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https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
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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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"""Technical analysis router -- indicators, chart data, Fibonacci, Ichimoku."""
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from typing import Any, Dict, List, Optional
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from fastapi import APIRouter, HTTPException, Query
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router = APIRouter()
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@router.get(
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"/{ticker}/indicators",
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summary="Technical indicators (RSI, SMA, EMA, MACD, BB, ATR)",
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)
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async def technical_indicators(ticker: str) -> Dict[str, Any]:
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"""Calculate and return common technical indicators for *ticker*.
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Returns RSI(14), SMA(20/50/200), EMA(12/26), MACD with signal and
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histogram, Bollinger Bands (20,2), and ATR(14).
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"""
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try:
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import yfinance as yf
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import ta # type: ignore[import-untyped]
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df = yf.download(ticker.upper(), period="1y", interval="1d", progress=False)
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if df.empty:
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raise HTTPException(status_code=404, detail=f"No data for {ticker}")
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# Flatten MultiIndex columns if present
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if hasattr(df.columns, "nlevels") and df.columns.nlevels > 1:
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df.columns = df.columns.get_level_values(0)
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close = df["Close"]
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high = df["High"]
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low = df["Low"]
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# RSI
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rsi_indicator = ta.momentum.RSIIndicator(close=close, window=14)
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rsi_val = rsi_indicator.rsi().iloc[-1]
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# SMA
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sma_20 = close.rolling(window=20).mean().iloc[-1]
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sma_50 = close.rolling(window=50).mean().iloc[-1]
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sma_200 = close.rolling(window=200).mean().iloc[-1] if len(close) >= 200 else None
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# EMA
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ema_12 = close.ewm(span=12, adjust=False).mean().iloc[-1]
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ema_26 = close.ewm(span=26, adjust=False).mean().iloc[-1]
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# MACD
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macd_indicator = ta.trend.MACD(close=close)
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macd_line = macd_indicator.macd().iloc[-1]
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macd_signal = macd_indicator.macd_signal().iloc[-1]
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macd_hist = macd_indicator.macd_diff().iloc[-1]
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# Bollinger Bands
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bb = ta.volatility.BollingerBands(close=close, window=20, window_dev=2)
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bb_upper = bb.bollinger_hband().iloc[-1]
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bb_middle = bb.bollinger_mavg().iloc[-1]
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bb_lower = bb.bollinger_lband().iloc[-1]
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# ATR
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atr_indicator = ta.volatility.AverageTrueRange(
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high=high, low=low, close=close, window=14,
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)
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atr_val = atr_indicator.average_true_range().iloc[-1]
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current_price = float(close.iloc[-1])
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return {
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"ticker": ticker.upper(),
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"current_price": current_price,
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"rsi_14": round(float(rsi_val), 2),
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"sma": {
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"sma_20": round(float(sma_20), 2),
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"sma_50": round(float(sma_50), 2),
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"sma_200": round(float(sma_200), 2) if sma_200 is not None else None,
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},
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"ema": {
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"ema_12": round(float(ema_12), 2),
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"ema_26": round(float(ema_26), 2),
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},
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"macd": {
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"macd": round(float(macd_line), 4),
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"signal": round(float(macd_signal), 4),
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"histogram": round(float(macd_hist), 4),
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},
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"bollinger_bands": {
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"upper": round(float(bb_upper), 2),
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"middle": round(float(bb_middle), 2),
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"lower": round(float(bb_lower), 2),
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},
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"atr_14": round(float(atr_val), 2),
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}
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except HTTPException:
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raise
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except Exception as exc:
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raise HTTPException(
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status_code=500,
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detail=f"Technical indicators failed: {exc}",
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) from exc
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@router.get(
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"/{ticker}/chart-data",
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summary="OHLCV data for charting",
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)
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async def chart_data(
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ticker: str,
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period: str = Query(
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default="6mo",
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description="Data period: 1d,5d,1mo,3mo,6mo,1y,2y,5y",
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),
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interval: str = Query(
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default="1d",
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description="Data interval: 1m,5m,15m,1h,1d,1wk",
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),
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) -> Dict[str, Any]:
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"""Return OHLCV data formatted for TradingView Lightweight Charts.
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Each bar is ``{time, open, high, low, close, volume}``.
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"""
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try:
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import yfinance as yf
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valid_periods = {"1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y"}
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valid_intervals = {"1m", "5m", "15m", "1h", "1d", "1wk"}
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if period not in valid_periods:
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raise HTTPException(
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status_code=400,
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detail=f"Invalid period '{period}'. Must be one of {valid_periods}",
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)
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if interval not in valid_intervals:
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raise HTTPException(
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status_code=400,
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detail=f"Invalid interval '{interval}'. Must be one of {valid_intervals}",
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)
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df = yf.download(
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ticker.upper(),
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period=period,
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interval=interval,
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progress=False,
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)
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if df.empty:
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raise HTTPException(status_code=404, detail=f"No data for {ticker}")
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# Flatten MultiIndex columns if present
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if hasattr(df.columns, "nlevels") and df.columns.nlevels > 1:
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df.columns = df.columns.get_level_values(0)
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bars: List[Dict[str, Any]] = []
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for idx, row in df.iterrows():
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time_str = str(idx)[:10] if interval in {"1d", "1wk"} else str(idx)
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bars.append({
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"time": time_str,
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"open": round(float(row["Open"]), 4),
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"high": round(float(row["High"]), 4),
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"low": round(float(row["Low"]), 4),
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"close": round(float(row["Close"]), 4),
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"volume": int(row["Volume"]),
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})
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return {
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"ticker": ticker.upper(),
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"period": period,
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"interval": interval,
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"bars": bars,
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}
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except HTTPException:
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raise
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except Exception as exc:
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raise HTTPException(
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status_code=500,
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detail=f"Chart data fetch failed: {exc}",
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) from exc
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@router.get(
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"/{ticker}/fibonacci",
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summary="Fibonacci retracement levels",
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)
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async def fibonacci_levels(ticker: str) -> Dict[str, Any]:
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"""Return Fibonacci retracement levels based on the 52-week high and low.
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Levels: 0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%.
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"""
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try:
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import yfinance as yf
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df = yf.download(ticker.upper(), period="1y", interval="1d", progress=False)
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if df.empty:
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raise HTTPException(status_code=404, detail=f"No data for {ticker}")
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# Flatten MultiIndex columns if present
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if hasattr(df.columns, "nlevels") and df.columns.nlevels > 1:
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df.columns = df.columns.get_level_values(0)
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high_52w: float = float(df["High"].max())
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low_52w: float = float(df["Low"].min())
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diff: float = high_52w - low_52w
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ratios = [0.0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0]
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levels: Dict[str, float] = {}
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for r in ratios:
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label = f"{r * 100:.1f}%"
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levels[label] = round(high_52w - diff * r, 2)
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current_price = float(df["Close"].iloc[-1])
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return {
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"ticker": ticker.upper(),
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"high_52w": round(high_52w, 2),
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"low_52w": round(low_52w, 2),
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"current_price": round(current_price, 2),
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"levels": levels,
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}
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except HTTPException:
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raise
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except Exception as exc:
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raise HTTPException(
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status_code=500,
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detail=f"Fibonacci levels failed: {exc}",
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) from exc
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@router.get(
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"/{ticker}/ichimoku",
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summary="Ichimoku cloud data",
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)
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async def ichimoku_cloud(ticker: str) -> Dict[str, Any]:
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"""Return Ichimoku cloud components for *ticker*.
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Components: Tenkan-sen (9), Kijun-sen (26), Senkou Span A,
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Senkou Span B (52), and Chikou Span.
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"""
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try:
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import yfinance as yf
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import pandas as pd
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df = yf.download(ticker.upper(), period="1y", interval="1d", progress=False)
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if df.empty:
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raise HTTPException(status_code=404, detail=f"No data for {ticker}")
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# Flatten MultiIndex columns if present
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if hasattr(df.columns, "nlevels") and df.columns.nlevels > 1:
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df.columns = df.columns.get_level_values(0)
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high = df["High"]
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low = df["Low"]
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close = df["Close"]
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# Tenkan-sen (Conversion Line): (9-period high + 9-period low) / 2
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nine_high = high.rolling(window=9).max()
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nine_low = low.rolling(window=9).min()
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tenkan = (nine_high + nine_low) / 2
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# Kijun-sen (Base Line): (26-period high + 26-period low) / 2
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k_high = high.rolling(window=26).max()
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k_low = low.rolling(window=26).min()
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kijun = (k_high + k_low) / 2
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# Senkou Span A (Leading Span A): (Tenkan + Kijun) / 2, shifted 26
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senkou_a = ((tenkan + kijun) / 2).shift(26)
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# Senkou Span B (Leading Span B): (52-period high + low) / 2, shifted 26
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b_high = high.rolling(window=52).max()
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b_low = low.rolling(window=52).min()
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senkou_b = ((b_high + b_low) / 2).shift(26)
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# Chikou Span (Lagging Span): Close shifted back 26 periods
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chikou = close.shift(-26)
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# Take last 100 data points for response
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n = min(100, len(df))
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dates = [str(d)[:10] for d in df.index[-n:]]
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def _to_list(series: pd.Series) -> List[Optional[float]]:
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"""Convert the last *n* values of a series to a list of floats."""
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vals = series.iloc[-n:]
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result: List[Optional[float]] = []
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for v in vals:
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try:
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result.append(round(float(v), 2))
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except (ValueError, TypeError):
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result.append(None)
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return result
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return {
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"ticker": ticker.upper(),
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"dates": dates,
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"tenkan_sen": _to_list(tenkan),
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"kijun_sen": _to_list(kijun),
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"senkou_span_a": _to_list(senkou_a),
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"senkou_span_b": _to_list(senkou_b),
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"chikou_span": _to_list(chikou),
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"close": _to_list(close),
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}
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except HTTPException:
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raise
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except Exception as exc:
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raise HTTPException(
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status_code=500,
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detail=f"Ichimoku cloud failed: {exc}",
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) from exc
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