+    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
+    c8 = (gm0 is not None and gm1 is not None and gm0 > gm1)
+    at0 = (rev0 / ta0) if rev0 and ta0 and ta0 != 0 else None
+    at1 = (rev1 / ta1) if rev1 and ta1 and ta1 != 0 else None
+    c9 = (at0 is not None and at1 is not None and at0 > at1)
+    criteria = [
+        ("Net Income > 0 (profitability)", c1),
+        ("Operating Cash Flow > 0 (cash generative)", c2),
+        ("ROA increased vs prior period (improving returns)", c3),
+        ("OCF > Net Income (earnings quality, less accruals)", c4),
+        ("Leverage decreased: LT Debt/Assets lower (less debt)", c5),
+        ("Current Ratio improved (better liquidity)", c6),
+        ("No dilution: shares unchanged or lower (no equity raise)", c7),
+        ("Gross Margin improved (pricing power)", c8),
+        ("Asset Turnover improved (efficiency)", c9),
+    ]
+    out["score"] = sum(1 for _, p in criteria if p)
+    out["criteria"] = criteria
+    return out
+
+
+def radar_metrics_from_ai(ai_dict: dict) -> dict:
+    """ROE, Current Ratio, Asset Turnover, Equity Mult, Revenue YoY from AI dict; normalized 0-100 for radar. Equity proxy: TotalAssets - CurrentLiabilities - LongTermDebt."""
+    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
+    return {
+        "theta": ["Profitability (ROE)", "Liquidity (Curr.Ratio)", "Efficiency (Asset Turn.)", "Solvency (Equity Mult.)", "Growth (Rev YoY)"],
+        "r": _radar_norm(roe, current_ratio, asset_turnover, equity_mult, rev_yoy),
+        "labels": ["Profitability (ROE)", "Liquidity (Curr.Ratio)", "Efficiency (Asset Turn.)", "Solvency (Equity Mult.)", "Growth (Rev YoY)"],
+    }