ok

.gitignore

@@ -15,3 +15,6 @@ node_modules/
 
 # Frontend dev artifacts
 .frontend-dev/
+
+# Runtime config
+schedule_config.json

README.md

@@ -238,7 +238,7 @@ npm run dev  # http://localhost:5173
 | **Stock Ranking (1-50)** | Composite scoring algorithm ranks stocks from best to worst investment opportunity |
 | **Analysis Pipeline** | 12-step visualization showing data collection, agent analysis, debate, and decision |
 | **Investment Debates** | Full bull vs bear debate transcripts with research manager synthesis |
-| **Backtesting** | Prediction accuracy tracking, risk metrics (Sharpe, drawdown), win/loss ratios |
+| **Backtesting** | Prediction accuracy tracking, risk metrics (Sharpe, drawdown), win/loss ratios, date backtest runner with cancel support |
 | **Portfolio Simulator** | Paper trading simulation with Zerodha-accurate brokerage charges and Nifty50 benchmarking |
 | **Settings Panel** | Configure LLM provider (Claude/OpenAI), model tiers, debate rounds, parallel workers |
 | **Dark Mode** | Automatic system theme detection with manual toggle |

cli/main.py

@@ -196,7 +196,7 @@ def update_display(layout, spinner_text=None):
     layout["header"].update(
         Panel(
             "[bold green]Welcome to TradingAgents CLI[/bold green]\n"
-            "[dim]© [Tauric Research](https://github.com/TauricResearch)[/dim]",
+            "[dim]© [hjlabs.in](https://hjlabs.in)[/dim]",
             title="Welcome to TradingAgents",
             border_style="green",
             padding=(1, 2),
@@ -408,7 +408,7 @@ def get_user_selections():
     welcome_content += "[bold]Workflow Steps:[/bold]\n"
     welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management\n\n"
     welcome_content += (
-        "[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]"
+        "[dim]Built by [hjlabs.in](https://hjlabs.in)[/dim]"
     )
 
     # Create and center the welcome box

frontend/README.md

@@ -80,12 +80,19 @@ Track AI performance over time with comprehensive analytics:
 - **Prediction Accuracy**: Overall and per-recommendation-type accuracy
 - **Accuracy Trend**: Visualize accuracy over time
 - **Risk Metrics**: Sharpe ratio, max drawdown, win rate
-- **Portfolio Simulator**: Test different investment amounts
+- **Portfolio Simulator**: Test different investment amounts with Zerodha-accurate brokerage charges
 - **AI vs Nifty50**: Compare AI strategy performance against the index
-- **Return Distribution**: Histogram of next-day returns
+- **Return Distribution**: Histogram of hold-period returns
+- **Date Backtest Runner**: Run AI analysis for any date directly from the History page
+- **Cancel Support**: Cancel in-progress bulk analysis
 
 ![History Page](docs/screenshots/10-history-page.png)
 
+#### Date Selection & Stock List
+Select any date to view all 50 ranked stocks with decisions, hold periods, and returns:
+
+![History Stocks Expanded](docs/screenshots/11-history-stocks-expanded.png)
+
 ## Tech Stack
 
 - **Frontend**: React 18 + TypeScript + Vite

frontend/backend/database.py

@@ -1219,6 +1219,42 @@ def get_backtest_results_by_date(date: str) -> list:
         conn.close()
 
 
+def get_all_backtest_results_grouped() -> dict:
+    """Get all backtest results grouped by date for the History page bundle.
+
+    Returns: { date: { symbol: { return_1d, return_1w, return_1m, return_at_hold, hold_days, prediction_correct, decision } } }
+    """
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT date, symbol, decision, return_1d, return_1w, return_1m,
+                   return_at_hold, hold_days, prediction_correct,
+                   price_at_prediction
+            FROM backtest_results
+            ORDER BY date
+        """)
+
+        grouped: dict = {}
+        for row in cursor.fetchall():
+            date = row['date']
+            if date not in grouped:
+                grouped[date] = {}
+            grouped[date][row['symbol']] = {
+                'return_1d': row['return_1d'],
+                'return_1w': row['return_1w'],
+                'return_1m': row['return_1m'],
+                'return_at_hold': row['return_at_hold'],
+                'hold_days': row['hold_days'] if 'hold_days' in row.keys() else None,
+                'prediction_correct': bool(row['prediction_correct']) if row['prediction_correct'] is not None else None,
+                'decision': row['decision'],
+            }
+        return grouped
+    finally:
+        conn.close()
+
+
 def get_all_backtest_results() -> list:
     """Get all backtest results for accuracy calculation."""
     conn = get_connection()

frontend/backend/server.py

@@ -8,7 +8,7 @@ import database as db
 import sys
 import os
 from pathlib import Path
-from datetime import datetime
+from datetime import datetime, timedelta
 import threading
 from concurrent.futures import ThreadPoolExecutor, as_completed
 import asyncio
@@ -549,6 +549,29 @@ bulk_analysis_state = {
     "cancelled": False  # Flag to signal cancellation
 }
 
+# Auto-analyze schedule config
+SCHEDULE_FILE = Path(__file__).parent / "schedule_config.json"
+
+def _load_schedule_config():
+    """Load schedule config from JSON file."""
+    if SCHEDULE_FILE.exists():
+        try:
+            with open(SCHEDULE_FILE, "r") as f:
+                return json.load(f)
+        except Exception:
+            pass
+    return {"enabled": False, "time": "09:00", "config": {}, "last_run_date": None}
+
+def _save_schedule_config(config):
+    """Persist schedule config to JSON file."""
+    try:
+        with open(SCHEDULE_FILE, "w") as f:
+            json.dump(config, f, indent=2)
+    except Exception as e:
+        print(f"[AutoSchedule] Failed to save config: {e}")
+
+schedule_config = _load_schedule_config()
+
 # List of Nifty 50 stocks
 NIFTY_50_SYMBOLS = [
     "RELIANCE", "TCS", "HDFCBANK", "INFY", "ICICIBANK", "HINDUNILVR", "ITC", "SBIN",
@@ -919,6 +942,69 @@ async def cancel_analysis(symbol: str):
     }
 
 
+# ============== History Bundle Endpoint ==============
+
+# In-memory cache for Nifty50 index prices (fetched once, refreshed lazily)
+_nifty50_cache = {"prices": {}, "fetched_at": None}
+
+def _fetch_nifty50_prices_sync():
+    """Fetch Nifty50 index prices (called once and cached)."""
+    try:
+        import yfinance as yf
+        from datetime import timedelta
+
+        dates = db.get_all_dates()
+        if not dates:
+            return {}
+
+        start_date = (datetime.strptime(min(dates), "%Y-%m-%d") - timedelta(days=7)).strftime("%Y-%m-%d")
+        end_date = (datetime.strptime(max(dates), "%Y-%m-%d") + timedelta(days=7)).strftime("%Y-%m-%d")
+
+        nifty = yf.Ticker("^NSEI")
+        hist = nifty.history(start=start_date, end=end_date, interval="1d")
+
+        prices = {}
+        for idx, row in hist.iterrows():
+            date_str = idx.strftime("%Y-%m-%d")
+            prices[date_str] = round(float(row['Close']), 2)
+        return prices
+    except Exception:
+        return {}
+
+
+@app.get("/history/bundle")
+async def get_history_bundle():
+    """Return ALL data the History page needs in a single response.
+
+    Combines: recommendations + all backtest results + accuracy metrics.
+    Everything comes from SQLite (instant), no yfinance calls.
+    Nifty50 prices are served from cache.
+    """
+    recommendations = db.get_all_recommendations()
+    backtest_by_date = db.get_all_backtest_results_grouped()
+    accuracy = db.calculate_accuracy_metrics()
+
+    # Serve Nifty50 from cache, refresh in background if stale
+    nifty_prices = _nifty50_cache.get("prices", {})
+    if not _nifty50_cache.get("fetched_at"):
+        # First request — return empty, trigger background fetch
+        def bg_fetch():
+            prices = _fetch_nifty50_prices_sync()
+            _nifty50_cache["prices"] = prices
+            _nifty50_cache["fetched_at"] = datetime.now().isoformat()
+        thread = threading.Thread(target=bg_fetch, daemon=True)
+        thread.start()
+    else:
+        nifty_prices = _nifty50_cache["prices"]
+
+    return {
+        "recommendations": recommendations,
+        "backtest_by_date": backtest_by_date,
+        "accuracy": accuracy,
+        "nifty50_prices": nifty_prices,
+    }
+
+
 # ============== Backtest Endpoints ==============
 # NOTE: Static routes must come BEFORE parameterized routes to avoid
 # "accuracy" being matched as a {date} parameter.
@@ -930,6 +1016,149 @@ async def get_accuracy_metrics():
     return metrics
 
 
+@app.get("/backtest/{date}/detailed")
+async def get_detailed_backtest(date: str):
+    """Get enriched backtest data with live prices, formulas, agent reports, and debate summaries."""
+    import yfinance as yf
+
+    rec = db.get_recommendation_by_date(date)
+    if not rec or 'analysis' not in rec:
+        return {"date": date, "total_stocks": 0, "stocks": []}
+
+    analysis = rec['analysis']
+    backtest_results = db.get_backtest_results_by_date(date)
+    bt_by_symbol = {r['symbol']: r for r in backtest_results}
+
+    pred_date = datetime.strptime(date, '%Y-%m-%d')
+    today = datetime.now()
+
+    # Collect symbols that need live prices (active hold periods)
+    symbols_needing_live = []
+    for symbol, stock_data in analysis.items():
+        hold_days = stock_data.get('hold_days') or 0
+        hold_end = pred_date + timedelta(days=hold_days) if hold_days > 0 else pred_date + timedelta(days=1)
+        if today < hold_end:
+            symbols_needing_live.append(symbol)
+
+    # Batch-fetch live prices for active holds
+    live_prices = {}
+    if symbols_needing_live:
+        def fetch_live_batch():
+            for sym in symbols_needing_live:
+                try:
+                    yf_sym = sym if '.' in sym else f"{sym}.NS"
+                    t = yf.Ticker(yf_sym)
+                    hist = t.history(period='1d')
+                    if not hist.empty:
+                        live_prices[sym] = round(float(hist['Close'].iloc[-1]), 2)
+                except Exception:
+                    pass
+        fetch_live_batch()
+
+    stocks = []
+    for symbol, stock_data in analysis.items():
+        decision = stock_data.get('decision', 'HOLD')
+        confidence = stock_data.get('confidence', 'MEDIUM')
+        risk = stock_data.get('risk', 'MEDIUM')
+        hold_days = stock_data.get('hold_days') or 0
+        raw_analysis = stock_data.get('raw_analysis', '')
+
+        bt = bt_by_symbol.get(symbol, {})
+        price_pred = bt.get('price_at_prediction')
+
+        # Calculate hold period status
+        hold_end_date = pred_date + timedelta(days=hold_days) if hold_days > 0 else pred_date + timedelta(days=1)
+        days_elapsed = (today - pred_date).days
+        hold_period_active = today < hold_end_date and hold_days > 0
+
+        # Determine display price and return
+        price_current = live_prices.get(symbol)
+        price_at_hold_end = None
+        return_current = None
+        return_at_hold = bt.get('return_at_hold')
+
+        if price_pred:
+            if hold_period_active and price_current:
+                return_current = round(((price_current - price_pred) / price_pred) * 100, 2)
+            elif not hold_period_active:
+                # Hold completed — use stored data
+                if return_at_hold is not None:
+                    price_at_hold_end = round(price_pred * (1 + return_at_hold / 100), 2)
+                elif bt.get('return_1d') is not None:
+                    return_current = bt['return_1d']
+
+        # Build formula string
+        formula = ""
+        if price_pred:
+            if hold_period_active and price_current:
+                ret = return_current or 0
+                sign = "+" if ret >= 0 else ""
+                formula = f"Return = (₹{price_current} - ₹{price_pred}) / ₹{price_pred} × 100 = {sign}{ret}%"
+            elif return_at_hold is not None:
+                p_end = price_at_hold_end or round(price_pred * (1 + return_at_hold / 100), 2)
+                sign = "+" if return_at_hold >= 0 else ""
+                formula = f"Return = (₹{p_end} - ₹{price_pred}) / ₹{price_pred} × 100 = {sign}{return_at_hold}%"
+            elif bt.get('return_1d') is not None:
+                p_1d = bt.get('price_1d_later', 0)
+                r_1d = bt['return_1d']
+                sign = "+" if r_1d >= 0 else ""
+                formula = f"Return = (₹{p_1d} - ₹{price_pred}) / ₹{price_pred} × 100 = {sign}{r_1d}%"
+
+        # Prediction correctness
+        prediction_correct = bt.get('prediction_correct')
+        if hold_period_active:
+            prediction_correct = None  # Can't judge while hold is active
+
+        # Agent reports (condensed)
+        agent_summary = {}
+        try:
+            reports = db.get_agent_reports(date, symbol)
+            for agent_type, report_data in reports.items():
+                content = report_data.get('report_content', '')
+                # Take first 300 chars as summary
+                agent_summary[agent_type] = content[:300] + ('...' if len(content) > 300 else '')
+        except Exception:
+            pass
+
+        # Debate summary
+        debate_summary = {}
+        try:
+            debates = db.get_debate_history(date, symbol)
+            for debate_type, debate_data in debates.items():
+                judge = debate_data.get('judge_decision', '')
+                judge_short = judge[:200] + ('...' if len(judge) > 200 else '') if judge else ''
+                debate_summary[debate_type] = judge_short
+        except Exception:
+            pass
+
+        stocks.append({
+            "symbol": symbol,
+            "company_name": stock_data.get('company_name', symbol),
+            "rank": stock_data.get('rank'),
+            "decision": decision,
+            "confidence": confidence,
+            "risk": risk,
+            "hold_days": hold_days,
+            "hold_days_elapsed": min(days_elapsed, hold_days) if hold_days > 0 else days_elapsed,
+            "hold_period_active": hold_period_active,
+            "price_at_prediction": price_pred,
+            "price_current": price_current,
+            "price_at_hold_end": price_at_hold_end,
+            "return_current": return_current,
+            "return_at_hold": return_at_hold,
+            "prediction_correct": prediction_correct,
+            "formula": formula,
+            "raw_analysis": raw_analysis[:500] if raw_analysis else '',
+            "agent_summary": agent_summary,
+            "debate_summary": debate_summary,
+        })
+
+    # Sort by rank
+    stocks.sort(key=lambda s: s.get('rank') or 999)
+
+    return {"date": date, "total_stocks": len(stocks), "stocks": stocks}
+
+
 @app.get("/backtest/{date}/{symbol}")
 async def get_backtest_result(date: str, symbol: str):
     """Get backtest result for a specific stock and date.
@@ -1045,11 +1274,198 @@ async def get_nifty50_history():
         return {"dates": [], "prices": {}, "error": str(e)}
 
 
+# ============== Schedule Endpoints ==============
+
+class ScheduleRequest(BaseModel):
+    enabled: bool = False
+    time: str = "09:00"
+    timezone: str = "Asia/Kolkata"
+    config: dict = {}
+
+@app.post("/settings/schedule")
+async def set_schedule(request: ScheduleRequest):
+    """Set the auto-analyze schedule."""
+    global schedule_config
+    schedule_config["enabled"] = request.enabled
+    schedule_config["time"] = request.time
+    schedule_config["timezone"] = request.timezone
+    schedule_config["config"] = request.config
+    _save_schedule_config(schedule_config)
+    status = "enabled" if request.enabled else "disabled"
+    print(f"[AutoSchedule] Schedule updated: {request.time} {request.timezone} ({status})")
+    return {"status": "ok", "message": f"Schedule {status} at {request.time} {request.timezone}"}
+
+@app.get("/settings/schedule")
+async def get_schedule():
+    """Get the current auto-analyze schedule."""
+    return {
+        "enabled": schedule_config.get("enabled", False),
+        "time": schedule_config.get("time", "09:00"),
+        "timezone": schedule_config.get("timezone", "Asia/Kolkata"),
+        "config": schedule_config.get("config", {}),
+        "last_run_date": schedule_config.get("last_run_date"),
+    }
+
+
+# ============== Scheduler Thread ==============
+
+def _auto_analyze_scheduler():
+    """Background thread that triggers Analyze All at the scheduled time daily."""
+    from zoneinfo import ZoneInfo
+    global schedule_config, bulk_analysis_state
+    print("[AutoSchedule] Scheduler thread started")
+
+    while True:
+        try:
+            time.sleep(30)
+
+            if not schedule_config.get("enabled"):
+                continue
+
+            # Get current time in the configured timezone
+            tz_name = schedule_config.get("timezone", "Asia/Kolkata")
+            try:
+                tz = ZoneInfo(tz_name)
+            except Exception:
+                tz = ZoneInfo("Asia/Kolkata")
+
+            now = datetime.now(tz)
+            scheduled_time = schedule_config.get("time", "09:00")
+            today_str = now.strftime("%Y-%m-%d")
+
+            # Already ran today (in the configured timezone)?
+            if schedule_config.get("last_run_date") == today_str:
+                continue
+
+            # Parse scheduled hour:minute
+            try:
+                sched_hour, sched_minute = map(int, scheduled_time.split(":"))
+            except (ValueError, AttributeError):
+                continue
+
+            # Check if we're within a 2-minute window of the scheduled time
+            current_minutes = now.hour * 60 + now.minute
+            scheduled_minutes = sched_hour * 60 + sched_minute
+            if abs(current_minutes - scheduled_minutes) > 1:
+                continue
+
+            # Don't trigger if already running
+            if bulk_analysis_state.get("status") == "running":
+                print(f"[AutoSchedule] Skipping — bulk analysis already running")
+                continue
+
+            print(f"[AutoSchedule] Triggering daily analysis at {scheduled_time} {tz_name}")
+            schedule_config["last_run_date"] = today_str
+            _save_schedule_config(schedule_config)
+
+            # Build analysis config
+            config = schedule_config.get("config", {})
+            analysis_config = {
+                "deep_think_model": config.get("deep_think_model", "opus"),
+                "quick_think_model": config.get("quick_think_model", "sonnet"),
+                "provider": config.get("provider", "claude_subscription"),
+                "api_key": config.get("api_key"),
+                "max_debate_rounds": config.get("max_debate_rounds", 1),
+            }
+            parallel_workers = max(1, min(5, config.get("parallel_workers", 3)))
+
+            # Same logic as POST /analyze/all
+            analysis_date = today_str
+            already_analyzed = set(db.get_analyzed_symbols_for_date(analysis_date))
+            symbols_to_analyze = [s for s in NIFTY_50_SYMBOLS if s not in already_analyzed]
+
+            if not symbols_to_analyze:
+                print(f"[AutoSchedule] All stocks already analyzed for {analysis_date}")
+                continue
+
+            def run_auto_bulk():
+                global bulk_analysis_state
+                bulk_analysis_state = {
+                    "status": "running",
+                    "total": len(symbols_to_analyze),
+                    "total_all": len(NIFTY_50_SYMBOLS),
+                    "skipped": len(already_analyzed),
+                    "completed": 0,
+                    "failed": 0,
+                    "current_symbols": [],
+                    "started_at": datetime.now().isoformat(),
+                    "completed_at": None,
+                    "results": {},
+                    "parallel_workers": parallel_workers,
+                    "cancelled": False,
+                }
+
+                with ThreadPoolExecutor(max_workers=parallel_workers) as executor:
+                    def analyze_one(symbol):
+                        try:
+                            if bulk_analysis_state.get("cancelled"):
+                                return (symbol, "cancelled", None)
+                            run_analysis_task(symbol, analysis_date, analysis_config)
+                            max_wait = 600
+                            waited = 0
+                            while waited < max_wait:
+                                if bulk_analysis_state.get("cancelled"):
+                                    return (symbol, "cancelled", None)
+                                if symbol not in running_analyses:
+                                    return (symbol, "unknown", None)
+                                status = running_analyses[symbol].get("status")
+                                if status not in ("running", "initializing"):
+                                    return (symbol, status, None)
+                                time.sleep(2)
+                                waited += 2
+                            return (symbol, "timeout", None)
+                        except Exception as e:
+                            return (symbol, "error", str(e))
+
+                    future_to_sym = {
+                        executor.submit(analyze_one, sym): sym
+                        for sym in symbols_to_analyze
+                    }
+                    bulk_analysis_state["current_symbols"] = list(symbols_to_analyze[:parallel_workers])
+
+                    for future in as_completed(future_to_sym):
+                        sym = future_to_sym[future]
+                        try:
+                            sym, status, error = future.result()
+                            bulk_analysis_state["results"][sym] = status if not error else f"error: {error}"
+                            if status == "completed":
+                                bulk_analysis_state["completed"] += 1
+                            else:
+                                bulk_analysis_state["failed"] += 1
+                            remaining = [s for s in symbols_to_analyze if s not in bulk_analysis_state["results"]]
+                            bulk_analysis_state["current_symbols"] = remaining[:parallel_workers]
+                        except Exception as e:
+                            bulk_analysis_state["results"][sym] = f"error: {str(e)}"
+                            bulk_analysis_state["failed"] += 1
+
+                bulk_analysis_state["status"] = "completed"
+                bulk_analysis_state["current_symbols"] = []
+                bulk_analysis_state["completed_at"] = datetime.now().isoformat()
+                print(f"[AutoSchedule] Daily analysis completed: {bulk_analysis_state['completed']} succeeded, {bulk_analysis_state['failed']} failed")
+
+            threading.Thread(target=run_auto_bulk, daemon=True).start()
+
+        except Exception as e:
+            print(f"[AutoSchedule] Scheduler error: {e}")
+            time.sleep(60)
+
+
 @app.on_event("startup")
 async def startup_event():
     """Rebuild daily_recommendations and trigger backtest calculations at startup."""
     db.rebuild_all_daily_recommendations()
 
+    # Start auto-analyze scheduler
+    threading.Thread(target=_auto_analyze_scheduler, daemon=True).start()
+
+    # Warm Nifty50 cache in background
+    def warm_nifty_cache():
+        prices = _fetch_nifty50_prices_sync()
+        _nifty50_cache["prices"] = prices
+        _nifty50_cache["fetched_at"] = datetime.now().isoformat()
+        print(f"[Nifty50] Cached {len(prices)} index prices")
+    threading.Thread(target=warm_nifty_cache, daemon=True).start()
+
     # Trigger backtest calculation for all dates in background
     def startup_backtest():
         import backtest_service as bt

frontend/src/components/AccuracyExplainModal.tsx

@@ -1,4 +1,5 @@
 import { X, HelpCircle, TrendingUp, TrendingDown, Minus, CheckCircle } from 'lucide-react';
+import { createPortal } from 'react-dom';
 import type { AccuracyMetrics } from '../types';
 
 interface AccuracyExplainModalProps {
@@ -17,7 +18,7 @@ export default function AccuracyExplainModal({ isOpen, onClose, metrics }: Accur
   const holdCorrect = Math.round(metrics.hold_accuracy * metrics.total_predictions * 0.66); // ~33 hold signals
   const holdTotal = Math.round(metrics.total_predictions * 0.66);
 
-  return (
+  return createPortal(
     <div className="fixed inset-0 z-50 flex items-center justify-center p-4">
       {/* Backdrop */}
       <div
@@ -172,6 +173,7 @@ export default function AccuracyExplainModal({ isOpen, onClose, metrics }: Accur
           </button>
         </div>
       </div>
-    </div>
+    </div>,
+    document.body
   );
 }

frontend/src/components/AccuracyTrendChart.tsx

@@ -1,5 +1,4 @@
 import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, Legend } from 'recharts';
-import { getAccuracyTrend } from '../data/recommendations';
 
 export interface AccuracyTrendPoint {
   date: string;
@@ -12,12 +11,11 @@ export interface AccuracyTrendPoint {
 interface AccuracyTrendChartProps {
   height?: number;
   className?: string;
-  data?: AccuracyTrendPoint[];  // Optional prop for real data
+  data?: AccuracyTrendPoint[];
 }
 
 export default function AccuracyTrendChart({ height = 200, className = '', data: propData }: AccuracyTrendChartProps) {
-  // Use provided data or fall back to mock data
-  const data = propData || getAccuracyTrend();
+  const data = propData || [];
 
   if (data.length === 0) {
     return (

frontend/src/components/CumulativeReturnChart.tsx

@@ -1,16 +1,14 @@
 import { AreaChart, Area, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, ReferenceLine } from 'recharts';
-import { getCumulativeReturns } from '../data/recommendations';
 import type { CumulativeReturnPoint } from '../types';
 
 interface CumulativeReturnChartProps {
   height?: number;
   className?: string;
-  data?: CumulativeReturnPoint[];  // Optional prop for real data
+  data?: CumulativeReturnPoint[];
 }
 
 export default function CumulativeReturnChart({ height = 160, className = '', data: propData }: CumulativeReturnChartProps) {
-  // Use provided data or fall back to mock data
-  const data = propData || getCumulativeReturns();
+  const data = propData || [];
 
   if (data.length === 0) {
     return (

frontend/src/components/FilterPanel.tsx

@@ -1,5 +1,5 @@
 import { SlidersHorizontal, ArrowUpDown } from 'lucide-react';
-import { getAllSectors } from '../data/recommendations';
+import { NIFTY_50_STOCKS } from '../types';
 import type { FilterState } from '../types';
 
 interface FilterPanelProps {
@@ -9,7 +9,7 @@ interface FilterPanelProps {
 }
 
 export default function FilterPanel({ filters, onFilterChange, className = '' }: FilterPanelProps) {
-  const sectors = getAllSectors();
+  const sectors = ['All', ...Array.from(new Set(NIFTY_50_STOCKS.map(s => s.sector).filter(Boolean))).sort()];
 
   const decisions: Array<FilterState['decision']> = ['ALL', 'BUY', 'SELL', 'HOLD'];
   const sortOptions: Array<{ value: FilterState['sortBy']; label: string }> = [

frontend/src/components/IndexComparisonChart.tsx

@@ -1,17 +1,15 @@
 import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, Legend, ReferenceLine } from 'recharts';
 import { TrendingUp, TrendingDown } from 'lucide-react';
-import { getCumulativeReturns } from '../data/recommendations';
 import type { CumulativeReturnPoint } from '../types';
 
 export interface IndexComparisonChartProps {
   height?: number;
   className?: string;
-  data?: CumulativeReturnPoint[];  // Optional prop for real data
+  data?: CumulativeReturnPoint[];
 }
 
 export default function IndexComparisonChart({ height = 220, className = '', data: propData }: IndexComparisonChartProps) {
-  // Use provided data or fall back to mock data
-  const data = propData || getCumulativeReturns();
+  const data = propData || [];
 
   if (data.length === 0) {
     return (

frontend/src/components/InfoModal.tsx

@@ -1,4 +1,5 @@
 import { X, Info } from 'lucide-react';
+import { createPortal } from 'react-dom';
 import type { ReactNode } from 'react';
 
 interface InfoModalProps {
@@ -12,7 +13,7 @@ interface InfoModalProps {
 export default function InfoModal({ isOpen, onClose, title, children, icon }: InfoModalProps) {
   if (!isOpen) return null;
 
-  return (
+  return createPortal(
     <div className="fixed inset-0 z-50 overflow-y-auto">
       {/* Backdrop */}
       <div
@@ -38,7 +39,7 @@ export default function InfoModal({ isOpen, onClose, title, children, icon }: In
           </div>
 
           {/* Content */}
-          <div className="p-4">
+          <div className="p-4 max-h-[70vh] overflow-y-auto">
             {children}
           </div>
 
@@ -53,7 +54,8 @@ export default function InfoModal({ isOpen, onClose, title, children, icon }: In
           </div>
         </div>
       </div>
-    </div>
+    </div>,
+    document.body
   );
 }
 

frontend/src/components/OverallReturnModal.tsx

@@ -1,5 +1,5 @@
 import { X, Activity } from 'lucide-react';
-import { getOverallReturnBreakdown } from '../data/recommendations';
+import { createPortal } from 'react-dom';
 import CumulativeReturnChart from './CumulativeReturnChart';
 import type { CumulativeReturnPoint } from '../types';
 
@@ -20,10 +20,9 @@ interface OverallReturnModalProps {
 export default function OverallReturnModal({ isOpen, onClose, breakdown: propBreakdown, cumulativeData }: OverallReturnModalProps) {
   if (!isOpen) return null;
 
-  // Use provided breakdown or fall back to mock data
-  const breakdown = propBreakdown || getOverallReturnBreakdown();
+  const breakdown = propBreakdown || { dailyReturns: [], finalMultiplier: 1, finalReturn: 0, formula: '' };
 
-  return (
+  return createPortal(
     <div className="fixed inset-0 z-50 flex items-center justify-center p-4">
       {/* Backdrop */}
       <div
@@ -246,6 +245,7 @@ export default function OverallReturnModal({ isOpen, onClose, breakdown: propBre
           </button>
         </div>
       </div>
-    </div>
+    </div>,
+    document.body
   );
 }

frontend/src/components/PortfolioSimulator.tsx

@@ -1,7 +1,6 @@
 import { useState, useMemo } from 'react';
 import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, ReferenceLine, Legend, BarChart, Bar, Cell, LabelList } from 'recharts';
-import { Calculator, ChevronDown, ChevronUp, IndianRupee, Settings2, BarChart3, Info, TrendingUp, TrendingDown, ArrowRightLeft, Wallet, PiggyBank, Receipt, HelpCircle, AlertCircle } from 'lucide-react';
-import { sampleRecommendations, getNifty50IndexHistory, getBacktestResult } from '../data/recommendations';
+import { Calculator, ChevronDown, ChevronUp, IndianRupee, Settings2, BarChart3, Info, TrendingUp, TrendingDown, ArrowRightLeft, Wallet, PiggyBank, Receipt, HelpCircle } from 'lucide-react';
 import { calculateBrokerage, formatINR, type BrokerageBreakdown } from '../utils/brokerageCalculator';
 import InfoModal, { InfoButton } from './InfoModal';
 import type { Decision, DailyRecommendation } from '../types';
@@ -9,7 +8,6 @@ import type { Decision, DailyRecommendation } from '../types';
 interface PortfolioSimulatorProps {
   className?: string;
   recommendations?: DailyRecommendation[];
-  isUsingMockData?: boolean;
   nifty50Prices?: Record<string, number>;
   allBacktestData?: Record<string, Record<string, number>>;
 }
@@ -37,7 +35,7 @@ interface TradeStats {
 
 // Smart trade counting logic using Zerodha brokerage for Equity Delivery
 function calculateSmartTrades(
-  recommendations: typeof sampleRecommendations,
+  recommendations: DailyRecommendation[],
   mode: InvestmentMode,
   startingAmount: number,
   nifty50Prices?: Record<string, number>,
@@ -48,7 +46,6 @@ function calculateSmartTrades(
   openPositions: Record<string, { entryDate: string; entryPrice: number; decision: Decision }>;
 } {
   const hasRealNifty = nifty50Prices && Object.keys(nifty50Prices).length > 0;
-  const niftyHistory = hasRealNifty ? null : getNifty50IndexHistory();
   const sortedRecs = [...recommendations].sort((a, b) => new Date(a.date).getTime() - new Date(b.date).getTime());
 
   // Precompute real Nifty start price for comparison
@@ -75,7 +72,6 @@ function calculateSmartTrades(
 
   let portfolioValue = startingAmount;
   let niftyValue = startingAmount;
-  const niftyStartValue = niftyHistory?.[0]?.value || 21500;
 
   const portfolioData = sortedRecs.map((rec) => {
     const stocks = getStocksToTrack(rec);
@@ -89,8 +85,7 @@ function calculateSmartTrades(
       const decision = analysis.decision;
       const prevPosition = openPositions[symbol];
 
-      const backtest = getBacktestResult(symbol);
-      const currentPrice = backtest?.current_price || 1000;
+      const currentPrice = 1000; // Nominal price for position sizing
       const quantity = Math.floor(investmentPerStock / currentPrice);
 
       if (decision === 'BUY') {
@@ -160,11 +155,6 @@ function calculateSmartTrades(
       if (closestDate && nifty50Prices[closestDate]) {
         niftyValue = startingAmount * (nifty50Prices[closestDate] / niftyStartPrice);
       }
-    } else if (niftyHistory) {
-      const niftyPoint = niftyHistory.find(n => n.date === rec.date);
-      if (niftyPoint) {
-        niftyValue = startingAmount * (niftyPoint.value / niftyStartValue);
-      }
     }
 
     return {
@@ -214,8 +204,7 @@ function getValueColorClass(value: number): string {
 
 export default function PortfolioSimulator({
   className = '',
-  recommendations = sampleRecommendations,
-  isUsingMockData = true,  // Default to true since this uses simulated returns
+  recommendations = [],
   nifty50Prices,
   allBacktestData,
 }: PortfolioSimulatorProps) {
@@ -705,16 +694,6 @@ export default function PortfolioSimulator({
         </div>
       )}
 
-      {/* Demo Data Notice */}
-      {isUsingMockData && (
-        <div className="flex items-center gap-2 px-3 py-2 bg-amber-50 dark:bg-amber-900/20 border border-amber-200 dark:border-amber-800 rounded-lg mt-3">
-          <AlertCircle className="w-3.5 h-3.5 text-amber-600 dark:text-amber-400 flex-shrink-0" />
-          <span className="text-[10px] text-amber-700 dark:text-amber-300">
-            Simulation uses demo data. Results are illustrative only.
-          </span>
-        </div>
-      )}
-
       <p className="text-[10px] text-gray-400 dark:text-gray-500 mt-3 text-center">
         Simulated using Zerodha Equity Delivery rates (0% brokerage, STT 0.1%, Exchange 0.00345%, SEBI 0.0001%, Stamp 0.015%).
         {investmentMode === 'topPicks' ? ' Investing in Top Picks only.' : ' Investing in all 50 stocks.'}

frontend/src/components/ReturnDistributionChart.tsx

@@ -1,19 +1,17 @@
 import { useState } from 'react';
 import { BarChart, Bar, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer } from 'recharts';
 import { X } from 'lucide-react';
-import { getReturnDistribution } from '../data/recommendations';
 import type { ReturnBucket } from '../types';
 
 export interface ReturnDistributionChartProps {
   height?: number;
   className?: string;
-  data?: ReturnBucket[];  // Optional prop for real data
+  data?: ReturnBucket[];
 }
 
 export default function ReturnDistributionChart({ height = 200, className = '', data: propData }: ReturnDistributionChartProps) {
   const [selectedBucket, setSelectedBucket] = useState<{ range: string; stocks: string[] } | null>(null);
-  // Use provided data or fall back to mock data
-  const data = propData || getReturnDistribution();
+  const data = propData || [];
 
   if (data.every(d => d.count === 0)) {
     return (

frontend/src/components/ReturnExplainModal.tsx

@@ -1,5 +1,6 @@
 import { X, CheckCircle, XCircle, Calculator } from 'lucide-react';
-import type { ReturnBreakdown } from '../data/recommendations';
+import { createPortal } from 'react-dom';
+import type { ReturnBreakdown } from '../types';
 
 interface ReturnExplainModalProps {
   isOpen: boolean;
@@ -18,7 +19,7 @@ export default function ReturnExplainModal({ isOpen, onClose, breakdown, date }:
     year: 'numeric',
   });
 
-  return (
+  return createPortal(
     <div className="fixed inset-0 z-50 flex items-center justify-center p-4">
       {/* Backdrop */}
       <div
@@ -214,6 +215,7 @@ export default function ReturnExplainModal({ isOpen, onClose, breakdown, date }:
           </button>
         </div>
       </div>
-    </div>
+    </div>,
+    document.body
   );
 }

frontend/src/components/RiskMetricsCard.tsx

@@ -1,20 +1,22 @@
 import { TrendingUp, TrendingDown, Activity, Target } from 'lucide-react';
-import { calculateRiskMetrics } from '../data/recommendations';
 import { useState } from 'react';
 import InfoModal, { InfoButton } from './InfoModal';
 import type { RiskMetrics } from '../types';
 
 export interface RiskMetricsCardProps {
   className?: string;
-  metrics?: RiskMetrics;  // Optional prop for real data
+  metrics?: RiskMetrics;
 }
 
 type MetricModal = 'sharpe' | 'drawdown' | 'winloss' | 'winrate' | null;
 
+const defaultMetrics: RiskMetrics = {
+  sharpeRatio: 0, maxDrawdown: 0, winLossRatio: 0, winRate: 0, volatility: 0, totalTrades: 0,
+};
+
 export default function RiskMetricsCard({ className = '', metrics: propMetrics }: RiskMetricsCardProps) {
   const [activeModal, setActiveModal] = useState<MetricModal>(null);
-  // Use provided metrics or fall back to mock data
-  const metrics = propMetrics || calculateRiskMetrics();
+  const metrics = propMetrics || defaultMetrics;
 
   // Color classes for metric values
   const COLOR_GOOD = 'text-green-600 dark:text-green-400';

frontend/src/components/SettingsModal.tsx

@@ -1,10 +1,11 @@
 import { useState } from 'react';
+import { createPortal } from 'react-dom';
 import {
   X, Settings, Cpu, Key, Zap, Brain, Sparkles,
-  Eye, EyeOff, Check, AlertCircle, RefreshCw
+  Eye, EyeOff, Check, AlertCircle, RefreshCw, Clock
 } from 'lucide-react';
-import { useSettings, MODELS, PROVIDERS } from '../contexts/SettingsContext';
-import type { ModelId, ProviderId } from '../contexts/SettingsContext';
+import { useSettings, MODELS, PROVIDERS, TIMEZONES } from '../contexts/SettingsContext';
+import type { ModelId, ProviderId, TimezoneId } from '../contexts/SettingsContext';
 
 export default function SettingsModal() {
   const { settings, updateSettings, resetSettings, isSettingsOpen, closeSettings } = useSettings();
@@ -52,7 +53,7 @@ export default function SettingsModal() {
 
   const selectedProvider = PROVIDERS[settings.provider];
 
-  return (
+  return createPortal(
     <div className="fixed inset-0 z-50 overflow-y-auto">
       {/* Backdrop */}
       <div
@@ -296,6 +297,102 @@ export default function SettingsModal() {
                 </p>
               </div>
             </section>
+
+            {/* Auto-Analyze Schedule */}
+            <section>
+              <h3 className="flex items-center gap-2 text-sm font-semibold text-gray-900 dark:text-gray-100 mb-3">
+                <Clock className="w-4 h-4 text-indigo-500" />
+                Auto-Analyze Schedule
+              </h3>
+
+              {/* Enable Toggle */}
+              <div className="flex items-center justify-between mb-4">
+                <div>
+                  <div className="text-xs font-medium text-gray-700 dark:text-gray-300">
+                    Daily Auto-Analyze
+                  </div>
+                  <div className="text-[10px] text-gray-500 dark:text-gray-400">
+                    Automatically run Analyze All at the scheduled time
+                  </div>
+                </div>
+                <button
+                  onClick={() => updateSettings({ autoAnalyzeEnabled: !settings.autoAnalyzeEnabled })}
+                  className={`relative w-10 h-5 rounded-full transition-colors ${
+                    settings.autoAnalyzeEnabled
+                      ? 'bg-nifty-600'
+                      : 'bg-gray-300 dark:bg-slate-600'
+                  }`}
+                >
+                  <span className={`absolute top-0.5 left-0.5 w-4 h-4 rounded-full bg-white shadow transition-transform ${
+                    settings.autoAnalyzeEnabled ? 'translate-x-5' : 'translate-x-0'
+                  }`} />
+                </button>
+              </div>
+
+              {/* Timezone */}
+              <div className={`mb-3 ${!settings.autoAnalyzeEnabled ? 'opacity-40 pointer-events-none' : ''}`}>
+                <label className="text-[10px] font-medium text-gray-500 dark:text-gray-400 mb-1 block">Timezone</label>
+                <select
+                  value={settings.autoAnalyzeTimezone}
+                  onChange={(e) => updateSettings({ autoAnalyzeTimezone: e.target.value as TimezoneId })}
+                  className="w-full px-3 py-2 rounded-lg border border-gray-200 dark:border-slate-700 bg-white dark:bg-slate-800 text-gray-900 dark:text-gray-100 text-sm focus:outline-none focus:ring-2 focus:ring-nifty-500"
+                >
+                  {TIMEZONES.map(tz => (
+                    <option key={tz.id} value={tz.id}>
+                      {tz.label} (UTC{tz.offset})
+                    </option>
+                  ))}
+                </select>
+              </div>
+
+              {/* Time Picker */}
+              <div className={`flex items-center gap-3 ${!settings.autoAnalyzeEnabled ? 'opacity-40 pointer-events-none' : ''}`}>
+                <div className="flex-1">
+                  <label className="text-[10px] font-medium text-gray-500 dark:text-gray-400 mb-1 block">Hour</label>
+                  <select
+                    value={settings.autoAnalyzeTime.split(':')[0]}
+                    onChange={(e) => {
+                      const minute = settings.autoAnalyzeTime.split(':')[1];
+                      updateSettings({ autoAnalyzeTime: `${e.target.value}:${minute}` });
+                    }}
+                    className="w-full px-3 py-2 rounded-lg border border-gray-200 dark:border-slate-700 bg-white dark:bg-slate-800 text-gray-900 dark:text-gray-100 text-sm font-mono focus:outline-none focus:ring-2 focus:ring-nifty-500"
+                  >
+                    {Array.from({ length: 24 }, (_, i) => (
+                      <option key={i} value={String(i).padStart(2, '0')}>
+                        {String(i).padStart(2, '0')}
+                      </option>
+                    ))}
+                  </select>
+                </div>
+                <span className="text-lg font-bold text-gray-400 dark:text-gray-500 mt-4">:</span>
+                <div className="flex-1">
+                  <label className="text-[10px] font-medium text-gray-500 dark:text-gray-400 mb-1 block">Minute</label>
+                  <select
+                    value={settings.autoAnalyzeTime.split(':')[1]}
+                    onChange={(e) => {
+                      const hour = settings.autoAnalyzeTime.split(':')[0];
+                      updateSettings({ autoAnalyzeTime: `${hour}:${e.target.value}` });
+                    }}
+                    className="w-full px-3 py-2 rounded-lg border border-gray-200 dark:border-slate-700 bg-white dark:bg-slate-800 text-gray-900 dark:text-gray-100 text-sm font-mono focus:outline-none focus:ring-2 focus:ring-nifty-500"
+                  >
+                    {Array.from({ length: 12 }, (_, i) => (
+                      <option key={i} value={String(i * 5).padStart(2, '0')}>
+                        {String(i * 5).padStart(2, '0')}
+                      </option>
+                    ))}
+                  </select>
+                </div>
+              </div>
+
+              {/* Preview */}
+              {settings.autoAnalyzeEnabled && (
+                <div className="mt-3 p-2.5 rounded-lg bg-indigo-50 dark:bg-indigo-900/20 border border-indigo-100 dark:border-indigo-800">
+                  <p className="text-xs text-indigo-700 dark:text-indigo-300 font-medium">
+                    Runs daily at {settings.autoAnalyzeTime} {TIMEZONES.find(tz => tz.id === settings.autoAnalyzeTimezone)?.label || settings.autoAnalyzeTimezone} when the backend is running
+                  </p>
+                </div>
+              )}
+            </section>
           </div>
 
           {/* Footer */}
@@ -315,6 +412,7 @@ export default function SettingsModal() {
           </div>
         </div>
       </div>
-    </div>
+    </div>,
+    document.body
   );
 }

frontend/src/components/TerminalModal.tsx

@@ -1,4 +1,5 @@
 import { useState, useEffect, useRef, useCallback } from 'react';
+import { createPortal } from 'react-dom';
 import { X, Terminal, Trash2, Download, Pause, Play, ChevronDown, Plus, Minus } from 'lucide-react';
 
 interface LogEntry {
@@ -224,7 +225,7 @@ export default function TerminalModal({ isOpen, onClose, isAnalyzing }: Terminal
 
   if (!isOpen) return null;
 
-  return (
+  return createPortal(
     <div className="fixed inset-0 z-50 flex items-end sm:items-center justify-center sm:p-4">
       {/* Backdrop */}
       <div className="absolute inset-0 bg-black/70 backdrop-blur-sm" onClick={onClose} />
@@ -407,6 +408,7 @@ export default function TerminalModal({ isOpen, onClose, isAnalyzing }: Terminal
           </span>
         </div>
       </div>
-    </div>
+    </div>,
+    document.body
   );
 }

frontend/src/components/TopPicks.tsx

@@ -1,9 +1,7 @@
 import { Link } from 'react-router-dom';
 import { Trophy, AlertTriangle, TrendingUp, TrendingDown, ChevronRight } from 'lucide-react';
 import type { TopPick, StockToAvoid } from '../types';
-import BackgroundSparkline from './BackgroundSparkline';
 import { RankBadge } from './StockCard';
-import { getBacktestResult } from '../data/recommendations';
 
 interface TopPicksProps {
   picks: TopPick[];
@@ -24,7 +22,6 @@ export default function TopPicks({ picks }: TopPicksProps) {
 
       <div className="grid grid-cols-1 sm:grid-cols-3 gap-2">
         {picks.map((pick, index) => {
-          const backtest = getBacktestResult(pick.symbol);
           return (
             <Link
               key={pick.symbol}
@@ -36,11 +33,6 @@ export default function TopPicks({ picks }: TopPicksProps) {
                   : 'linear-gradient(135deg, rgba(16,185,129,0.04), rgba(5,150,105,0.01))',
               }}
             >
-              {backtest && (
-                <div className="absolute inset-0 opacity-[0.06]">
-                  <BackgroundSparkline data={backtest.price_history} trend="up" />
-                </div>
-              )}
               <div className="relative z-10">
                 <div className="flex items-center justify-between mb-2">
                   <div className="flex items-center gap-2">
@@ -91,7 +83,6 @@ export function StocksToAvoid({ stocks }: StocksToAvoidProps) {
 
       <div className="grid grid-cols-1 sm:grid-cols-2 gap-2">
         {stocks.map((stock) => {
-          const backtest = getBacktestResult(stock.symbol);
           return (
             <Link
               key={stock.symbol}
@@ -99,11 +90,6 @@ export function StocksToAvoid({ stocks }: StocksToAvoidProps) {
               className="group relative overflow-hidden rounded-xl border border-red-200/40 dark:border-red-800/25 p-3 transition-all hover:border-red-300 dark:hover:border-red-700/40"
               style={{ background: 'linear-gradient(135deg, rgba(239,68,68,0.04), rgba(220,38,38,0.01))' }}
             >
-              {backtest && (
-                <div className="absolute inset-0 opacity-[0.06]">
-                  <BackgroundSparkline data={backtest.price_history} trend="down" />
-                </div>
-              )}
               <div className="relative z-10">
                 <div className="flex items-center justify-between mb-2">
                   <span className="font-bold text-gray-900 dark:text-gray-100 text-sm">{stock.symbol}</span>

frontend/src/contexts/SettingsContext.tsx

@@ -27,6 +27,30 @@ export const PROVIDERS = {
 export type ModelId = keyof typeof MODELS;
 export type ProviderId = keyof typeof PROVIDERS;
 
+// Common timezones with labels and IANA identifiers
+export const TIMEZONES = [
+  { id: 'Asia/Kolkata', label: 'IST (India)', offset: '+05:30' },
+  { id: 'Asia/Tokyo', label: 'JST (Japan)', offset: '+09:00' },
+  { id: 'Asia/Shanghai', label: 'CST (China)', offset: '+08:00' },
+  { id: 'Asia/Singapore', label: 'SGT (Singapore)', offset: '+08:00' },
+  { id: 'Asia/Dubai', label: 'GST (Dubai)', offset: '+04:00' },
+  { id: 'Asia/Hong_Kong', label: 'HKT (Hong Kong)', offset: '+08:00' },
+  { id: 'Europe/London', label: 'GMT/BST (London)', offset: '+00:00' },
+  { id: 'Europe/Paris', label: 'CET (Paris/Berlin)', offset: '+01:00' },
+  { id: 'Europe/Moscow', label: 'MSK (Moscow)', offset: '+03:00' },
+  { id: 'America/New_York', label: 'EST (New York)', offset: '-05:00' },
+  { id: 'America/Chicago', label: 'CST (Chicago)', offset: '-06:00' },
+  { id: 'America/Los_Angeles', label: 'PST (Los Angeles)', offset: '-08:00' },
+  { id: 'America/Sao_Paulo', label: 'BRT (Sao Paulo)', offset: '-03:00' },
+  { id: 'Australia/Sydney', label: 'AEST (Sydney)', offset: '+10:00' },
+  { id: 'Australia/Perth', label: 'AWST (Perth)', offset: '+08:00' },
+  { id: 'Pacific/Auckland', label: 'NZST (Auckland)', offset: '+12:00' },
+  { id: 'Africa/Johannesburg', label: 'SAST (Johannesburg)', offset: '+02:00' },
+  { id: 'UTC', label: 'UTC', offset: '+00:00' },
+] as const;
+
+export type TimezoneId = typeof TIMEZONES[number]['id'];
+
 interface Settings {
   // Model settings
   deepThinkModel: ModelId;
@@ -41,6 +65,11 @@ interface Settings {
   // Analysis settings
   maxDebateRounds: number;
   parallelWorkers: number;
+
+  // Auto-analyze schedule
+  autoAnalyzeEnabled: boolean;
+  autoAnalyzeTime: string; // "HH:MM" in 24hr format
+  autoAnalyzeTimezone: TimezoneId;
 }
 
 interface SettingsContextType {
@@ -59,6 +88,9 @@ const DEFAULT_SETTINGS: Settings = {
   anthropicApiKey: '',
   maxDebateRounds: 1,
   parallelWorkers: 3,
+  autoAnalyzeEnabled: false,
+  autoAnalyzeTime: '09:00',
+  autoAnalyzeTimezone: 'Asia/Kolkata',
 };
 
 const STORAGE_KEY = 'nifty50ai_settings';
@@ -92,6 +124,34 @@ export function SettingsProvider({ children }: { children: ReactNode }) {
     }
   }, [settings]);
 
+  // Sync auto-analyze schedule to backend whenever it changes
+  useEffect(() => {
+    const syncSchedule = async () => {
+      try {
+        await fetch('http://localhost:8001/settings/schedule', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({
+            enabled: settings.autoAnalyzeEnabled,
+            time: settings.autoAnalyzeTime,
+            timezone: settings.autoAnalyzeTimezone,
+            config: {
+              deep_think_model: settings.deepThinkModel,
+              quick_think_model: settings.quickThinkModel,
+              provider: settings.provider,
+              api_key: settings.anthropicApiKey || undefined,
+              max_debate_rounds: settings.maxDebateRounds,
+              parallel_workers: settings.parallelWorkers,
+            },
+          }),
+        });
+      } catch {
+        // Backend may not be running — silently ignore
+      }
+    };
+    syncSchedule();
+  }, [settings.autoAnalyzeEnabled, settings.autoAnalyzeTime, settings.autoAnalyzeTimezone, settings.deepThinkModel, settings.quickThinkModel, settings.provider, settings.anthropicApiKey, settings.maxDebateRounds, settings.parallelWorkers]);
+
   const updateSettings = (newSettings: Partial<Settings>) => {
     setSettings(prev => ({ ...prev, ...newSettings }));
   };

frontend/src/index.css

@@ -1,5 +1,8 @@
 @import "tailwindcss";
 
+/* Use class-based dark mode (html.dark) instead of media query */
+@variant dark (&:where(.dark, .dark *));
+
 @theme {
   /* Custom colors */
   --color-nifty-50: #f0f9ff;

frontend/src/pages/Dashboard.tsx

@@ -5,8 +5,6 @@ import TopPicks, { StocksToAvoid } from '../components/TopPicks';
 import { DecisionBadge, HoldDaysBadge, RankBadge } from '../components/StockCard';
 import TerminalModal from '../components/TerminalModal';
 import HowItWorks from '../components/HowItWorks';
-import BackgroundSparkline from '../components/BackgroundSparkline';
-import { getLatestRecommendation, getBacktestResult as getStaticBacktestResult } from '../data/recommendations';
 import { api } from '../services/api';
 import { useSettings } from '../contexts/SettingsContext';
 import { useNotification } from '../contexts/NotificationContext';
@@ -19,28 +17,17 @@ export default function Dashboard() {
   // State for real API data
   const [recommendation, setRecommendation] = useState<DailyRecommendation | null>(null);
   const [isLoadingData, setIsLoadingData] = useState(true);
-  const [isUsingMockData, setIsUsingMockData] = useState(false);
 
-  // Fetch real recommendation from API
+  // Fetch recommendation from API
   const fetchRecommendation = useCallback(async () => {
     setIsLoadingData(true);
     try {
       const data = await api.getLatestRecommendation();
       if (data && data.analysis && Object.keys(data.analysis).length > 0) {
         setRecommendation(data);
-        setIsUsingMockData(false);
-      } else {
-        // API returned empty data, fall back to mock
-        const mockData = getLatestRecommendation();
-        setRecommendation(mockData || null);
-        setIsUsingMockData(true);
       }
     } catch (error) {
       console.error('Failed to fetch recommendation from API:', error);
-      // Fall back to mock data
-      const mockData = getLatestRecommendation();
-      setRecommendation(mockData || null);
-      setIsUsingMockData(true);
     } finally {
       setIsLoadingData(false);
     }
@@ -493,16 +480,6 @@ export default function Dashboard() {
           </div>
         </div>
 
-        {/* Mock Data Indicator */}
-        {isUsingMockData && (
-          <div className="mt-3 flex items-center gap-2 px-3 py-2 bg-amber-50 dark:bg-amber-900/20 border border-amber-200 dark:border-amber-800 rounded-lg">
-            <AlertCircle className="w-4 h-4 text-amber-600 dark:text-amber-400 flex-shrink-0" />
-            <span className="text-xs text-amber-700 dark:text-amber-300">
-              Using demo data. Run "Analyze All" or start the backend server for real AI recommendations.
-            </span>
-          </div>
-        )}
-
         {/* Analysis Progress Banner */}
         {isAnalyzing && analysisProgress && (
           <div className="mt-3 p-3 bg-blue-50 dark:bg-blue-900/30 rounded-lg border border-blue-200 dark:border-blue-800">
@@ -662,8 +639,6 @@ export default function Dashboard() {
           {filteredItems.map((item) => {
             // COMPLETED with analysis data: clickable link
             if (item.liveState === 'completed' && item.analysis) {
-              const backtest = isUsingMockData ? getStaticBacktestResult(item.symbol) : null;
-              const trend = item.analysis.decision === 'BUY' ? 'up' : item.analysis.decision === 'SELL' ? 'down' : 'flat';
               return (
                 <Link
                   key={item.symbol}
@@ -671,11 +646,6 @@ export default function Dashboard() {
                   className="card-hover p-2 group relative overflow-hidden"
                   role="listitem"
                 >
-                  {backtest && (
-                    <div className="absolute inset-0 opacity-[0.06]">
-                      <BackgroundSparkline data={backtest.price_history.slice(-15)} trend={trend} />
-                    </div>
-                  )}
                   <div className="relative z-10">
                     <div className="flex items-center gap-1.5 mb-0.5">
                       <RankBadge rank={item.analysis.rank} size="small" />

frontend/src/pages/StockDetail.tsx

@@ -7,7 +7,6 @@ import {
 } from 'lucide-react';
 import { NIFTY_50_STOCKS } from '../types';
 import type { DailyRecommendation, StockAnalysis } from '../types';
-import { sampleRecommendations, getStockHistory as getStaticStockHistory, getRawAnalysis } from '../data/recommendations';
 import { DecisionBadge, ConfidenceBadge, RiskBadge, HoldDaysBadge, RankBadge } from '../components/StockCard';
 import AIAnalysisPanel from '../components/AIAnalysisPanel';
 import StockPriceChart from '../components/StockPriceChart';
@@ -79,17 +78,9 @@ export default function StockDetail() {
         if (rec && rec.analysis && Object.keys(rec.analysis).length > 0) {
           setLatestRecommendation(rec);
           setAnalysis(rec.analysis[symbol || '']);
-        } else {
-          // Fallback to static data
-          const mockRec = sampleRecommendations[0];
-          setLatestRecommendation(mockRec);
-          setAnalysis(mockRec?.analysis[symbol || '']);
         }
-      } catch {
-        // Fallback to static data
-        const mockRec = sampleRecommendations[0];
-        setLatestRecommendation(mockRec);
-        setAnalysis(mockRec?.analysis[symbol || '']);
+      } catch (err) {
+        console.error('Failed to fetch recommendation:', err);
       }
 
       try {
@@ -97,13 +88,9 @@ export default function StockDetail() {
         const historyData = await api.getStockHistory(symbol || '');
         if (historyData && historyData.history && historyData.history.length > 0) {
           setHistory(historyData.history);
-        } else {
-          // Fallback to static data
-          setHistory(symbol ? getStaticStockHistory(symbol) : []);
         }
-      } catch {
-        // Fallback to static data
-        setHistory(symbol ? getStaticStockHistory(symbol) : []);
+      } catch (err) {
+        console.error('Failed to fetch stock history:', err);
       }
 
     };
@@ -824,9 +811,9 @@ export default function StockDetail() {
           </section>
 
           {/* AI Analysis Panel */}
-          {analysis && (analysis.raw_analysis || getRawAnalysis(symbol || '')) && (
+          {analysis?.raw_analysis && (
             <AIAnalysisPanel
-              analysis={analysis.raw_analysis || getRawAnalysis(symbol || '') || ''}
+              analysis={analysis.raw_analysis}
               decision={analysis.decision}
             />
           )}

frontend/src/pages/Stocks.tsx

@@ -1,15 +1,29 @@
-import { useState, useMemo } from 'react';
+import { useState, useEffect, useMemo } from 'react';
 import { Link } from 'react-router-dom';
 import { Search, Building2 } from 'lucide-react';
 import { NIFTY_50_STOCKS } from '../types';
-import { getLatestRecommendation } from '../data/recommendations';
+import type { DailyRecommendation } from '../types';
 import { DecisionBadge, ConfidenceBadge } from '../components/StockCard';
+import { api } from '../services/api';
 
 export default function Stocks() {
   const [search, setSearch] = useState('');
   const [sectorFilter, setSectorFilter] = useState<string>('ALL');
+  const [recommendation, setRecommendation] = useState<DailyRecommendation | null>(null);
 
-  const recommendation = getLatestRecommendation();
+  useEffect(() => {
+    const fetchData = async () => {
+      try {
+        const rec = await api.getLatestRecommendation();
+        if (rec && rec.analysis && Object.keys(rec.analysis).length > 0) {
+          setRecommendation(rec);
+        }
+      } catch (err) {
+        console.error('Failed to fetch recommendations:', err);
+      }
+    };
+    fetchData();
+  }, []);
 
   const sectors = useMemo(() => {
     const sectorSet = new Set(NIFTY_50_STOCKS.map(s => s.sector).filter(Boolean));

frontend/src/services/api.ts

@@ -349,6 +349,35 @@ class ApiService {
     return this.fetch('/analyze/all/cancel', { method: 'POST', noCache: true });
   }
 
+  // ============== Schedule Methods ==============
+
+  /**
+   * Set the auto-analyze schedule
+   */
+  async setSchedule(config: {
+    enabled: boolean;
+    time: string;
+    config: Record<string, unknown>;
+  }): Promise<{ status: string; message: string }> {
+    return this.fetch('/settings/schedule', {
+      method: 'POST',
+      body: JSON.stringify(config),
+      noCache: true,
+    });
+  }
+
+  /**
+   * Get the current auto-analyze schedule
+   */
+  async getSchedule(): Promise<{
+    enabled: boolean;
+    time: string;
+    config: Record<string, unknown>;
+    last_run_date: string | null;
+  }> {
+    return this.fetch('/settings/schedule', { noCache: true });
+  }
+
   // ============== Stock Price History Methods ==============
 
   /**
@@ -375,6 +404,35 @@ class ApiService {
     return this.fetch('/nifty50/history');
   }
 
+  // ============== History Bundle ==============
+
+  /**
+   * Get all data the History page needs in a single call.
+   * Returns recommendations + all backtest results + accuracy metrics + nifty50 prices.
+   */
+  async getHistoryBundle(): Promise<{
+    recommendations: DailyRecommendation[];
+    backtest_by_date: Record<string, Record<string, {
+      return_1d?: number;
+      return_1w?: number;
+      return_1m?: number;
+      return_at_hold?: number;
+      hold_days?: number;
+      prediction_correct?: boolean;
+      decision: string;
+    }>>;
+    accuracy: {
+      overall_accuracy: number;
+      total_predictions: number;
+      correct_predictions: number;
+      by_decision: Record<string, { accuracy: number; total: number; correct: number }>;
+      by_confidence: Record<string, { accuracy: number; total: number; correct: number }>;
+    };
+    nifty50_prices: Record<string, number>;
+  }> {
+    return this.fetch('/history/bundle');
+  }
+
   // ============== Backtest Methods ==============
 
   /**
@@ -416,6 +474,37 @@ class ApiService {
     return this.fetch(`/backtest/${date}`);
   }
 
+  /**
+   * Get detailed backtest data with live prices, formulas, agent reports
+   */
+  async getDetailedBacktest(date: string): Promise<{
+    date: string;
+    total_stocks: number;
+    stocks: Array<{
+      symbol: string;
+      company_name: string;
+      rank?: number;
+      decision: string;
+      confidence: string;
+      risk: string;
+      hold_days: number;
+      hold_days_elapsed: number;
+      hold_period_active: boolean;
+      price_at_prediction: number | null;
+      price_current: number | null;
+      price_at_hold_end: number | null;
+      return_current: number | null;
+      return_at_hold: number | null;
+      prediction_correct: boolean | null;
+      formula: string;
+      raw_analysis: string;
+      agent_summary: Record<string, string>;
+      debate_summary: Record<string, string>;
+    }>;
+  }> {
+    return this.fetch(`/backtest/${date}/detailed`, { noCache: true });
+  }
+
   /**
    * Calculate backtest for all recommendations on a date
    */

frontend/src/types/index.ts

@@ -2,6 +2,23 @@ export type Decision = 'BUY' | 'SELL' | 'HOLD';
 export type Confidence = 'HIGH' | 'MEDIUM' | 'LOW';
 export type Risk = 'HIGH' | 'MEDIUM' | 'LOW';
 
+export interface ReturnBreakdown {
+  correctPredictions: {
+    count: number;
+    totalReturn: number;
+    avgReturn: number;
+    stocks: { symbol: string; decision: string; return1d: number }[];
+  };
+  incorrectPredictions: {
+    count: number;
+    totalReturn: number;
+    avgReturn: number;
+    stocks: { symbol: string; decision: string; return1d: number }[];
+  };
+  weightedReturn: number;
+  formula: string;
+}
+
 // Backtest Types
 export interface PricePoint {
   date: string;

frontend/tailwind.config.js

@@ -1,5 +1,6 @@
 /** @type {import('tailwindcss').Config} */
 export default {
+  darkMode: 'class',
   content: [
     "./index.html",
     "./src/**/*.{js,ts,jsx,tsx}",

package.json

@@ -1,22 +1,22 @@
 {
   "name": "tradingagents",
   "version": "1.0.0",
-  "description": "<p align=\"center\">   <img src=\"assets/TauricResearch.png\" style=\"width: 60%; height: auto;\"> </p>",
+  "description": "Multi-Agent LLM Financial Trading Framework",
   "main": "index.js",
   "scripts": {
     "test": "echo \"Error: no test specified\" && exit 1"
   },
   "repository": {
     "type": "git",
-    "url": "git+https://github.com/TauricResearch/TradingAgents.git"
+    "url": "git+https://github.com/hjlabs/TradingAgents.git"
   },
   "keywords": [],
   "author": "",
   "license": "ISC",
   "bugs": {
-    "url": "https://github.com/TauricResearch/TradingAgents/issues"
+    "url": "https://github.com/hjlabs/TradingAgents/issues"
   },
-  "homepage": "https://github.com/TauricResearch/TradingAgents#readme",
+  "homepage": "https://github.com/hjlabs/TradingAgents#readme",
   "dependencies": {
     "playwright": "^1.58.1"
   }

setup.py

@@ -10,7 +10,7 @@ setup(
     description="Multi-Agents LLM Financial Trading Framework",
     author="TradingAgents Team",
     author_email="yijia.xiao@cs.ucla.edu",
-    url="https://github.com/TauricResearch",
+    url="https://github.com/hjlabs/TradingAgents",
     packages=find_packages(),
     install_requires=[
         "langchain>=0.1.0",

tradingagents/claude_max_llm.py

@@ -162,8 +162,10 @@ class ClaudeMaxLLM(BaseChatModel):
             user_prompt: The user prompt/query
         """
         # Create environment without ANTHROPIC_API_KEY to force subscription auth
+        # Also remove CLAUDECODE to allow nested CLI calls from within Claude Code sessions
         env = os.environ.copy()
         env.pop("ANTHROPIC_API_KEY", None)
+        env.pop("CLAUDECODE", None)
 
         # Build the command with --system-prompt to override Claude Code's default behavior
         cmd = [

tradingagents/dataflows/y_finance.py

@@ -694,22 +694,19 @@ def get_yfinance_news(
             news = ticker_obj.news
             if news and len(news) > 0:
                 for i, article in enumerate(news[:10]):
-                    title = article.get("title", "No title")
-                    publisher = article.get("publisher", "Unknown")
-                    publish_time = article.get("providerPublishTime", "")
-                    if publish_time:
-                        try:
-                            from datetime import datetime as _dt
-                            publish_time = _dt.fromtimestamp(publish_time).strftime("%Y-%m-%d %H:%M")
-                        except Exception:
-                            pass
-                    related = article.get("relatedTickers", [])
-                    related_str = ", ".join(related) if related else "N/A"
+                    # yfinance news has nested 'content' structure
+                    content = article.get("content", article)
+                    title = content.get("title", article.get("title", "No title"))
+                    provider = content.get("provider", {})
+                    publisher = provider.get("displayName", article.get("publisher", "Unknown")) if isinstance(provider, dict) else "Unknown"
+                    publish_time = content.get("pubDate", article.get("providerPublishTime", ""))
+                    summary = content.get("summary", "")
 
                     sections.append(f"## Article {i+1}: {title}")
                     sections.append(f"  Publisher: {publisher}")
                     sections.append(f"  Published: {publish_time}")
-                    sections.append(f"  Related Tickers: {related_str}")
+                    if summary:
+                        sections.append(f"  Summary: {summary[:200]}")
                     sections.append("")
             else:
                 sections.append("No news articles available from Yahoo Finance.\n")