mirror of
https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
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shawnkim1997
d337c63976
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>
210 lines
12 KiB
Python
210 lines
12 KiB
Python
"""Tab 1 — Financial Health (Tables & Charts): Sankey, Radar, F-Score,
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Altman Z, red flags, sector metrics, YoY changes, quarterly momentum/ratios."""
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import pandas as pd
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import streamlit as st
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from data.sec_downloader import get_10k_sections
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from data.ratios import get_dupont_altman_redflags_yoy, get_quarterly_momentum, get_quarterly_ratio_changes
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from data.scores import (
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get_income_statement_sankey_data, get_radar_metrics_normalized, _build_radar_figure,
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get_piotroski_fscore, get_sector_specific_metrics,
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)
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from data.scores_ai import sankey_data_from_ai, piotroski_from_ai, radar_metrics_from_ai
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from ai.gemini_sec import get_sec_financials_llm
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from utils.charts import (
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_build_sankey_figure, _build_radar_figure_from_metrics,
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_build_radar_from_manual, _apply_dark_theme,
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)
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try:
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from yahooquery import Ticker as YQTicker
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except ImportError:
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YQTicker = None
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def _style_change_column(df: pd.DataFrame):
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"""Green for improvement (+), red for decline (-) in Change column."""
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change_col = "Change (%)" if "Change (%)" in df.columns else "Change"
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if change_col not in df.columns or df.empty:
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return df.style
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def _cell_style(v):
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if v is None or (isinstance(v, float) and pd.isna(v)):
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return ""
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s = str(v).strip()
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if s == "—":
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return ""
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if s.startswith("+") or "↑" in s:
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return "background-color: #d4edda; color: #155724"
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if s.startswith("-") or "↓" in s:
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return "background-color: #f8d7da; color: #721c24"
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return ""
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return df.style.apply(lambda col: [_cell_style(v) for v in col], subset=[change_col])
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def render_tab1_quantitative(ticker, quant_ticker, market, sector, industry, google_api_key, sec_email):
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"""Render the Financial Health section of Tab 1."""
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ai_data = {}
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if market and "US" in market and google_api_key and sec_email:
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with st.spinner("SEC 10-K 원본에서 재무제표 데이터를 해독하여 그래프를 생성 중입니다... (약 30~60초 소요)"):
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sections, _ = get_10k_sections(ticker, sec_email)
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item8 = (sections or {}).get("item8") or ""
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if item8.strip():
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ai_data = get_sec_financials_llm(google_api_key, item8, ticker)
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q = get_dupont_altman_redflags_yoy(quant_ticker)
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dupont_df = (q or {}).get("dupont") if q else None
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if q or ai_data:
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st.markdown("---")
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st.markdown("#### 📊 Financial Health (Tables & Charts)")
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c1, c2 = st.columns(2)
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with c1:
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if ai_data and ai_data.get("current_yr"):
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sankey_data = sankey_data_from_ai(ai_data)
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else:
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sankey_data = get_income_statement_sankey_data(quant_ticker)
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if sankey_data.get("revenue", 0) > 0:
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fig_sankey = _build_sankey_figure(sankey_data)
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if fig_sankey is not None:
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_apply_dark_theme(fig_sankey)
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st.plotly_chart(fig_sankey, use_container_width=True)
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else:
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st.caption("Income Statement flow: data not available.")
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with c2:
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if ai_data and ai_data.get("current_yr"):
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radar_metrics = radar_metrics_from_ai(ai_data)
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fig_radar = _build_radar_figure_from_metrics(radar_metrics) if radar_metrics else None
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else:
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fig_radar = _build_radar_figure(quant_ticker)
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if fig_radar is not None:
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_apply_dark_theme(fig_radar)
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st.plotly_chart(fig_radar, use_container_width=True)
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else:
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st.caption("Financial radar: need 2+ years of data.")
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with st.expander("Manual Data Entry (Radar Chart Fallback)", expanded=False):
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st.caption("Enter 5 key ratios to plot a custom radar. ROE %, Current Ratio, Asset Turnover, Equity Mult., Revenue YoY %.")
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roe_man = st.number_input("ROE %", value=15.0, min_value=-50.0, max_value=100.0, step=1.0, key="radar_roe")
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cr_man = st.number_input("Current Ratio", value=1.5, min_value=0.0, max_value=10.0, step=0.1, key="radar_cr")
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at_man = st.number_input("Asset Turnover", value=0.8, min_value=0.0, max_value=5.0, step=0.1, key="radar_at")
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em_man = st.number_input("Equity Mult.", value=2.0, min_value=0.5, max_value=10.0, step=0.1, key="radar_em")
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yoy_man = st.number_input("Revenue YoY %", value=10.0, min_value=-50.0, max_value=200.0, step=1.0, key="radar_yoy")
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if st.button("Plot Radar", key="radar_plot_btn"):
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fig_man = _build_radar_from_manual(roe_man, cr_man, at_man, em_man, yoy_man)
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if fig_man is not None:
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st.session_state["radar_manual_fig"] = fig_man
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if st.session_state.get("radar_manual_fig") is not None:
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_apply_dark_theme(st.session_state["radar_manual_fig"])
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st.plotly_chart(st.session_state["radar_manual_fig"], use_container_width=True)
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with st.expander("Debug: Raw YahooQuery Data", expanded=False):
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if YQTicker and quant_ticker:
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try:
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yq_ticker = YQTicker(quant_ticker.upper())
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inc_raw = yq_ticker.income_statement(trailing=False)
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bal_raw = yq_ticker.balance_sheet(trailing=False)
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if inc_raw is not None and not inc_raw.empty:
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st.caption("Income statement (last 2 periods) — check column names for mapping.")
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st.dataframe(inc_raw.tail(2), use_container_width=True, hide_index=True)
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else:
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st.caption("Income statement: no data.")
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if bal_raw is not None and not bal_raw.empty:
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st.caption("Balance sheet (last 2 periods) — check column names for mapping.")
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st.dataframe(bal_raw.tail(2), use_container_width=True, hide_index=True)
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else:
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st.caption("Balance sheet: no data.")
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except Exception as e:
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st.error(f"YahooQuery debug failed: {e}")
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else:
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st.caption("YahooQuery not available or no ticker selected.")
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if ai_data and ai_data.get("current_yr"):
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piot = piotroski_from_ai(ai_data)
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else:
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piot = get_piotroski_fscore(quant_ticker)
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st.markdown("**Piotroski F-Score (9-point checklist)**")
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score = piot.get("score", 0)
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legend = "**Score 8–9: Excellent** · 4–7: Average · 0–3: High Risk"
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st.metric("F-Score", f"{score} / 9", legend)
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if ai_data and ai_data.get("current_yr"):
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st.caption("*(from SEC 10-K Item 8)*")
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elif piot.get("used_ttm"):
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st.caption("*(Estimated via TTM Data)*")
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st.caption("✅ = Good (passes criterion). ❌ = Fails criterion.")
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criteria = piot.get("criteria", [])
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if criteria:
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cols = st.columns(3)
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for i, (label, passed) in enumerate(criteria):
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with cols[i % 3]:
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st.caption(("✅ " if passed else "❌ ") + label)
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az = (q or {}).get("altman_z")
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if az is not None:
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st.caption(f"**Altman Z-Score:** {az} (Safe > 2.99 · Grey 1.81–2.99 · Distress < 1.81)")
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red_flags = (q or {}).get("red_flags") or []
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if red_flags:
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for rf in red_flags:
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val = rf.get("value")
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val_str = "N/A" if (val is None or (isinstance(val, float) and (pd.isna(val) or val != val))) else val
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st.warning(f"**{rf.get('metric')}:** {val_str} (threshold: {rf.get('threshold')})")
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elif dupont_df is not None and not dupont_df.empty:
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st.success("No red flags (Current Ratio ≥ 1.0, Interest Coverage ≥ 1.5).")
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sector_metrics = get_sector_specific_metrics(quant_ticker, sector) if quant_ticker else {}
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if sector_metrics:
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st.markdown("**Sector-specific metrics**")
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cols = st.columns(min(len(sector_metrics), 4))
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for i, (k, v) in enumerate(sector_metrics.items()):
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with cols[i % len(cols)]:
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disp = f"{v}" if v is not None else "N/A"
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st.metric(k, disp, None)
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yoy_list = (q or {}).get("yoy") or []
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if yoy_list:
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st.markdown("**YoY ratio changes**")
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rows_yoy = []
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for item in yoy_list:
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cur = item.get("Latest")
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if cur is not None and isinstance(cur, (int, float)):
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cur = round(cur, 2)
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chg_pp = item.get("YoY (pp)")
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chg_pct = item.get("YoY %")
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if chg_pp is not None:
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chg_str = f"{chg_pp:+.1f}%"
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elif chg_pct is not None:
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chg_str = f"{chg_pct:+.1f}%"
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else:
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chg_str = "—"
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status = "↑" if (chg_pp is not None and chg_pp > 0) or (chg_pct is not None and chg_pct > 0) else ("↓" if (chg_pp is not None and chg_pp < 0) or (chg_pct is not None and chg_pct < 0) else "—")
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cur_disp = f"{cur:.2f}" if isinstance(cur, (int, float)) else ("—" if cur is None else str(cur))
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rows_yoy.append({"Metric": item.get("Ratio"), "Current Value": cur_disp, "Change (%)": chg_str, "Status": status})
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if rows_yoy:
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df_yoy = pd.DataFrame(rows_yoy)
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st.dataframe(_style_change_column(df_yoy), use_container_width=True, hide_index=True)
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st.markdown("**Quarter ratio changes**")
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qmom = get_quarterly_momentum(quant_ticker)
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qoq_rows = get_quarterly_ratio_changes(quant_ticker)
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qoq_r, qoq_n = qmom.get("qoq_revenue_pct"), qmom.get("qoq_ni_pct")
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build = []
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if qoq_r is not None:
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build.append({"Metric": "Revenue", "Current Value": "—", "Change (%)": f"{qoq_r:+.1f}%", "Status": "↑" if qoq_r > 0 else "↓"})
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if qoq_n is not None:
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build.append({"Metric": "Net Income", "Current Value": "—", "Change (%)": f"{qoq_n:+.1f}%", "Status": "↑" if qoq_n > 0 else "↓"})
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for r in qoq_rows:
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r_copy = dict(r)
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if "Current Value" in r_copy:
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v = r_copy["Current Value"]
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if isinstance(v, (int, float)):
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r_copy["Current Value"] = f"{round(v, 2):.2f}"
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elif v is None:
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r_copy["Current Value"] = "—"
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else:
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r_copy["Current Value"] = str(v)
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if "Change" in r_copy and "Change (%)" not in r_copy:
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r_copy["Change (%)"] = r_copy.pop("Change", "—")
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if "Trend" in r_copy:
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r_copy["Status"] = r_copy.pop("Trend", "—")
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build.append(r_copy)
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if build:
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df_q = pd.DataFrame(build)
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if "Change" in df_q.columns and "Change (%)" not in df_q.columns:
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df_q = df_q.rename(columns={"Change": "Change (%)"})
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if "Trend" in df_q.columns:
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df_q = df_q.rename(columns={"Trend": "Status"})
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st.dataframe(_style_change_column(df_q), use_container_width=True, hide_index=True)
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elif not qmom.get("df") or qmom["df"].empty:
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st.caption("Quarterly data not available for this ticker.")
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else:
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st.info("Quantitative data not available for this ticker.")
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