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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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"""Builds AI context from active widget data for the ATLAS Terminal chat."""
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from __future__ import annotations
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from typing import Any
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# ---------------------------------------------------------------------------
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# Widget-specific context templates
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# ---------------------------------------------------------------------------
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_DCF_TEMPLATE = """
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## DCF Valuation Context
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- Implied share price: ${implied_price:.2f}
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- Current market price: ${current_price:.2f}
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- Upside/Downside: {upside:+.1f}%
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- WACC: {wacc:.1f}%
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- Terminal growth rate: {terminal_growth:.1f}%
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- FCF projections (5Y): {fcf_projections}
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"""
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_FINANCIALS_TEMPLATE = """
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## Financial Metrics Context
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- Revenue (TTM): ${revenue}
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- Net income (TTM): ${net_income}
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- Gross margin: {gross_margin:.1f}%
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- Operating margin: {operating_margin:.1f}%
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- ROE: {roe:.1f}%
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- Debt/Equity: {debt_equity:.2f}
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- Current ratio: {current_ratio:.2f}
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"""
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_TECHNICAL_TEMPLATE = """
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## Technical Analysis Context
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- RSI (14): {rsi:.1f}
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- MACD: {macd:.4f} | Signal: {macd_signal:.4f}
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- SMA 50: ${sma_50:.2f} | SMA 200: ${sma_200:.2f}
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- 52-week high: ${high_52w:.2f} | Low: ${low_52w:.2f}
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- Volume (avg 20d): {avg_volume}
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"""
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_PORTFOLIO_TEMPLATE = """
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## Portfolio Context
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- Total value: ${total_value:,.0f}
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- Number of positions: {position_count}
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- Top holdings: {top_holdings}
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- Sector allocation: {sector_allocation}
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"""
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_FILING_TEMPLATE = """
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## SEC Filing Context
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- Latest filing type: {filing_type}
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- Filed on: {filing_date}
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- Key sections available: {sections}
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"""
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# ---------------------------------------------------------------------------
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# Suggested questions per widget
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# ---------------------------------------------------------------------------
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_WIDGET_QUESTIONS: dict[str, list[str]] = {
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"dcf": [
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"Are the market's growth assumptions reasonable for this company?",
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"What would the fair value be with a higher discount rate?",
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"How sensitive is the valuation to terminal growth assumptions?",
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],
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"financials": [
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"How do the margins compare to industry peers?",
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"Is the revenue growth trend sustainable?",
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"What are the key drivers behind the profitability changes?",
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],
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"technical": [
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"What does the current technical setup suggest for the near term?",
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"Is the stock overbought or oversold based on RSI?",
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"Are there any notable divergences between price and momentum?",
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],
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"portfolio": [
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"Is my sector diversification sufficient?",
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"Which positions carry the most concentration risk?",
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"How does my portfolio beta compare to the market?",
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],
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"filing": [
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"What are the key risks disclosed in the latest filing?",
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"Are there any notable changes in accounting policies?",
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"What does management say about the competitive landscape?",
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],
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"news": [
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"What is the overall sentiment of recent news?",
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"Are there any material events that could affect the stock?",
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"How might recent headlines impact the company's outlook?",
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],
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}
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_DEFAULT_QUESTIONS: list[str] = [
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"Give me a quick overview of this company's financial health.",
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"What are the biggest risks facing this stock right now?",
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"Should I consider adding this to my portfolio? Why or why not?",
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]
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# ---------------------------------------------------------------------------
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# ContextBuilder
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# ---------------------------------------------------------------------------
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class ContextBuilder:
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"""Builds AI context from active widget data.
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The context is injected into the system prompt so the LLM can reference
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concrete numbers when answering the user's questions about a ticker.
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"""
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def build_system_prompt(
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self,
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ticker: str,
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active_widgets: list[str],
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widget_data: dict[str, Any],
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) -> str:
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"""Build a context-aware system prompt.
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Args:
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ticker: The active ticker symbol (e.g. ``"AAPL"``).
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active_widgets: List of widget identifiers currently visible
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(e.g. ``["dcf", "financials", "technical"]``).
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widget_data: A dict keyed by widget name containing the data
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displayed in each widget.
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Returns:
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A system prompt string enriched with financial context.
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"""
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sections: list[str] = [
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"You are ATLAS, an expert financial analyst assistant "
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"integrated into the ATLAS Terminal.\n"
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"You have direct access to the data the user is currently viewing.\n"
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"Answer concisely with concrete numbers when available. "
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"Use markdown formatting for readability.",
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]
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# Always include base info if available
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base = widget_data.get("base", {})
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if ticker:
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sections.append(
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f"\n## Active Ticker: {ticker.upper()}\n"
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f"- Sector: {base.get('sector', 'N/A')}\n"
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f"- Current price: ${base.get('current_price', 'N/A')}\n"
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f"- Market cap: {base.get('market_cap', 'N/A')}\n"
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)
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# Append widget-specific context
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for widget in active_widgets:
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section = self._build_widget_section(widget, widget_data)
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if section:
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sections.append(section)
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return "\n".join(sections)
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def build_suggested_questions(
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self,
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active_widgets: list[str],
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) -> list[str]:
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"""Generate suggested questions based on active widgets.
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Args:
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active_widgets: List of widget identifiers currently visible.
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Returns:
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A list of 3-5 suggested question strings.
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"""
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questions: list[str] = []
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for widget in active_widgets:
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widget_key = widget.lower().strip()
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if widget_key in _WIDGET_QUESTIONS:
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# Pick the first question from each active widget
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questions.append(_WIDGET_QUESTIONS[widget_key][0])
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# Pad with defaults if we have fewer than 3
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for q in _DEFAULT_QUESTIONS:
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if len(questions) >= 5:
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break
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if q not in questions:
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questions.append(q)
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return questions[:5]
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# -- private helpers ----------------------------------------------------
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def _build_widget_section(
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self, widget: str, widget_data: dict[str, Any]
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) -> str:
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"""Render context section for a specific widget.
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Returns an empty string when no data is available for the widget.
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"""
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widget_key = widget.lower().strip()
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data = widget_data.get(widget_key, {})
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if not data:
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return ""
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try:
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if widget_key == "dcf":
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return _DCF_TEMPLATE.format(**data)
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if widget_key == "financials":
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return _FINANCIALS_TEMPLATE.format(**data)
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if widget_key == "technical":
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return _TECHNICAL_TEMPLATE.format(**data)
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if widget_key == "portfolio":
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return _PORTFOLIO_TEMPLATE.format(**data)
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if widget_key == "filing":
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return _FILING_TEMPLATE.format(**data)
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except (KeyError, ValueError, TypeError) as exc:
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# Gracefully degrade -- partial data is fine
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return f"\n## {widget.title()} Context\nPartial data: {data}\n"
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# Unknown widget -- dump raw data as a summary
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return f"\n## {widget.title()} Context\n{data}\n"
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# ---------------------------------------------------------------------------
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# Singleton
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# ---------------------------------------------------------------------------
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context_builder = ContextBuilder()
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