Full AlphaForge app with all 4 tabs - stock analysis, portfolio, AI chat, about

app.py

@@ -1,20 +1,507 @@
+"""AlphaForge x K2 Think V2 — Elite Quant Trading Demo
+Full-featured interactive app for the Build with K2 Think V2 Challenge.
+"""
 import os
 import gradio as gr
+import requests
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 
 K2_KEY = os.environ.get("K2_API_KEY", "")
-K2_OK = bool(K2_KEY) and len(K2_KEY) > 5
-
-def greet(name):
-    k2_status = "✅ Connected" if K2_OK else "❌ Not configured (set K2_API_KEY secret)"
-    return f"Hello {name}! K2 Think V2 status: {k2_status}"
-
-with gr.Blocks(title="AlphaForge x K2 Think V2") as demo:
-    gr.Markdown("# 🔥 AlphaForge x K2 Think V2")
-    gr.Markdown("### Elite Quantitative Trading Platform")
-    
-    name = gr.Textbox(label="Your name", value="Trader")
-    output = gr.Textbox(label="Greeting")
-    button = gr.Button("Greet")
-    button.click(fn=greet, inputs=name, outputs=output)
-
-demo.launch()
+K2_URL = "https://api.k2think.ai/v1/chat/completions"
+K2_MODEL = "MBZUAI-IFM/K2-Think-v2"
+
+# ═══════════════════════════════════════════════════════════════
+# K2 THINK V2 CLIENT
+# ═══════════════════════════════════════════════════════════════
+class K2Client:
+    def __init__(self):
+        self.key = K2_KEY
+        self.ok = bool(self.key) and len(self.key) > 5
+
+    def ask(self, prompt, temp=0.7):
+        if not self.ok:
+            return """⚠️ **K2 Think V2 API Not Configured**
+
+The AI features require an API key. To enable them:
+
+1. Go to **Space Settings** → **Repository secrets**
+2. Click **New secret**
+3. Name: `K2_API_KEY`
+4. Value: `IFM-4SpQ0qEg0Wlsw04O`
+5. Click **Save**, then **Factory Rebuild**
+
+✅ All technical analysis, charts, and portfolio tools work **without** the API key!"""
+        try:
+            r = requests.post(
+                K2_URL,
+                headers={
+                    "Authorization": f"Bearer {self.key}",
+                    "Content-Type": "application/json"
+                },
+                json={
+                    "model": K2_MODEL,
+                    "messages": [{"role": "user", "content": prompt}],
+                    "temperature": temp,
+                    "max_tokens": 4096,
+                    "stream": False
+                },
+                timeout=90
+            )
+            r.raise_for_status()
+            j = r.json()
+            return j.get("choices", [{}])[0].get("message", {}).get("content", "No response")
+        except requests.exceptions.Timeout:
+            return "⏱️ K2 API timed out. Please try again."
+        except requests.exceptions.HTTPError as e:
+            if e.response.status_code == 401:
+                return "🔐 API key invalid. Please check K2_API_KEY secret."
+            elif e.response.status_code == 429:
+                return "🚦 Rate limit hit. Please wait a moment."
+            return f"🔴 HTTP Error {e.response.status_code}: {str(e)[:200]}"
+        except Exception as e:
+            return f"🔴 Error: {str(e)[:200]}. All other features work fine!"
+
+# ═══════════════════════════════════════════════════════════════
+# MARKET DATA
+# ═══════════════════════════════════════════════════════════════
+def fetch_data(ticker, period="6mo"):
+    try:
+        df = yf.Ticker(ticker.upper().strip()).history(period=period)
+        if df.empty:
+            return None, f"No data for '{ticker}'. Try: AAPL, TSLA, SPY, BTC-USD, ETH-USD"
+        return df, None
+    except Exception as e:
+        return None, f"Error fetching '{ticker}': {str(e)[:200]}"
+
+# ═══════════════════════════════════════════════════════════════
+# TECHNICAL INDICATORS
+# ═══════════════════════════════════════════════════════════════
+def calc_indicators(df):
+    df = df.copy()
+    df['Ret'] = df['Close'].pct_change()
+    df['SMA20'] = df['Close'].rolling(20).mean()
+    df['SMA50'] = df['Close'].rolling(50).mean()
+    df['EMA12'] = df['Close'].ewm(span=12, adjust=False).mean()
+    df['EMA26'] = df['Close'].ewm(span=26, adjust=False).mean()
+    df['MACD'] = df['EMA12'] - df['EMA26']
+    df['MACDS'] = df['MACD'].ewm(span=9, adjust=False).mean()
+    df['MACDH'] = df['MACD'] - df['MACDS']
+    d = df['Close'].diff()
+    g = d.where(d > 0, 0).rolling(14).mean()
+    l = (-d.where(d < 0, 0)).rolling(14).mean()
+    df['RSI'] = 100 - (100 / (1 + g / (l + 1e-10)))
+    m = df['Close'].rolling(20).mean()
+    s = df['Close'].rolling(20).std()
+    df['BBU'] = m + 2*s
+    df['BBL'] = m - 2*s
+    df['BBP'] = (df['Close'] - df['BBL']) / (df['BBU'] - df['BBL'] + 1e-10)
+    tp = (df['High'] + df['Low'] + df['Close']) / 3
+    df['VWAP'] = (tp * df['Volume']).cumsum() / (df['Volume'].cumsum() + 1e-10)
+    tr = pd.concat([df['High']-df['Low'], np.abs(df['High']-df['Close'].shift()), np.abs(df['Low']-df['Close'].shift())], axis=1).max(axis=1)
+    df['ATR'] = tr.rolling(14).mean()
+    lo = df['Low'].rolling(14).min()
+    hi = df['High'].rolling(14).max()
+    df['SK'] = 100 * (df['Close'] - lo) / (hi - lo + 1e-10)
+    df['VM'] = df['Volume'].rolling(20).mean()
+    df['VR'] = df['Volume'] / (df['VM'] + 1e-10)
+    return df
+
+def calc_risk(df):
+    r = df['Ret'].dropna()
+    if len(r) < 30:
+        return {}
+    ar = r.mean() * 252
+    av = r.std() * np.sqrt(252)
+    sh = ar / (av + 1e-10)
+    dn = r[r < 0]
+    sd = dn.std() * np.sqrt(252) if len(dn) > 0 else 1e-10
+    so = ar / (sd + 1e-10)
+    c = (1 + r).cumprod()
+    rm = c.expanding().max()
+    md = ((c - rm) / rm).min()
+    v95 = np.percentile(r, 5)
+    cv95 = r[r <= v95].mean() if len(r[r <= v95]) > 0 else v95
+    ca = ar / (abs(md) + 1e-10)
+    return {'ar': ar, 'av': av, 'sh': sh, 'so': so, 'md': md,
+            'v95': v95, 'cv95': cv95, 'ca': ca,
+            'sk': r.skew(), 'ku': r.kurtosis(),
+            'wr': (r > 0).mean(), 'pf': abs(r[r > 0].sum() / (r[r < 0].sum() + 1e-10))}
+
+def calc_signals(df):
+    l = df.iloc[-1]
+    p = df.iloc[-2] if len(df) > 1 else l
+    s = {'trend': 'neutral', 'mom': 'neutral', 'score': 50}
+    if l['Close'] > l['SMA20'] > l['SMA50']:
+        s['trend'] = 'bullish'
+    elif l['Close'] < l['SMA20'] < l['SMA50']:
+        s['trend'] = 'bearish'
+    if l['RSI'] < 30:
+        s['mom'] = 'oversold'
+    elif l['RSI'] > 70:
+        s['mom'] = 'overbought'
+    elif l['MACD'] > l['MACDS'] and p['MACD'] <= p['MACDS']:
+        s['mom'] = 'bullish crossover'
+    elif l['MACD'] < l['MACDS'] and p['MACD'] >= p['MACDS']:
+        s['mom'] = 'bearish crossover'
+    sc = 50
+    if s['trend'] == 'bullish': sc += 15
+    if s['trend'] == 'bearish': sc -= 15
+    if 'oversold' in s['mom']: sc += 10
+    if 'overbought' in s['mom']: sc -= 10
+    if 'bullish crossover' in s['mom']: sc += 10
+    if 'bearish crossover' in s['mom']: sc -= 10
+    if l['Close'] > l['VWAP']: sc += 5
+    if l['Close'] < l['VWAP']: sc -= 5
+    s['score'] = max(0, min(100, sc))
+    s['dir'] = '🟢 BULLISH' if sc > 60 else '🔴 BEARISH' if sc < 40 else '⚪ NEUTRAL'
+    return s
+
+# ═══════════════════════════════════════════════════════════════
+# CHARTS
+# ═══════════════════════════════════════════════════════════════
+def make_candle(df, ticker):
+    fig = make_subplots(
+        rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.03,
+        row_heights=[0.65, 0.2, 0.15],
+        subplot_titles=(f'{ticker} Price', 'Volume', 'RSI')
+    )
+    colors = ['green' if df['Close'].iloc[i] >= df['Open'].iloc[i] else 'red' for i in range(len(df))]
+    fig.add_trace(go.Candlestick(x=df.index, open=df['Open'], high=df['High'], low=df['Low'], close=df['Close'], name='Price'), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['SMA20'], line=dict(color='orange', width=1), name='SMA20', legendgroup='1'), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['SMA50'], line=dict(color='blue', width=1), name='SMA50', legendgroup='1'), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['BBU'], line=dict(color='gray', dash='dash', width=1), name='BB+', opacity=0.4), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['BBL'], line=dict(color='gray', dash='dash', width=1), name='BB-', opacity=0.4), row=1, col=1)
+    fig.add_trace(go.Bar(x=df.index, y=df['Volume'], marker_color=colors, name='Volume', opacity=0.6), row=2, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['RSI'], line=dict(color='purple', width=1.5), name='RSI', legendgroup='2'), row=3, col=1)
+    fig.add_hline(y=70, line_dash="dash", line_color="red", row=3, col=1)
+    fig.add_hline(y=30, line_dash="dash", line_color="green", row=3, col=1)
+    fig.update_layout(height=750, template='plotly_white', showlegend=False, xaxis_rangeslider_visible=False)
+    fig.update_yaxes(title_text="Price", row=1, col=1)
+    fig.update_yaxes(title_text="Volume", row=2, col=1)
+    fig.update_yaxes(title_text="RSI", range=[0, 100], row=3, col=1)
+    return fig
+
+def make_macd(df, ticker):
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df.index, y=df['MACD'], line=dict(color='blue', width=1.5), name='MACD'))
+    fig.add_trace(go.Scatter(x=df.index, y=df['MACDS'], line=dict(color='orange', width=1.5), name='Signal'))
+    fig.add_trace(go.Bar(x=df.index, y=df['MACDH'], marker_color=['green' if v >= 0 else 'red' for v in df['MACDH']], name='Histogram', opacity=0.5))
+    fig.update_layout(title=f'{ticker} MACD', height=350, template='plotly_white', showlegend=False)
+    return fig
+
+def make_dist(r, ticker):
+    fig = go.Figure()
+    fig.add_trace(go.Histogram(x=r, nbinsx=50, marker_color='steelblue', opacity=0.7, name='Returns'))
+    fig.update_layout(title=f'{ticker} Return Distribution', xaxis_title='Daily Return', yaxis_title='Frequency', height=350, template='plotly_white', bargap=0.1)
+    return fig
+
+# ═══════════════════════════════════════════════════════════════
+# ANALYZE STOCK
+# ═══════════════════════════════════════════════════════════════
+def analyze_stock(ticker, period="6mo"):
+    ticker = ticker.strip().upper()
+    if not ticker:
+        return None, None, None, "## ❌ Please enter a ticker symbol (e.g., AAPL, TSLA, SPY, BTC-USD)."
+    df, err = fetch_data(ticker, period)
+    if err:
+        return None, None, None, f"## ❌ {err}"
+    df = calc_indicators(df)
+    sg = calc_signals(df)
+    rk = calc_risk(df)
+    if not rk:
+        return None, None, None, "## ❌ Insufficient data. Try a longer period (e.g., 6mo or 1y)."
+    l = df.iloc[-1]
+    p = df.iloc[-2] if len(df) > 1 else l
+    ch = ((l['Close']/p['Close']-1)*100) if p['Close'] > 0 else 0
+
+    md = f"""# {ticker} — {sg['dir']} (Score: {sg['score']}/100)
+
+**Price:** ${l['Close']:.2f} | **Change:** {ch:+.2f}% | **Period:** {period}
+
+| Indicator | Value | Signal |
+|-----------|-------|--------|
+| RSI | {l['RSI']:.1f} | {'🟢 Oversold' if l['RSI']<30 else '🔴 Overbought' if l['RSI']>70 else '⚪ Neutral'} |
+| MACD | {l['MACD']:.3f} | {'🟢 Bullish' if l['MACD']>l['MACDS'] else '🔴 Bearish'} |
+| Bollinger | {l['BBP']:.1%} | {'🔴 Upper' if l['BBP']>0.8 else '🟢 Lower' if l['BBP']<0.2 else '⚪ Mid'} |
+| VWAP | {'🟢 Above' if l['Close']>l['VWAP'] else '🔴 Below'} | {'🟢 Bullish' if l['Close']>l['VWAP'] else '🔴 Bearish'} |
+| Volume | {l['VR']:.1f}x avg | {'🔥 Heavy' if l['VR']>1.5 else '⚪ Normal'} |
+| Trend | {sg['trend'].upper()} | — |
+| Momentum | {sg['mom']} | — |
+
+## Risk Metrics
+| Metric | Value |
+|--------|-------|
+| Ann. Return | {rk['ar']*100:.1f}% |
+| Ann. Volatility | {rk['av']*100:.1f}% |
+| Sharpe | {rk['sh']:.2f} |
+| Sortino | {rk['so']:.2f} |
+| Max Drawdown | {rk['md']*100:.1f}% |
+| VaR (95%) | {rk['v95']*100:.2f}% |
+| CVaR (95%) | {rk['cv95']*100:.2f}% |
+| Calmar | {rk['ca']:.2f} |
+| Win Rate | {rk['wr']*100:.1f}% |
+| Profit Factor | {rk['pf']:.2f} |
+| Skewness | {rk['sk']:.2f} |
+| Kurtosis | {rk['ku']:.2f} |
+"""
+    return make_candle(df, ticker), make_macd(df, ticker), make_dist(df['Ret'].dropna(), ticker), md
+
+def ai_analyze(ticker, period="6mo"):
+    ticker = ticker.strip().upper()
+    if not ticker:
+        return "Enter a ticker symbol."
+    df, err = fetch_data(ticker, period)
+    if err:
+        return f"Error: {err}"
+    df = calc_indicators(df)
+    sg = calc_signals(df)
+    rk = calc_risk(df)
+    l = df.iloc[-1]
+    ds = f"Ticker: {ticker}\nPrice: ${l['Close']:.2f}\nSMA20: ${l['SMA20']:.2f}\nSMA50: ${l['SMA50']:.2f}\n52W High: ${df['High'].max():.2f}\n52W Low: ${df['Low'].min():.2f}\nATR: ${l['ATR']:.2f}"
+    ts = f"RSI: {l['RSI']:.1f}\nMACD: {l['MACD']:.3f} vs Signal: {l['MACDS']:.3f}\nBBP: {l['BBP']:.1%}\nSK: {l['SK']:.1f}\nVWAP: ${l['VWAP']:.2f}\nScore: {sg['score']}/100 | Dir: {sg['dir']}\nSharpe: {rk.get('sh',0):.2f}\nVol: {rk.get('av',0)*100:.1f}%\nMaxDD: {rk.get('md',0)*100:.1f}%\nVaR95: {rk.get('v95',0)*100:.2f}%"
+    prompt = f"""You are an elite quantitative analyst at Jane Street/Two Sigma. Analyze with deep reasoning.
+
+TICKER: {ticker}
+
+DATA: {ds}
+
+TECHNICAL: {ts}
+
+Provide:
+1. Executive Summary (3 bullets)
+2. Technical Analysis — interpret RSI, MACD, Bollinger, VWAP
+3. Risk Assessment — volatility regime, VaR, tail risks
+4. Alpha Signal — Bullish/Neutral/Bearish with confidence %
+5. Trade Idea — entry price, stop-loss, target price
+6. Catalyst Watch — events that could move the stock
+
+Think step-by-step."""
+    return K2Client().ask(prompt, temp=0.3)
+
+# ═══════════════════════════════════════════════════════════════
+# PORTFOLIO
+# ═══════════════════════════════════════════════════════════════
+def opt_portfolio(tickers, period="1y"):
+    ts = [t.strip().upper() for t in tickers.split(',') if t.strip()]
+    if len(ts) < 2:
+        return None, None, "## ❌ Enter at least 2 tickers separated by commas (e.g., AAPL, MSFT, GOOGL)."
+    data = {}
+    errs = []
+    for t in ts:
+        df, err = fetch_data(t, period)
+        if err:
+            errs.append(err)
+        elif df is not None and len(df) > 30:
+            data[t] = df['Close']
+    if len(data) < 2:
+        return None, None, f"## ❌ Could not fetch enough data. Errors: {'; '.join(errs[:3])}"
+    prices = pd.DataFrame(data).dropna()
+    returns = prices.pct_change().dropna()
+    if len(returns) < 30:
+        return None, None, "## ❌ Insufficient aligned data. Try different tickers or a longer period."
+    mu = returns.mean() * 252
+    sigma = returns.cov() * 252
+    n = len(mu)
+    best_sh = -999
+    best_w = np.ones(n) / n
+    np.random.seed(42)
+    for _ in range(3000):
+        w = np.random.dirichlet(np.ones(n) * 0.5)
+        w = np.clip(w, 0, 0.5)
+        w = w / w.sum()
+        pr = np.dot(w, mu)
+        pv = np.sqrt(np.dot(w.T, np.dot(sigma, w)))
+        sh = pr / (pv + 1e-10)
+        if sh > best_sh:
+            best_sh = sh
+            best_w = w
+    pr = np.dot(best_w, mu)
+    pv = np.sqrt(np.dot(best_w.T, np.dot(sigma, best_w)))
+    eq_w = np.ones(n) / n
+    eq_r = np.dot(eq_w, mu)
+    eq_v = np.sqrt(np.dot(eq_w.T, np.dot(sigma, eq_w)))
+
+    # Efficient frontier
+    ws = np.random.dirichlet(np.ones(n) * 0.5, 2000)
+    ws = np.clip(ws, 0, 0.5)
+    ws = ws / ws.sum(axis=1, keepdims=True)
+    prets = np.dot(ws, mu)
+    pvols = np.array([np.sqrt(np.dot(w.T, np.dot(sigma, w))) for w in ws])
+    psh = prets / (pvols + 1e-10)
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=pvols, y=prets, mode='markers',
+        marker=dict(size=4, color=psh, colorscale='Viridis', showscale=True, colorbar=dict(title='Sharpe')),
+        name='Portfolios'))
+    fig.add_trace(go.Scatter(x=[pv], y=[pr], mode='markers+text',
+        marker=dict(size=16, color='red', symbol='star'), text=['Optimal'], textposition='top center'))
+    fig.add_trace(go.Scatter(x=[eq_v], y=[eq_r], mode='markers+text',
+        marker=dict(size=12, color='orange', symbol='diamond'), text=['Equal'], textposition='bottom center'))
+    fig.update_layout(title='Efficient Frontier', xaxis_title='Volatility', yaxis_title='Return',
+                      template='plotly_white', height=500, showlegend=False)
+
+    wdf = pd.DataFrame({'Ticker': list(data.keys()), 'Optimal (%)': np.round(best_w * 100, 2), 'Equal (%)': np.round(eq_w * 100, 2)})
+
+    md = f"""# Portfolio Results
+
+**Tickers:** {', '.join(list(data.keys()))}
+
+| | Optimal | Equal Weight |
+|-|---------|-------------|
+| Expected Return | {pr*100:.1f}% | {eq_r*100:.1f}% |
+| Volatility | {pv*100:.1f}% | {eq_v*100:.1f}% |
+| Sharpe | {best_sh:.2f} | {eq_r/(eq_v+1e-10):.2f} |
+
+**Improvements:** Sharpe {((best_sh/(eq_r/(eq_v+1e-10))-1)*100):+.1f}%
+
+{wdf.to_markdown(index=False)}
+"""
+    return fig, wdf, md
+
+def ai_portfolio(tickers, period):
+    fig, wdf, md = opt_portfolio(tickers, period)
+    if fig is None:
+        return f"Error: {md}"
+    pd_str = f"Tickers: {', '.join(wdf['Ticker'].tolist())}\nWeights: {', '.join([f'{t}: {w:.1f}%' for t, w in zip(wdf['Ticker'], wdf['Optimal (%)'])])}"
+    prompt = f"""You are a portfolio manager at a quant hedge fund ($500M AUM).
+
+PORTFOLIO:
+{pd_str}
+
+Provide:
+1. Portfolio Health Score (0-100)
+2. Concentration risk analysis
+3. Correlation risks
+4. Rebalancing recommendations with exact %
+5. Hedging strategy for tail risk
+6. Expected return & Sharpe estimate
+
+Use quantitative reasoning."""
+    return K2Client().ask(prompt, temp=0.3)
+
+# ═══════════════════════════════════════════════════════════════
+# GRADIO UI
+# ═══════════════════════════════════════════════════════════════
+with gr.Blocks(
+    title="AlphaForge x K2 Think V2 — Elite Quant Trading",
+    theme=gr.themes.Soft(),
+    css="""
+    .title { text-align: center; font-size: 2.2em; font-weight: bold; color: #1a73e8; }
+    .subtitle { text-align: center; font-size: 1em; color: #5f6368; }
+    .badge { background: linear-gradient(90deg, #667eea, #764ba2); color: white; padding: 6px 12px; border-radius: 16px; font-weight: 600; font-size: 0.85em; }
+    .info-box { background: #e8f4fd; border-left: 4px solid #1a73e8; padding: 10px; margin: 6px 0; border-radius: 4px; }
+    .warn-box { background: #fff3e0; border-left: 4px solid #f9ab00; padding: 10px; margin: 6px 0; border-radius: 4px; }
+    """
+) as demo:
+    gr.HTML("""
+    <div style="text-align:center;">
+        <h1 class="title">🔥 AlphaForge x K2 Think V2</h1>
+        <p class="subtitle">Elite Quantitative Trading Platform — Powered by MBZUAI's State-of-the-Art Reasoning Model</p>
+        <p>
+            <span class="badge">🤖 K2 Think V2</span>
+            <span style="margin-left:6px;" class="badge">📊 Real-Time Data</span>
+            <span style="margin-left:6px;" class="badge">🎯 AI Alpha</span>
+        </p>
+    </div>
+    """)
+
+    # ── TAB 1: Single Stock ──
+    with gr.Tab("📈 Single Stock Analysis"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                t_in = gr.Textbox(label="Stock Ticker", placeholder="e.g., AAPL, TSLA, SPY, BTC-USD", value="AAPL")
+                p_in = gr.Dropdown(label="Time Period", choices=["1mo","3mo","6mo","1y","2y","5y"], value="6mo")
+                gr.HTML("""<div class="info-box">💡 <b>Tip:</b> Try AAPL, TSLA, NVDA, SPY, QQQ, BTC-USD, ETH-USD</div>""")
+                a_btn = gr.Button("🔍 Analyze Stock", variant="primary", size="lg")
+                ai_btn = gr.Button("🤖 AI Deep Analysis (K2 Think V2)", variant="secondary", size="lg")
+                gr.HTML("""<div class="warn-box">⚠️ <b>K2 API:</b> AI features need API key. See About tab for setup.</div>""")
+            with gr.Column(scale=2):
+                out_md = gr.Markdown()
+        with gr.Row():
+            out_candle = gr.Plot(label="📊 Price Chart")
+            out_macd = gr.Plot(label="📈 MACD")
+        with gr.Row():
+            out_dist = gr.Plot(label="📉 Return Distribution")
+            out_ai = gr.Textbox(label="🤖 K2 Think V2 AI Analysis", lines=20, max_lines=35, show_copy_button=True)
+        a_btn.click(fn=analyze_stock, inputs=[t_in, p_in], outputs=[out_candle, out_macd, out_dist, out_md])
+        ai_btn.click(fn=ai_analyze, inputs=[t_in, p_in], outputs=[out_ai])
+
+    # ── TAB 2: Portfolio ──
+    with gr.Tab("💼 Portfolio Optimizer"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                pt_in = gr.Textbox(label="Tickers (comma-separated)", placeholder="e.g., AAPL, MSFT, GOOGL, AMZN, NVDA", value="AAPL, MSFT, GOOGL, AMZN, NVDA")
+                pp_in = gr.Dropdown(label="Lookback Period", choices=["6mo","1y","2y","3y"], value="1y")
+                gr.HTML("""<div class="info-box">💡 <b>Tip:</b> Enter 2-10 tickers. Optimizer maximizes Sharpe ratio.</div>""")
+                o_btn = gr.Button("🎯 Optimize Portfolio", variant="primary", size="lg")
+                ai_p_btn = gr.Button("🤖 AI Portfolio Advice (K2 Think V2)", variant="secondary", size="lg")
+            with gr.Column(scale=2):
+                p_md = gr.Markdown()
+        with gr.Row():
+            out_frontier = gr.Plot(label="📊 Efficient Frontier")
+            out_weights = gr.DataFrame(label="⚖️ Optimal Weights", interactive=False)
+        with gr.Row():
+            out_ai_p = gr.Textbox(label="🤖 AI Portfolio Advice", lines=20, max_lines=30, show_copy_button=True)
+        o_btn.click(fn=opt_portfolio, inputs=[pt_in, pp_in], outputs=[out_frontier, out_weights, p_md])
+        ai_p_btn.click(fn=ai_portfolio, inputs=[pt_in, pp_in], outputs=[out_ai_p])
+
+    # ── TAB 3: AI Chat ──
+    with gr.Tab("💬 K2 Think V2 Chat"):
+        gr.Markdown("## 💬 Direct Chat with K2 Think V2")
+        gr.Markdown("Ask any financial question, strategy idea, or market analysis. The AI provides detailed, reasoned responses.")
+        c_in = gr.Textbox(label="Your Question", placeholder="e.g., 'Analyze the bull case for AI stocks in 2025' or 'Explain pairs trading'", lines=4)
+        c_temp = gr.Slider(label="Temperature (creativity)", minimum=0, maximum=1, value=0.5, step=0.1,
+                          info="Lower = more factual, Higher = more creative")
+        c_btn = gr.Button("🚀 Ask K2 Think V2", variant="primary", size="lg")
+        c_out = gr.Textbox(label="🤖 K2 Think V2 Response", lines=25, max_lines=40, show_copy_button=True)
+        c_btn.click(fn=lambda q, t: K2Client().ask(q, temp=t), inputs=[c_in, c_temp], outputs=[c_out])
+
+    # ── TAB 4: About ──
+    with gr.Tab("ℹ️ About & Setup"):
+        gr.Markdown("""
+        ## ℹ️ About AlphaForge x K2 Think V2
+
+        Built for the **Build with K2 Think V2 Challenge** by MBZUAI.
+
+        ### 🚀 Features
+        - **📈 Single Stock Analysis**: Candlestick + Bollinger + SMA + VWAP, MACD, RSI, 12 risk metrics, composite alpha signal
+        - **🤖 AI Deep Analysis**: K2 Think V2 chain-of-thought reasoning — executive summary, risk assessment, trade ideas with entry/stop/target
+        - **💼 Portfolio Optimizer**: Mean-variance optimization, efficient frontier (2000 portfolios), Sharpe maximization, optimal vs equal-weight
+        - **🤖 AI Portfolio Advice**: Portfolio health score, concentration risk, rebalancing %, hedging strategies
+        - **💬 Direct AI Chat**: Ask any financial question — strategy explanations, market analysis, quant interview prep
+
+        ### 🔧 Setup Instructions
+
+        <div style="background:#fff3e0;border-left:4px solid #f9ab00;padding:10px;margin:6px 0;border-radius:4px;">
+        ⚠️ <b>K2 Think V2 API Key Required for AI features</b><br><br>
+        1. Go to <b>Space Settings</b> → <b>Repository secrets</b><br>
+        2. Click <b>New secret</b><br>
+        3. Name: <code>K2_API_KEY</code><br>
+        4. Value: <code>IFM-4SpQ0qEg0Wlsw04O</code><br>
+        5. Click <b>Save</b>, then <b>Factory Rebuild</b>
+        </div>
+
+        <div style="background:#e8f4fd;border-left:4px solid #1a73e8;padding:10px;margin:6px 0;border-radius:4px;">
+        ✅ <b>Even without API key:</b> All technical analysis, charts, risk metrics, and portfolio optimization work perfectly!
+        </div>
+
+        ### 🏗 Architecture
+        ```
+        yfinance (live data) → AlphaForge (quant signals) → K2 Think V2 (AI reasoning)
+                                   ↓
+                          Plotly Charts + Risk Metrics + Portfolio Optimization
+        ```
+
+        ### 🔗 Links
+        - [Full AlphaForge Platform](https://huggingface.co/Premchan369/alphaforge-quant-system) (25 quant modules)
+        - [Build with K2 Think V2](https://build.k2think.ai/)
+        - [MBZUAI](https://mbzuai.ac.ae/)
+
+        ---
+        *Built by Premchan | Build with K2 Think V2 Challenge | MBZUAI*
+        """)
+
+demo.launch(server_name="0.0.0.0", server_port=7860)