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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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"""AI Analysis router -- Gemini-powered financial analysis.
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Direct Gemini API calls without depending on Streamlit app module.
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"""
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import json
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import logging
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from typing import Optional
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from fastapi import APIRouter, HTTPException
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel, Field
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logger = logging.getLogger(__name__)
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router = APIRouter()
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class AnalysisRequest(BaseModel):
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ticker: str
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question: str = ""
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api_key: str = ""
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sector: str = ""
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industry: str = ""
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class SimpleQuestionRequest(BaseModel):
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ticker: str
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question: str
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api_key: str = ""
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def _call_gemini(api_key: str, prompt: str, max_tokens: int = 4096) -> str:
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"""Call Gemini API directly and return text response."""
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import urllib.request
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import urllib.error
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url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}"
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payload = json.dumps({
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"contents": [{"parts": [{"text": prompt}]}],
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"generationConfig": {"maxOutputTokens": max_tokens, "temperature": 0.7}
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}).encode("utf-8")
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req = urllib.request.Request(url, data=payload, headers={"Content-Type": "application/json"})
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try:
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with urllib.request.urlopen(req, timeout=60) as resp:
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data = json.loads(resp.read().decode("utf-8"))
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candidates = data.get("candidates", [])
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if candidates:
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parts = candidates[0].get("content", {}).get("parts", [])
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if parts:
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return parts[0].get("text", "")
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return "No response from Gemini."
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except urllib.error.HTTPError as e:
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body = e.read().decode("utf-8", errors="replace")
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logger.error("Gemini API error %d: %s", e.code, body)
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raise HTTPException(status_code=e.code, detail=f"Gemini API error: {body[:200]}")
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Gemini call failed: {e}")
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def _get_financial_context(ticker: str) -> str:
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"""Build financial context from yfinance for AI analysis."""
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try:
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import yfinance as yf
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t = yf.Ticker(ticker)
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info = t.info or {}
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ctx = f"""Company: {info.get('longName', ticker)} ({ticker})
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Sector: {info.get('sector', 'N/A')} | Industry: {info.get('industry', 'N/A')}
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Market Cap: ${info.get('marketCap', 0)/1e9:.1f}B
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Revenue: ${info.get('totalRevenue', 0)/1e9:.1f}B | Revenue Growth: {(info.get('revenueGrowth', 0) or 0)*100:.1f}%
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Profit Margin: {(info.get('profitMargins', 0) or 0)*100:.1f}% | Gross Margin: {(info.get('grossMargins', 0) or 0)*100:.1f}%
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ROE: {(info.get('returnOnEquity', 0) or 0)*100:.1f}% | ROA: {(info.get('returnOnAssets', 0) or 0)*100:.1f}%
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D/E: {info.get('debtToEquity', 'N/A')} | Current Ratio: {info.get('currentRatio', 'N/A')}
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P/E: {info.get('trailingPE', 'N/A')} | Forward P/E: {info.get('forwardPE', 'N/A')}
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Price: ${info.get('currentPrice', 'N/A')} | 52W High: ${info.get('fiftyTwoWeekHigh', 'N/A')} | 52W Low: ${info.get('fiftyTwoWeekLow', 'N/A')}
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Target Mean: ${info.get('targetMeanPrice', 'N/A')} | Recommendation: {info.get('recommendationKey', 'N/A')}
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Free Cash Flow: ${info.get('freeCashflow', 0)/1e9:.1f}B
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"""
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return ctx
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except Exception:
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return f"Ticker: {ticker}"
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@router.post("/strategy", summary="AI financial analysis")
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async def strategy_analysis(req: AnalysisRequest):
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"""General AI financial analysis using Gemini."""
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api_key = req.api_key
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if not api_key:
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raise HTTPException(status_code=400, detail="API key required. Set your Gemini key in Settings.")
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context = _get_financial_context(req.ticker.upper())
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question = req.question or f"Provide a comprehensive financial analysis of {req.ticker.upper()}"
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prompt = f"""You are an expert financial analyst. Analyze the following company and answer the user's question.
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{context}
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User Question: {question}
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Provide a detailed, professional analysis in markdown format. Include:
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- Key financial metrics assessment
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- Strengths and weaknesses
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- Valuation perspective
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- Risk factors
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- Your overall assessment
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Be specific with numbers and data. Answer in the same language as the question."""
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result = _call_gemini(api_key, prompt)
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return {"ticker": req.ticker.upper(), "analysis": result}
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@router.post("/risks", summary="Risk analysis")
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async def risk_analysis(req: AnalysisRequest):
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"""AI-powered risk analysis."""
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api_key = req.api_key
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if not api_key:
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raise HTTPException(status_code=400, detail="API key required.")
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context = _get_financial_context(req.ticker.upper())
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prompt = f"""You are a risk analyst. Analyze the following company's risk factors:
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{context}
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Provide a detailed risk assessment including:
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1. Financial risks (leverage, liquidity, profitability trends)
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2. Market risks (valuation, competition, sector headwinds)
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3. Operational risks
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4. Regulatory risks
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5. Overall risk rating (Low/Medium/High)
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Be specific and use the financial data provided. Answer in markdown format."""
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result = _call_gemini(api_key, prompt)
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return {"ticker": req.ticker.upper(), "analysis": result}
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@router.post("/mda", summary="MD&A analysis")
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async def mda_insights(req: AnalysisRequest):
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"""AI management discussion analysis."""
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api_key = req.api_key
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if not api_key:
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raise HTTPException(status_code=400, detail="API key required.")
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context = _get_financial_context(req.ticker.upper())
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prompt = f"""Analyze the management perspective for this company:
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{context}
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Provide insights on:
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1. Revenue drivers and growth strategy
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2. Margin trends and cost management
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3. Capital allocation priorities
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4. Key management concerns
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5. Future outlook
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Use markdown format with headers and bullet points."""
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result = _call_gemini(api_key, prompt)
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return {"ticker": req.ticker.upper(), "report": result}
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@router.post("/forensic", summary="Forensic audit")
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async def forensic_audit(req: AnalysisRequest):
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"""AI forensic audit analysis."""
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api_key = req.api_key
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if not api_key:
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raise HTTPException(status_code=400, detail="API key required.")
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context = _get_financial_context(req.ticker.upper())
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prompt = f"""Perform a forensic financial audit on this company:
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{context}
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Check for:
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1. Earnings quality (cash flow vs net income)
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2. Aggressive accounting signs
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3. Related party transactions
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4. Off-balance sheet items
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5. Revenue recognition concerns
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6. Management compensation alignment
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Use markdown format. Be thorough but fair."""
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result = _call_gemini(api_key, prompt)
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return {"ticker": req.ticker.upper(), "forensic": result}
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@router.post("/financials", summary="Extract financials via LLM")
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async def extract_financials(req: AnalysisRequest):
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"""Use Gemini to provide financial analysis."""
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api_key = req.api_key
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if not api_key:
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raise HTTPException(status_code=400, detail="API key required.")
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context = _get_financial_context(req.ticker.upper())
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result = _call_gemini(api_key, f"Summarize the key financial data for analysis:\n\n{context}")
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return {"ticker": req.ticker.upper(), "financials": result}
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