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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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"""
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Tab 9 — Portfolio Management
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Full-featured portfolio dashboard: holdings table, sector pie chart,
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earnings calendar, dividends, key events, news, AI OCR import.
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"""
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import streamlit as st
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import pandas as pd
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from data.portfolio import get_portfolio_prices, get_sector_allocation
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from views.portfolio_widgets import render_earnings, render_dividends, render_news
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try:
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import plotly.express as px
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except ImportError:
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px = None
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# ───────── Session State Init ─────────
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def _init_portfolio():
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if "portfolio_holdings" not in st.session_state:
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st.session_state["portfolio_holdings"] = []
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# ───────── Manual Entry Form ─────────
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def _render_add_form():
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"""Manual stock entry form."""
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st.markdown("#### Add Holding")
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cols = st.columns([2, 1, 1, 1])
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with cols[0]:
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ticker = st.text_input("Ticker", placeholder="AAPL", key="pf_add_ticker")
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with cols[1]:
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name = st.text_input("Name", placeholder="Apple Inc", key="pf_add_name")
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with cols[2]:
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shares = st.number_input("Shares", min_value=0.0, step=0.01, key="pf_add_shares")
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with cols[3]:
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avg_cost = st.number_input("Avg Cost ($)", min_value=0.0, step=0.01, key="pf_add_avg")
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if st.button("Add to Portfolio", key="pf_add_btn"):
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if ticker.strip():
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st.session_state["portfolio_holdings"].append({
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"ticker": ticker.strip().upper(),
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"name": name.strip() or ticker.strip().upper(),
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"shares": shares, "avg_cost": avg_cost,
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})
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st.rerun()
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# ───────── AI OCR Import ─────────
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def _render_ai_import():
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"""AI-powered screenshot import for Trading 212 / IBKR."""
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st.markdown("#### Import from Brokerage Screenshot")
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google_api_key = (st.session_state.get("google_api_key") or "").strip()
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if not google_api_key:
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st.info("Enter your Google API Key in the sidebar to enable AI portfolio import.")
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return
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broker = st.selectbox("Broker", ["Trading 212", "IBKR", "Other"], key="pf_broker")
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uploaded = st.file_uploader(
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"Upload screenshot", type=["png", "jpg", "jpeg", "webp"],
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key="pf_screenshot", help="Upload a screenshot of your portfolio holdings"
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)
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if uploaded and st.button("Extract Holdings with AI", key="pf_extract_btn", type="primary"):
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with st.spinner("Gemini is analyzing your screenshot..."):
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from ai.gemini_portfolio import extract_portfolio_from_image
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holdings = extract_portfolio_from_image(google_api_key, uploaded.read(), broker)
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if holdings:
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for h in holdings:
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st.session_state["portfolio_holdings"].append({
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"ticker": h["ticker"], "name": h.get("name", h["ticker"]),
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"shares": h.get("shares") or 0,
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"avg_cost": h.get("avg_cost") or h.get("current_price") or 0,
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})
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st.success(f"Extracted {len(holdings)} holdings!")
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st.rerun()
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else:
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st.warning("No holdings found. Try a clearer screenshot.")
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# ───────── Portfolio Overview Cards ─────────
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def _render_overview(holdings: list, prices: dict):
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"""Total asset value, daily P&L, total P&L cards."""
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total_value, total_cost, daily_pnl = 0.0, 0.0, 0.0
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for h in holdings:
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p = prices.get(h["ticker"], {})
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cur = p.get("price") or h.get("avg_cost") or 0
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prev = p.get("prev_close") or cur
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shares = h.get("shares", 0)
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total_value += cur * shares
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total_cost += h.get("avg_cost", 0) * shares
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daily_pnl += (cur - prev) * shares
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total_pnl = total_value - total_cost
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pnl_pct = (total_pnl / total_cost * 100) if total_cost > 0 else 0
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c1, c2, c3 = st.columns(3)
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with c1:
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st.metric("Total Portfolio Value", f"${total_value:,.2f}")
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with c2:
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st.metric("Daily P&L", f"${daily_pnl:,.2f}", delta=f"{daily_pnl:+,.2f}")
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with c3:
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st.metric("Total P&L", f"${total_pnl:,.2f}", delta=f"{pnl_pct:+.2f}%")
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# ───────── Holdings Table ─────────
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def _render_holdings_table(holdings: list, prices: dict):
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"""Display holdings with current price, change, P&L."""
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if not holdings:
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return
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st.markdown("#### Holdings")
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rows = []
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for i, h in enumerate(holdings):
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sym = h["ticker"]
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p = prices.get(sym, {})
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cur = p.get("price")
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chg = p.get("change_pct", 0)
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shares, avg = h.get("shares", 0), h.get("avg_cost", 0)
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mkt = (cur or 0) * shares
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pnl = (cur - avg) * shares if cur and avg else 0
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pnl_p = ((cur - avg) / avg * 100) if cur and avg and avg > 0 else 0
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rows.append({
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"": i, "Ticker": sym, "Name": h.get("name", sym),
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"Shares": f"{shares:,.2f}" if shares != int(shares) else f"{int(shares):,}",
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"Avg Cost": f"${avg:,.2f}" if avg else "N/A",
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"Current": f"${cur:,.2f}" if cur else "N/A",
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"Day Chg": f"{chg:+.2f}%", "P&L": f"${pnl:+,.2f}",
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"P&L %": f"{pnl_p:+.2f}%", "Mkt Value": f"${mkt:,.2f}",
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})
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df = pd.DataFrame(rows).set_index("")
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st.dataframe(df, use_container_width=True, height=min(40 * len(rows) + 38, 500))
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with st.expander("Remove Holdings"):
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for i, h in enumerate(holdings):
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if st.button(f"Remove {h['ticker']}", key=f"pf_del_{i}"):
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st.session_state["portfolio_holdings"].pop(i)
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st.rerun()
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# ───────── Sector Pie Chart ─────────
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def _render_sector_pie(holdings: list):
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"""Sector allocation donut chart."""
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if not holdings or not px:
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return
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tickers = tuple(h["ticker"] for h in holdings)
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sectors = get_sector_allocation(tickers)
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agg = {}
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for h in holdings:
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sec = sectors.get(h["ticker"], "Other")
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agg[sec] = agg.get(sec, 0) + h.get("shares", 0) * h.get("avg_cost", 0)
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if not agg:
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return
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fig = px.pie(
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names=list(agg.keys()), values=list(agg.values()),
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title="Sector Allocation", hole=0.4,
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color_discrete_sequence=px.colors.qualitative.Set3,
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)
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fig.update_layout(
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paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
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font_color="#E5E7EB", height=350, margin=dict(t=40, b=20, l=20, r=20),
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)
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st.plotly_chart(fig, use_container_width=True)
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# ───────── Main Render ─────────
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def render_tab9():
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"""Render the Portfolio Management tab."""
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_init_portfolio()
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st.subheader("Portfolio Management")
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holdings = st.session_state["portfolio_holdings"]
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with st.expander("Add / Import Holdings", expanded=not bool(holdings)):
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t1, t2 = st.tabs(["Manual Entry", "AI Screenshot Import"])
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with t1:
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_render_add_form()
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with t2:
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_render_ai_import()
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if not holdings:
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st.info("Add holdings above to see your portfolio dashboard.")
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return
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tickers = tuple(h["ticker"] for h in holdings)
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with st.spinner("Fetching live prices..."):
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prices = get_portfolio_prices(tickers)
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_render_overview(holdings, prices)
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st.markdown("---")
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left, right = st.columns([3, 2])
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with left:
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_render_holdings_table(holdings, prices)
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_render_sector_pie(holdings)
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with right:
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render_earnings(tickers)
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st.markdown("---")
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render_dividends(tickers)
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st.markdown("---")
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render_news(tickers)
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