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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>
This commit is contained in:
shawnkim1997
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
56a9561f71
commit
b2acda81ee
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"""High-level Gemini analysis orchestrators.
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Contains the composite analysis functions that combine multiple Gemini
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calls (chunked insights, comparative MD&A, industry outlook). These build
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on the primitives in :mod:`server.services.gemini_service`.
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"""
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from typing import Any, Callable, Dict, Optional
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from server.services.gemini_service import (
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_gemini_forensic_audit,
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_gemini_summarize_segment,
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_gemini_synthesize_report,
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_generate_with_retry,
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_is_rate_limit_error,
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get_gemini_model,
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)
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from server.services.text_chunker import clean_text_for_llm, smart_chunk, _split_into_chunks
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def get_mda_chunked_insights(
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api_key: str,
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sections: Dict[str, str],
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ticker: str,
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sector: str,
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industry: str,
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progress_callback: Optional[Callable[[str], None]] = None,
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) -> str:
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"""Full-text analysis: chunk 1A+7, summarise each, synthesise, then append forensic.
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Parameters
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----------
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api_key:
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Google Gemini API key.
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sections:
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Dict with keys ``item1a``, ``item7``, ``item3``, ``item9a``.
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ticker:
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Stock ticker symbol.
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sector / industry:
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Used for sector-aware KPI extraction.
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progress_callback:
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Optional ``fn(msg: str)`` called with status updates.
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Returns
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-------
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str
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Markdown-formatted Executive Insight Report.
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"""
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def _progress(msg: str) -> None:
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if progress_callback:
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progress_callback(msg)
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combined = (sections.get("item1a") or "") + "\n\n---\n\n" + (sections.get("item7") or "")
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combined = combined.strip()
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if not combined:
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return "No 10-K text available to analyse."
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chunks = _split_into_chunks(combined, max_chars=22_000)
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if not chunks:
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return "No content extracted."
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summaries = []
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n = len(chunks)
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for i, ch in enumerate(chunks):
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_progress(f"Analyzing Segment {i + 1}/{n}...")
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summary = _gemini_summarize_segment(api_key, ch, ticker, f"Segment {i + 1}/{n}")
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if summary:
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summaries.append(summary)
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if not summaries:
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return "Segment analysis produced no summaries."
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_progress("Synthesizing final report...")
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report = _gemini_synthesize_report(api_key, summaries, ticker, sector or "N/A", industry or "N/A")
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_progress("Running forensic audit (Item 3 & 9A)...")
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forensic = _gemini_forensic_audit(api_key, sections.get("item3") or "", sections.get("item9a") or "", ticker)
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return (report or "") + "\n\n---\n\n**Forensic (Item 3 & 9A)**\n\n" + (forensic or "")
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def get_mda_insights(
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api_key: str,
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item1a_text: str,
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item7_text: str,
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ticker: str,
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) -> str:
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"""Single-shot analysis of Item 1A + Item 7 (tone, strategy, risks)."""
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model = get_gemini_model(api_key)
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combined = []
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if item1a_text:
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combined.append(clean_text_for_llm(item1a_text))
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if item7_text:
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combined.append(clean_text_for_llm(item7_text))
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combined_text = smart_chunk("\n\n---\n\n".join(combined), max_chars=22_000)
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prompt = (
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f"You are a senior equity analyst. Use British English.\n\n"
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f"The text below is from the 10-K for {ticker}: **Item 1A** and **Item 7**.\n\n"
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"Provide a concise report:\n"
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"1. **Management's Tone (Sentiment)**\n"
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"2. **Key Strategic Shifts**\n"
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"3. **Major Hidden Risks**\n\n"
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"Use clear headings. Under 800 words."
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)
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full = f"--- 10-K Excerpt ---\n\n{combined_text}\n\n---\n\n{prompt}"
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try:
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response = _generate_with_retry(model, full, {"temperature": 0.3, "max_output_tokens": 4096})
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except Exception as api_err:
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if _is_rate_limit_error(api_err):
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raise RuntimeError("Rate limit exceeded. Please try again in a few minutes.") from api_err
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raise
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if not response or not response.text:
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return "No analysis generated."
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return response.text.strip()
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def get_mda_comparative_insights(
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api_key: str,
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item1a_text: str,
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item7_latest: str,
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item7_3y_ago: Optional[str],
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ticker: str,
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sector: Optional[str] = None,
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industry: Optional[str] = None,
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) -> str:
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"""Comparative or single-year MD&A deep-dive with sector-aware KPIs."""
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model = get_gemini_model(api_key)
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sector_label = (sector or "N/A").strip()
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industry_label = (industry or "N/A").strip()
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kpi_instruction = (
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f" Given that this company is in the **{sector_label}** sector"
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+ (f" (industry: {industry_label})" if industry_label != "N/A" else "")
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+ ", extract **industry-specific Non-GAAP KPIs** in a markdown table."
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)
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if not item7_3y_ago or not item7_3y_ago.strip():
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combined = []
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if item1a_text:
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combined.append(clean_text_for_llm(item1a_text))
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if item7_latest:
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combined.append(clean_text_for_llm(item7_latest))
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combined_text = smart_chunk("\n\n---\n\n".join(combined), max_chars=22_000)
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prompt = (
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f"You are a senior equity analyst. Use British English.\n"
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f"Latest 10-K only for {ticker} (Item 1A + Item 7). Provide:\n"
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"1. **Management's Tone**\n2. **Current Strategy & Priorities**\n"
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"3. **Major Hidden Risks**\n4. **Forensic / Quality of Earnings**\n"
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f"{kpi_instruction}\nUnder 800 words."
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)
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full = f"--- 10-K Excerpt (Latest Year) ---\n\n{combined_text}\n\n---\n\n{prompt}"
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else:
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latest_clean = smart_chunk(clean_text_for_llm(item7_latest), max_chars=12_000)
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past_clean = smart_chunk(clean_text_for_llm(item7_3y_ago), max_chars=12_000)
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prompt = (
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f"You are a senior equity analyst. Use British English.\n"
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f"Below are Item 7 from the 10-K for {ticker}: LATEST and THREE YEARS AGO.\n"
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"1. **Core strategy** changes\n2. **Emerging risks**\n"
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"3. **Management's tone** shift\n4. **Industry-specific KPIs**\n"
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f"{kpi_instruction}\nUnder 900 words."
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)
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full = (
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f"--- MD&A LATEST YEAR ---\n\n{latest_clean}\n\n"
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f"--- MD&A THREE YEARS AGO ---\n\n{past_clean}\n\n---\n\n{prompt}"
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)
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try:
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response = _generate_with_retry(model, full, {"temperature": 0.3, "max_output_tokens": 4096})
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except Exception as api_err:
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if _is_rate_limit_error(api_err):
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raise RuntimeError("Rate limit exceeded. Please try again in a few minutes.") from api_err
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raise
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if not response or not response.text:
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return "No analysis generated."
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return response.text.strip()
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def get_industry_outlook(
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api_key: str,
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industry_name: str,
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tickers: list,
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) -> str:
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"""Generate a Wall Street macro-analyst-style Industry Outlook (12-18 months)."""
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model = get_gemini_model(api_key)
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ticker_list_str = ", ".join(str(t).upper() for t in tickers if t)
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prompt = (
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f"Act as an elite Wall Street macro analyst. Provide a concise "
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f"**Industry Outlook** for the **{industry_name}** sector, "
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f"which includes companies like {ticker_list_str}.\n\n"
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"Focus on:\n"
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"1. **Macro trends** (next 12-18 months)\n"
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"2. **Major growth drivers**\n"
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"3. **Key headwinds or regulatory risks**\n\n"
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"Use clear headings. Under 600 words."
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)
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try:
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response = _generate_with_retry(model, prompt, {"temperature": 0.4, "max_output_tokens": 2048})
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except Exception as api_err:
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if _is_rate_limit_error(api_err):
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raise RuntimeError("Rate limit exceeded. Please try again in a few minutes.") from api_err
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raise
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if not response or not response.text:
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return "No industry outlook generated."
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return response.text.strip()
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