merge: resolve conflicts, keep local framework changes

Fix order pricing: 1-tick advantage at best bid/ask + process guard
A-S was quoting at best bid/ask (0% win) — orders filled but 0.04% round-trip maker fee exceeded spread capture. Now: bid+1 / ask-1 = captures spread minus 1 tick each side. Signal-driven strategies: same 1-tick pricing instead of 0.03% offset that crossed the book or sat too far away. Added fcntl file lock to prevent duplicate live nodes. Added IOC fallback (market-crossing) when post-only rejected. A-S win rate: 0% → 25% (first 8 trades with new pricing)
2026-08-06 17:52:55 +08:00 · 2026-08-06 09:47:21 +00:00 · 2026-08-06 17:43:47 +08:00 · 2026-08-06 17:33:52 +08:00 · 2026-08-06 17:23:49 +08:00 · 2026-08-06 09:13:51 +00:00
206 changed files with 88489 additions and 14147 deletions
@@ -1,14 +1,31 @@
+# Python
 __pycache__/
 *.py[cod]
 *.egg-info/
+dist/
 .venv/
-venv/
+
+# Next.js / Dashboard
+.next/
+out/
+node_modules/
+
+# Environment
 .env
-*.pem
-*_pk
-data/
-*.parquet
-.ipynb_checkpoints/
+*.env.local
+
+# IDE
 .idea/
 .vscode/
+*.swp
+*.swo
+
+# Runtime artifacts
+/tmp/
+*.log
+metrics.json
+paper_metrics.json
+
+# OS
 .DS_Store
+Thumbs.db
@@ -1,59 +1,168 @@
-# FTDT Quant Lab — Quantitative Trading Strategies
+# FTDT Quant Lab

-A collection of quantitative trading strategies running on
-**Hyperliquid Testnet** via **Nautilus Trader**. Built as part of
-my professional portfolio to demonstrate algorithmic trading,
-market microstructure, and risk management skills.
+Production multi-strategy quant trading system running on Hyperliquid.  
+Live testnet node, paper trading simulator, historical backtesting, and real-time dashboard.

-## What's inside
+**Live:** https://ftdt.io/cv

-Five strategies, from simple to advanced:
+---

-| # | Strategy | Concept |
-|---|----------|---------|
-| 1 | Order Book Imbalance | Trades on L2 bid/ask pressure |
-| 2 | Iceberg / TWAP Detection | Follows whale accumulation patterns |
-| 3 | Funding Rate Arbitrage | Delta-neutral carry trade |
-| 4 | Pairs Trading (BTC/ETH) | Cointegration-based stat arb |
-| 5 | Avellaneda-Stoikov Market Making | Stochastic optimal control |
+## Stack

-All strategies share a common risk manager and portfolio tracker.
+| Layer | Technology |
+|-------|-----------|
+| **Runtime** | Python 3.13 (async trading) |
+| **API Client** | nautilus_trader (Hyperliquid SDK, Rust bindings) |
+| **Dashboard** | Next.js 16 (static export) + shadcn/ui + Framer Motion |
+| **Design System** | Hallmark Cobalt — Ubuntu font, hairline borders, cool paper palette |
+| **Reverse Proxy** | Caddy → auto HTTPS |
+| **WebSocket** | FastAPI (live/paper streaming) |
+| **Data** | PostgreSQL 17 (`ftdt_quant`), JSON metrics files |
+| **Backtesting** | Custom dollar-bar engine + numpy |
+| **Infra** | OVH VPS (4 vCPU, 8GB RAM, Debian 13), 2GB swap |

-## Quick start
+~5,300 lines of Python + TypeScript. 67 commits since July 2026.

-```bash
-# Install dependencies
-pip install -r requirements.txt
+---

-# Set your Hyperliquid testnet key
-export HYPERLIQUID_TESTNET_PK=0x...
-
-# Run live (testnet only)
-python live/node.py
-```
-
-## Project layout
+## Repository Structure

 ```
 ftdt-quant-lab/
-├── config/          # Per-strategy YAML configuration
-├── strategies/      # Strategy implementations
-├── common/          # Risk manager, portfolio tracker, metrics
-├── backtests/       # Historical backtest runners
-├── live/            # Live trading node (Hyperliquid Testnet)
-├── docs/            # Documentation and strategy writeups
-└── notebooks/       # Analysis notebooks
+├── live/
+│   ├── node.py                  # Live trading node — testnet, 9 strategies
+│   └── paper_trader.py          # Paper trading — mainnet data, 10 strategies
+├── strategies/
+│   ├── orderbook_imbalance.py   # L2 bid/ask volume skew (OBI)
+│   ├── iceberg_detection.py     # Whale TWAP accumulation detection
+│   ├── funding_arb.py           # Delta-neutral carry — spot/perp funding
+│   ├── pairs_trading.py         # BTC/ETH ratio Z-score (1.5σ)
+│   ├── avellaneda_stoikov.py    # Dual-sided stochastic control MM
+│   ├── kalman_pairs/            # Kalman-filter adaptive hedge ratio
+│   ├── hawkes_ofi.py            # Hawkes process order flow
+│   ├── deep_lob.py              # Deep LOB CNN feature extraction
+│   ├── queue_imbalance.py       # Weighted queue dynamics
+│   ├── hurst_vpin.py            # Hurst exponent + VPIN directional
+│   ├── hurst_vpin_live.py       # Lightweight Hurst/VPIN for live tick stream
+│   └── quant_report.py          # QF-Lib style quant analytics
+├── dashboard/
+│   ├── server.py                # FastAPI backend — WS, REST, static files
+│   └── next/
+│       └── src/
+│           ├── app/             # Main page + layout
+│           ├── components/      # QuantReport, StrategyCard, L2Terminal
+│           └── lib/             # Types, API client
+├── backtests/
+│   ├── run.py                   # Backtest runner
+│   └── results/
+│       └── historical/          # JSON backtest snapshots (32 entries)
+├── common/                      # Shared utilities
+│   ├── risk.py, risk_manager.py
+│   ├── hyperliquid_api.py
+│   └── portfolio.py, metrics.py
+├── config/
+│   └── fee_tiers.py             # Perp/spot fee schedules
+└── infrastructure/
+    ├── Caddyfile                # Reverse proxy config
+    └── systemd/                 # Service units (pending)
 ```

-## Strategy details
+---

-See `docs/STRATEGIES.md` for a walkthrough of each strategy.
+## Strategies — Current State

-## Risk warning
+### Live Node (Hyperliquid Testnet — 9 strategies, $100 each)

-This is **testnet only**. These strategies are educational — they
-are not financial advice and have no alpha guarantee. Never run
-them on mainnet without thorough backtesting and your own due diligence.
+| # | Strategy | Type | Asset | Size | PnL | Trades | Win |
+|---|----------|------|-------|------|-----|--------|-----|
+| 1 | Order Book Imbalance | reversal | BTC | 0.000200 | $0.00 | 0 | — |
+| 2 | Iceberg Detection | momentum | BTC | 0.000210 | $0.00 | 2 | 0% |
+| 3 | Funding Rate Arb | carry | BTC | 0.000220 | $0.00 | 0 | — |
+| 4 | Pairs Trading | stat_arb | ETH | 0.006000 | **+$0.74** | 9 | 67% |
+| 5 | Avellaneda-Stoikov | market_making | BTC | 0.000230 | -$1.35 | 32 | 0% |
+| 6 | Momentum Breakout | momentum | ETH | 0.000500 | $0.00 | 0 | — |
+| 7 | Mean Reversion | reversal | ETH | 0.000500 | $0.00 | 0 | — |
+| 8 | Kalman Pairs | stat_arb | ETH | 0.005000 | $0.00 | 0 | — |
+| 9 | Hurst VPIN | momentum | BTC | 0.000240 | $0.00 | 0 | — |
+
+**Execution:** GTC POST-ONLY limit orders. Signals every 5 ticks (5s), dual-sided for A-S.  
+**Fee model:** Maker 0.02% (testnet).
+
+### Paper Trader (Hyperliquid Mainnet data — 10 strategies, $100 each)
+
+Same set + Queue Imbalance. Real mainnet orderbook + funding data. Fee model: taker 0.05% / maker 0.02%. Trades simulated with 1bps slippage.

 ---
-Built by [Ramses Echikh](https://git.ftdt.io/rams) · Part of my quant trading portfolio
+
+## Historical Backtests
+
+32 backtest snapshots across 8 strategies × 4 coins (BTC, ETH, HYPE, VVV).  
+Hurst/VPIN BTC: **46 trades, 96% win rate, +1.10%** on synthetic trending data.
+
+---
+
+## Priority Analysis
+
+### Strategies showing real signal
+
+| Strategy | Signal | Status |
+|----------|--------|--------|
+| **Pairs Trading** | ✅ | +$0.74, 67% win rate — only profitable live strategy |
+| **Avellaneda-Stoikov** | ⚠️ | 32 trades but losing — spread capture not covering fees |
+| **Iceberg Detection** | ⚠️ | 2 trades — rare signals, needs threshold tuning |
+| **Hurst VPIN** | 🔬 | 96% win in backtest, 0 live trades — very selective |
+| **Mean Reversion** | ⏳ | 0 trades — VWAP deviation not crossing 1.0σ |
+| **Momentum** | ⏳ | 0 trades — Bollinger 1.2σ too tight for ETH |
+
+### Recommendation: focus investment here
+
+1. **Pairs Trading** — `#1 priority`. Only live winner. Extend to more pairs (SOL, ARB, OP). Add Kalman dynamic hedge ratio. This is the clearest path to sustained PnL.
+
+2. **Hurst/VPIN** — `#2 priority`. Backtest shows strong edge (96% win). Needs real market data (not synthetic) and 3-day candle feed to trigger more signals. The selectivity IS the edge — don't dilute it.
+
+3. **Avellaneda-Stoikov** — Needs inventory control. 32 trades losing because adverse selection. Add skew-aware quoting (update reserve price based on queue imbalance).
+
+4. **Iceberg Detection** — Lower detection threshold. Currently requires 7/10 consecutive ticks same direction — too strict.
+
+5. **Funding Rate Arb** — Real Hyperliquid funding data already plumbed. Test threshold from 3% → 1% APR. Prefunding detection (predict next rate before announcement).
+
+6. **Backtest engine** — Replace synthetic data with real Hyperliquid candles. Add walk-forward optimization. The `hurst_vpin.py` infrastructure is ready.
+
+### Skip for now
+
+- OBI / Mean Reversion / Momentum — 0 trades. Signal thresholds need fundamental redesign, not just tuning.
+- Cartea-Jaimungal / Gueant MM — academic models, not adapted to crypto microstructure.
+- DeepLOB / Hawkes OFI — dependency-heavy, no live integration.
+
+---
+
+## Next Steps
+
+```bash
+# Clone and deploy
+git clone https://git.ftdt.io/rams/ftdt-quant-lab.git
+cd ftdt-quant-lab
+python3 -m venv .venv && source .venv/bin/activate
+pip install -r requirements.txt        # (pending — currently manual)
+
+# Start services
+python live/node.py &                  # Trading node
+python live/paper_trader.py &          # Paper simulator
+python dashboard/server.py --port 9175  # Dashboard backend
+```
+
+---
+
+## Roadmap
+
+- [ ] Docker Compose for reproducible deployment
+- [ ] Walk-forward backtest on real Hyperliquid candle data
+- [ ] Extend Pairs Trading to BTC/SOL, BTC/ARB
+- [ ] Hurst/VPIN 3-day candle feed → real live signals
+- [ ] Memory leak proofing — current guard at 512MB RSS
+- [ ] systemd service unit files for auto-restart
+- [ ] Grafana + Prometheus monitoring dashboard
+
+---
+
+*Built with Hermes Agent · Hallmark Cobalt · Ubuntu fonts*
@@ -42,6 +42,7 @@ STRATEGIES = {
    "avellaneda":{"name": "Avellaneda-Stoikov",   "size": 0.001, "fee_model": "maker"},
    "momentum":  {"name": "Momentum Breakout",    "size": 0.002, "fee_model": "taker"},
    "mean_rev":  {"name": "Mean Reversion",       "size": 0.002, "fee_model": "taker"},
+    "kalman_pairs": {"name": "Kalman Pairs",       "size": 0.005, "fee_model": "taker"},
 }


@@ -81,6 +82,7 @@ def fetch_candles(coin: str, interval: str = "1h", limit: int = 720) -> list[dic
 def simulate_strategy_on_candles(
    key: str,
    candles: list[dict],
+    coin_name: str = "BTC",
    allocation: float = 100.0,
    fee_tier: int = 0,
    staking_tier: str = "none",
@@ -143,9 +145,14 @@ def simulate_strategy_on_candles(
                reason = f"Iceberg: {up_count}/10 upward ticks"
                signal_strength = 1 - up_count / 10
        
-        elif key == "funding_arb":
-            # Funding rate arb: need real funding data — skip for candle-only backtest
-            pass
+        elif key == "funding_arb" and len(prices_20) >= 20:
+            # Funding Rate Arb: hourly price trend as funding proxy
+            long_return = (close - prices_20[0]) / prices_20[0]
+            annual_rate = long_return * 365 * 24  # hourly to annual
+            if abs(annual_rate) > 0.03:  # >3% annualized
+                signal = "SELL" if annual_rate > 0 else "BUY"
+                reason = f"Fund: {annual_rate*100:.1f}% APR ({long_return*100:.2f}% 1h)"
+                signal_strength = min(1.0, abs(annual_rate) * 5)
        
        elif key == "pairs" and len(prices_20) >= 20:
            # Pairs: BTC/ETH ratio Z-score (only works if we have both)
@@ -205,6 +212,23 @@ def simulate_strategy_on_candles(
                signal = "BUY"
                reason = f"VWAP: dev={dev:.1f}σ below VWAP ${vwap:.0f}"
                signal_strength = abs(dev)
+        elif key == "kalman_pairs" and len(prices_20) >= 20:
+            # Kalman filter reversion: adaptively tracks price vs SMA
+            if "_kalman_trader" not in dir():
+                import sys as _sys
+                _sys.path.insert(0, ".")
+                from strategies.kalman_pairs import KalmanPairsTrader
+                globals()["_kalman_trader"] = KalmanPairsTrader(
+                    transition_covariance=1e-3, observation_covariance=1e-1,
+                    z_entry=2.0, z_exit=0.5, warmup_bars=20,
+                )
+            # Use 20-period SMA as the "pair" asset X, price as Y
+            sma_20 = sum(prices_20) / len(prices_20)
+            result = globals()["_kalman_trader"].step(sma_20, close)
+            if result["signal"] != 0:
+                signal = "BUY" if result["signal"] > 0 else "SELL"
+                reason = f"K-pairs z={result['z_score']:.2f} b={result['beta']:.3f}"
+                signal_strength = abs(result["z_score"]) / 4.0
        
        # ── Execute signal ──
        if signal and signal_strength > 0.15:  # minimum strength filter
@@ -289,7 +313,7 @@ def simulate_strategy_on_candles(
    return {
        "strategy": name,
        "strategy_key": key,
-        "coin": candles[0]["t"] if candles else "unknown",
+        "coin": coin_name,  # actual ticker (BTC, ETH, etc.)
        "allocation": allocation,
        "start_time": curve[0]["t"] if curve else "",
        "end_time": curve[-1]["t"] if curve else "",
@@ -319,7 +343,7 @@ def simulate_strategy_on_candles(

 def main():
    p = argparse.ArgumentParser(description="FTDT Historical Backtest Runner")
-    p.add_argument("--coin", default="BTC", choices=["BTC", "ETH", "SOL"], help="Coin to backtest")
+    p.add_argument("--coin", default="BTC", choices=["BTC", "ETH", "SOL", "HYPE", "VVV"], help="Coin to backtest")
    p.add_argument("--strategy", "-s", choices=list(STRATEGIES) + ["all"], default="all")
    p.add_argument("--fee-tier", type=int, default=0, choices=range(7))
    p.add_argument("--staking-tier", default="none", choices=list(STAKING_TIERS.keys()))
@@ -354,8 +378,72 @@ def main():
        cfg = STRATEGIES[key]
        print(f"\n  Running: {cfg['name']} on {a.coin}...")
        
+        # Kalman Pairs: use real BTC/ETH pair data
+        if key == "kalman_pairs":
+            try:
+                sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+                from strategies.kalman_pairs import KalmanPairsTrader, backtest_kalman_pairs
+                
+                # Fetch ETH candles
+                if a.coin != "ETH":
+                    eth_candles = fetch_candles("ETH", interval="1h", limit=a.hours)
+                else:
+                    eth_candles = fetch_candles("BTC", interval="1h", limit=a.hours)
+                
+                if eth_candles:
+                    X = [float(c["c"]) for c in eth_candles]
+                    Y = [float(c["c"]) for c in candles]
+                    n = min(len(X), len(Y))
+                    X, Y = X[:n], Y[:n]
+                    
+                    trader = KalmanPairsTrader(
+                        transition_covariance=1e-4, observation_covariance=1e-2,
+                        z_entry=2.0, z_exit=0.5, warmup_bars=20,
+                    )
+                    bt = backtest_kalman_pairs(
+                        X, Y, trader,
+                        trade_size_usd=50.0,
+                        transaction_cost_bps=2.5,
+                    )
+                    
+                    # Convert to standard format expected by the dashboard
+                    result = {
+                        "strategy": cfg["name"],
+                        "strategy_key": key,
+                        "coin": a.coin,
+                        "allocation": 100.0,
+                        "start_time": str(bt["equity_curve"][0]["t"]) if bt["equity_curve"] else "",
+                        "end_time": str(bt["equity_curve"][-1]["t"]) if bt["equity_curve"] else "",
+                        "start_equity": 100.0,
+                        "end_equity": round(bt["final_equity"], 4),
+                        "pnl": round(bt["total_pnl"], 4),
+                        "pnl_pct": round(bt["pnl_pct"], 2),
+                        "pnl_gross": round(bt["total_pnl"] + bt["transaction_costs"], 4),
+                        "pnl_gross_pct": round(bt["pnl_pct"], 2),
+                        "fees_total": round(bt["transaction_costs"], 4),
+                        "fee_tier": a.fee_tier,
+                        "staking_tier": a.staking_tier,
+                        "fee_model": cfg["fee_model"],
+                        "sharpe": round(bt["sharpe"], 4),
+                        "sortino": round(bt["sortino"], 4),
+                        "max_dd": round(bt["max_drawdown"], 4),
+                        "max_dd_pct": round(bt["max_drawdown"] * 100, 2),
+                        "win_rate": round(bt["win_rate"], 4),
+                        "total_trades": bt["total_trades"],
+                        "equity_curve": [{"t": e["t"], "v": e["equity"]} for e in bt["equity_curve"]],
+                        "trades": bt["trades"][-100:],
+                        "num_periods": n,
+                        "data_source": "Hyperliquid Mainnet (BTC/ETH pair)",
+                        "generated_at": datetime.now().isoformat(),
+                    }
+                else:
+                    result = {}  # Skip
+            except Exception as e:
+                print(f"    Kalman pairs error: {e}")
+                result = {}
+        else:
            result = simulate_strategy_on_candles(
-            key, candles,
+                key, candles, a.coin,
                fee_tier=a.fee_tier,
                staking_tier=a.staking_tier,
            )
@@ -0,0 +1,249 @@
+"""
+NautilusTrader event-driven backtest engine for Hyperliquid strategies.
+
+Sets up a BacktestEngine with Hyperliquid venue, instruments, historical
+bar data, and registered strategies. Runs event-driven simulation with
+realistic fill emulation (maker/taker, slippage).
+
+Slower but more realistic than VectorBT — intended for final validation
+before paper/live deployment.
+"""
+from __future__ import annotations
+
+import logging
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from nautilus_trader.backtest.engine import BacktestEngine, BacktestEngineConfig
+from nautilus_trader.model.data import Bar, BarSpecification, BarType
+from nautilus_trader.model.enums import AccountType, BarAggregation, OmsType, PriceType
+from nautilus_trader.model.identifiers import InstrumentId, Venue
+from nautilus_trader.model.instruments import CryptoPerpetual
+from nautilus_trader.model.objects import Currency, Money, Price, Quantity
+
+from framework.data import HyperliquidDataProvider, INTERVAL_TO_SECONDS
+from framework.instruments import HL_VENUE
+
+logger = logging.getLogger(__name__)
+
+
+INTERVAL_TO_AGG = {
+    "1m": (1, BarAggregation.MINUTE),
+    "5m": (5, BarAggregation.MINUTE),
+    "15m": (15, BarAggregation.MINUTE),
+    "30m": (30, BarAggregation.MINUTE),
+    "1h": (1, BarAggregation.HOUR),
+    "4h": (4, BarAggregation.HOUR),
+    "8h": (8, BarAggregation.HOUR),
+    "1d": (1, BarAggregation.DAY),
+}
+
+
+class NTBacktestRunner:
+    """NautilusTrader backtest engine wrapper for Hyperliquid."""
+
+    def __init__(self):
+        pass
+
+    def run_backtest(
+        self,
+        strategy: str = "pairs",
+        interval: str = "1h",
+        instruments: dict[str, CryptoPerpetual] | None = None,
+        testnet: bool = False,
+        limit: int = 5000,
+    ) -> dict[str, Any] | None:
+        """Run event-driven backtest with NautilusTrader.
+
+        1. Set up BacktestEngine
+        2. Register Hyperliquid venue + instruments
+        3. Load historical bars from Hyperliquid
+        4. Add strategy and run
+        5. Return metrics
+        """
+
+        step, agg = INTERVAL_TO_AGG.get(interval, (1, BarAggregation.HOUR))
+
+        config = BacktestEngineConfig()
+        engine = BacktestEngine(config=config)
+        engine.add_venue(
+            venue=HL_VENUE,
+            oms_type=OmsType.NETTING,
+            account_type=AccountType.MARGIN,
+            starting_balances=[Money(10_000.0, Currency.from_str("USD"))],
+        )
+
+        # Add instruments
+        coin = self._get_coin(strategy)
+        inst_for_coin = None
+        if instruments:
+            for name, inst in instruments.items():
+                engine.add_instrument(inst)
+                if name.upper() == coin.upper():
+                    inst_for_coin = inst
+
+        if not inst_for_coin and instruments:
+            # Try to find any instrument matching
+            for inst in instruments.values():
+                instr_name = str(inst.id.symbol)
+                if coin.upper() in instr_name.upper():
+                    inst_for_coin = inst
+                    break
+
+        sz_prec = inst_for_coin.size_precision if inst_for_coin else 5
+
+        # Fetch real candles
+        provider = HyperliquidDataProvider(testnet=testnet)
+        df = provider.fetch_candles(coin, interval=interval, limit=limit)
+        if df.empty:
+            logger.error("No candles for %s", coin)
+            return None
+
+        # Build bars
+        inst_id = InstrumentId.from_str(f"{coin.upper()}-USD-PERP.HYPERLIQUID")
+        bars = self._df_to_bars(df, inst_id, step, agg, size_precision=sz_prec)
+
+        # Add bars
+        engine.add_data(bars)
+
+        # Add strategy
+        strategy_class = self._resolve_strategy_class(strategy)
+        if strategy_class is None:
+            logger.error("No NT strategy class for %s", strategy)
+            return None
+
+        from framework.config import StrategyConfig
+        cfg = StrategyConfig(
+            name=strategy,
+            instrument=f"{coin}-USD-PERP",
+            asset=coin,
+            allocation=10000.0,
+            order_size=0.001,
+        )
+        nt_strategy = strategy_class(cfg)
+        engine.add_strategy(nt_strategy)
+
+        # Run
+        try:
+            result = engine.run()
+        except Exception as e:
+            logger.error("Backtest engine error: %s", e)
+            import traceback
+            traceback.print_exc()
+            return None
+
+        # Extract metrics
+        return self._extract_result(result, engine, strategy, interval, len(bars))
+
+    # ── Helpers ─────────────────────────────────────────────────
+
+    def _get_coin(self, strategy: str) -> str:
+        return {"pairs": "ETH", "hurst_vpin": "BTC", "as_mm": "BTC",
+                "obi": "BTC", "funding_arb": "BTC"}.get(strategy, "BTC")
+
+    def _df_to_bars(
+        self,
+        df: pd.DataFrame,
+        instrument_id: InstrumentId,
+        step: int,
+        aggregation: BarAggregation,
+        size_precision: int = 5,
+    ) -> list[Bar]:
+        spec = BarSpecification(step, aggregation, PriceType.LAST)
+        bar_type = BarType(instrument_id, spec)
+        bars = []
+        for idx, row in df.iterrows():
+            ts = int(idx.timestamp() * 1e9)
+            bar = Bar(
+                bar_type=bar_type,
+                open=Price.from_str(str(row["open"])),
+                high=Price.from_str(str(row["high"])),
+                low=Price.from_str(str(row["low"])),
+                close=Price.from_str(str(row["close"])),
+                volume=Quantity.from_str(f'{row["volume"]:.{size_precision}f}'),
+                ts_event=ts,
+                ts_init=ts,
+            )
+            bars.append(bar)
+        return bars
+
+    def _resolve_strategy_class(self, strategy: str):
+        import importlib
+        registry = {
+            "pairs": "strategies.nt.pairs_trading_nt.PairsTradingNT",
+            "hurst_vpin": "strategies.nt.hurst_vpin_nt.HurstVPINNT",
+            "as_mm": "strategies.nt.as_mm_nt.ASMarketMakingNT",
+        }
+        path = registry.get(strategy)
+        if not path:
+            return None
+        module_path, class_name = path.rsplit(".", 1)
+        mod = importlib.import_module(module_path)
+        return getattr(mod, class_name)
+
+    def _extract_result(
+        self,
+        result,
+        engine,
+        strategy: str,
+        interval: str,
+        n_bars: int,
+    ) -> dict:
+        try:
+            pnl = float(sum(
+                a.pnl() for a in result.accounts if hasattr(a, 'pnl')
+            )) if hasattr(result, 'accounts') else 0.0
+        except Exception:
+            pnl = 0.0
+
+        try:
+            equity = result.equity_curve if hasattr(result, 'equity_curve') else None
+        except Exception:
+            equity = None
+
+        equity_vals = []
+        if equity is not None and hasattr(equity, '__iter__'):
+            equity_vals = [float(v) for v in equity] if equity is not None else []
+
+        total_return = (equity_vals[-1] / 10000.0 - 1) * 100 if equity_vals else 0.0
+
+        return {
+            "strategy": strategy,
+            "engine": "nautilus_trader",
+            "interval": interval,
+            "n_bars": n_bars,
+            "start_equity": 10000.0,
+            "end_equity": equity_vals[-1] if equity_vals else 10000.0,
+            "total_return_pct": round(total_return, 2),
+            "pnl": round(pnl, 2),
+            "sharpe": self._compute_sharpe(equity_vals),
+            "max_drawdown_pct": round(self._compute_max_dd(equity_vals) * 100, 2),
+            "generated_at": datetime.now(timezone.utc).isoformat(),
+        }
+
+    def _compute_sharpe(self, equity: list[float]) -> float:
+        if len(equity) < 2:
+            return 0.0
+        returns = [(equity[i] - equity[i - 1]) / equity[i - 1] for i in range(1, len(equity))]
+        mean_ret = np.mean(returns) if returns else 0.0
+        std_ret = np.std(returns, ddof=1) if returns else 0.0
+        return (mean_ret / std_ret) * np.sqrt(365 * 24) if std_ret > 0 else 0.0
+
+    def _compute_max_dd(self, equity: list[float]) -> float:
+        if not equity:
+            return 0.0
+        peak = equity[0]
+        worst = 0.0
+        for v in equity:
+            if v > peak:
+                peak = v
+            dd = (peak - v) / peak if peak > 0 else 0.0
+            worst = max(worst, dd)
+        return worst
@@ -1,10 +1,10 @@
 {
-  "strategy": "Funding Rate Arb",
-  "strategy_key": "funding_arb",
-  "coin": 1783234800000,
+  "strategy": "Avellaneda-Stoikov",
+  "strategy_key": "avellaneda",
+  "coin": "HYPE",
  "allocation": 100.0,
-  "start_time": "2026-07-05T07:00:00",
-  "end_time": "2026-08-04T07:00:00",
+  "start_time": "2026-07-06T05:00:00",
+  "end_time": "2026-08-05T05:00:00",
  "start_equity": 100.0,
  "end_equity": 100.0,
  "pnl": 0.0,
@@ -14,7 +14,7 @@
  "fees_total": 0.0,
  "fee_tier": 0,
  "staking_tier": "none",
-  "fee_model": "taker",
+  "fee_model": "maker",
  "sharpe": 0.0,
  "sortino": 0.0,
  "max_dd": 0.0,
@@ -22,94 +22,6 @@
  "win_rate": 0.0,
  "total_trades": 0,
  "equity_curve": [
-    {
-      "t": "2026-07-05T07:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T08:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T09:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T10:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T11:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T12:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T13:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T14:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T15:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T16:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T17:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T18:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T19:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T20:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T21:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T22:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T23:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T00:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T01:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T02:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T03:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T04:00:00",
-      "v": 100.0
-    },
    {
      "t": "2026-07-06T05:00:00",
      "v": 100.0
@@ -2905,10 +2817,98 @@
    {
      "t": "2026-08-04T07:00:00",
      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T08:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T09:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T10:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T11:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T12:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T13:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T14:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T15:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T16:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T17:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T18:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T19:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T20:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T21:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T22:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T23:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T00:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T01:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T02:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T03:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T04:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T05:00:00",
+      "v": 100.0
    }
  ],
  "trades": [],
  "num_periods": 721,
  "data_source": "Hyperliquid Mainnet",
-  "generated_at": "2026-08-04T07:28:17.886718"
+  "generated_at": "2026-08-05T05:07:41.026245"
 }
@@ -1,10 +1,10 @@
 {
-  "strategy": "Funding Rate Arb",
-  "strategy_key": "funding_arb",
-  "coin": 1783234800000,
+  "strategy": "Avellaneda-Stoikov",
+  "strategy_key": "avellaneda",
+  "coin": "VVV",
  "allocation": 100.0,
-  "start_time": "2026-07-05T07:00:00",
-  "end_time": "2026-08-04T07:00:00",
+  "start_time": "2026-07-06T05:00:00",
+  "end_time": "2026-08-05T05:00:00",
  "start_equity": 100.0,
  "end_equity": 100.0,
  "pnl": 0.0,
@@ -14,7 +14,7 @@
  "fees_total": 0.0,
  "fee_tier": 0,
  "staking_tier": "none",
-  "fee_model": "taker",
+  "fee_model": "maker",
  "sharpe": 0.0,
  "sortino": 0.0,
  "max_dd": 0.0,
@@ -22,94 +22,6 @@
  "win_rate": 0.0,
  "total_trades": 0,
  "equity_curve": [
-    {
-      "t": "2026-07-05T07:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T08:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T09:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T10:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T11:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T12:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T13:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T14:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T15:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T16:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T17:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T18:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T19:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T20:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T21:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T22:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-05T23:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T00:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T01:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T02:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T03:00:00",
-      "v": 100.0
-    },
-    {
-      "t": "2026-07-06T04:00:00",
-      "v": 100.0
-    },
    {
      "t": "2026-07-06T05:00:00",
      "v": 100.0
@@ -2905,10 +2817,98 @@
    {
      "t": "2026-08-04T07:00:00",
      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T08:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T09:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T10:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T11:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T12:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T13:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T14:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T15:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T16:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T17:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T18:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T19:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T20:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T21:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T22:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-04T23:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T00:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T01:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T02:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T03:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T04:00:00",
+      "v": 100.0
+    },
+    {
+      "t": "2026-08-05T05:00:00",
+      "v": 100.0
    }
  ],
  "trades": [],
  "num_periods": 721,
  "data_source": "Hyperliquid Mainnet",
-  "generated_at": "2026-08-04T07:29:03.636645"
+  "generated_at": "2026-08-05T05:07:41.798429"
 }
@@ -0,0 +1 @@
+{"strategy": "Hurst VPIN", "strategy_key": "hurst_vpin", "coin": "ETH", "allocation": 99.9999907962162, "start_time": "2026-08-06T07:09:06.405305", "end_time": "2026-08-06T07:09:06.405333", "start_equity": 100.0, "end_equity": 99.9374907962162, "pnl": -0.06, "pnl_pct": -0.06, "sharpe": 0.35, "sortino": 0.64, "max_dd": 0.0003, "win_rate": 0.0, "total_trades": 1, "trades": [{"time": "2026-08-06T07:03:31.457823", "side": "SELL", "entry_price": 1868.6, "size": 0.00024, "hurst": 0.5751, "vpin": 0.7986, "bar_count": 236, "exit_price": 1856.9195301809586, "pnl": -0.0625}], "equity_curve": [{"t": 0, "v": 100.0}, {"t": 1000, "v": 99.9374907962162}], "signals_generated": 15385, "data_source": "hyperliquid_mainnet"}
@@ -0,0 +1 @@
+{"strategy": "Hurst VPIN", "strategy_key": "hurst_vpin", "coin": "HYPE", "allocation": 99.999993931191, "start_time": "2026-08-06T07:14:02.976396", "end_time": "2026-08-06T07:14:02.976412", "start_equity": 100.0, "end_equity": 100.092893931191, "pnl": 0.09, "pnl_pct": 0.09, "sharpe": 0.21, "sortino": 0.61, "max_dd": 0, "win_rate": 1.0, "total_trades": 1, "trades": [{"time": "2026-08-06T07:09:16.367204", "side": "SELL", "entry_price": 52.65, "size": 0.00024, "hurst": 0.568, "vpin": 0.853, "bar_count": 532, "exit_price": 53.13908654772063, "pnl": 0.0929}], "equity_curve": [{"t": 0, "v": 100.0}, {"t": 1000, "v": 100.092893931191}], "signals_generated": 13450, "data_source": "hyperliquid_mainnet"}
@@ -0,0 +1 @@
+{"strategy": "Hurst VPIN", "strategy_key": "hurst_vpin", "coin": "VVV", "allocation": 99.99999936064138, "start_time": "2026-08-06T07:14:11.162510", "end_time": "2026-08-06T07:14:11.162521", "start_equity": 100.0, "end_equity": 99.93239936064138, "pnl": -0.07, "pnl_pct": -0.07, "sharpe": 0.43, "sortino": 0.52, "max_dd": 0.0003, "win_rate": 0.0, "total_trades": 1, "trades": [{"time": "2026-08-06T07:14:03.784899", "side": "SELL", "entry_price": 12.625, "size": 0.00024, "hurst": 0.5848, "vpin": 0.4823, "bar_count": 149, "exit_price": 12.539654192809744, "pnl": -0.0676}], "equity_curve": [{"t": 0, "v": 100.0}, {"t": 1000, "v": 99.93239936064138}], "signals_generated": 485, "data_source": "hyperliquid_mainnet"}
@@ -0,0 +1 @@
+{"strategy": "SPX Mean Reversion", "strategy_key": "spx_mean_reversion", "coin": "SPX", "allocation": 100.0, "start_time": "2026-07-30T07:30:00", "end_time": "2026-08-06T07:30:00", "start_equity": 100.0, "end_equity": 100.14, "pnl": 0.14, "pnl_pct": 0.14, "sharpe": 0.63, "sortino": 1.0, "max_dd": 0.01, "win_rate": 0.7727, "total_trades": 22, "trades": [{"time": "2026-08-06T07:34:45.055488", "side": "BUY", "entry_price": 0.33078, "z_entry": -1.58, "size": 0.01, "exit_price": 0.33383, "pnl": 0.0092, "exit_z": -0.28}, {"time": "2026-08-06T07:34:45.055850", "side": "SELL", "entry_price": 0.33624, "z_entry": 2.24, "size": 0.01, "exit_price": 0.32967, "pnl": 0.0195, "exit_z": -1.78}, {"time": "2026-08-06T07:34:45.055974", "side": "BUY", "entry_price": 0.32918, "z_entry": -1.83, "size": 0.01, "exit_price": 0.32434, "pnl": -0.0147, "exit_z": -0.22}, {"time": "2026-08-06T07:34:45.056641", "side": "BUY", "entry_price": 0.32171, "z_entry": -1.5, "size": 0.01, "exit_price": 0.32165, "pnl": -0.0002, "exit_z": 0.18}, {"time": "2026-08-06T07:34:45.057239", "side": "SELL", "entry_price": 0.32372, "z_entry": 1.84, "size": 0.01, "exit_price": 0.3218, "pnl": 0.0059, "exit_z": 0.19}, {"time": "2026-08-06T07:34:45.057798", "side": "SELL", "entry_price": 0.32404, "z_entry": 2.08, "size": 0.01, "exit_price": 0.32262, "pnl": 0.0044, "exit_z": -0.06}, {"time": "2026-08-06T07:34:45.058058", "side": "SELL", "entry_price": 0.32373, "z_entry": 1.62, "size": 0.01, "exit_price": 0.32046, "pnl": 0.0101, "exit_z": -3.19}, {"time": "2026-08-06T07:34:45.058229", "side": "BUY", "entry_price": 0.31678, "z_entry": -8.2, "size": 0.01, "exit_price": 0.31904, "pnl": 0.0071, "exit_z": -0.06}, {"time": "2026-08-06T07:34:45.058593", "side": "SELL", "entry_price": 0.32421, "z_entry": 2.0, "size": 0.01, "exit_price": 0.32154, "pnl": 0.0082, "exit_z": 0.09}, {"time": "2026-08-06T07:34:45.059004", "side": "BUY", "entry_price": 0.31961, "z_entry": -1.91, "size": 0.01, "exit_price": 0.32139, "pnl": 0.0056, "exit_z": -0.24}, {"time": "2026-08-06T07:34:45.059289", "side": "SELL", "entry_price": 0.32407, "z_entry": 2.02, "size": 0.01, "exit_price": 0.32727, "pnl": -0.0099, "exit_z": 0.04}, {"time": "2026-08-06T07:34:45.059931", "side": "BUY", "entry_price": 0.32388, "z_entry": -1.66, "size": 0.01, "exit_price": 0.32533, "pnl": 0.0045, "exit_z": -0.16}, {"time": "2026-08-06T07:34:45.060187", "side": "SELL", "entry_price": 0.33477, "z_entry": 4.22, "size": 0.01, "exit_price": 0.3297, "pnl": 0.0152, "exit_z": -0.11}, {"time": "2026-08-06T07:34:45.060590", "side": "BUY", "entry_price": 0.32663, "z_entry": -1.55, "size": 0.01, "exit_price": 0.32146, "pnl": -0.0158, "exit_z": 0.27}, {"time": "2026-08-06T07:34:45.061181", "side": "SELL", "entry_price": 0.32413, "z_entry": 2.06, "size": 0.01, "exit_price": 0.32194, "pnl": 0.0068, "exit_z": -0.01}, {"time": "2026-08-06T07:34:45.061481", "side": "SELL", "entry_price": 0.32694, "z_entry": 2.25, "size": 0.01, "exit_price": 0.32449, "pnl": 0.0075, "exit_z": 0.2}, {"time": "2026-08-06T07:34:45.061750", "side": "BUY", "entry_price": 0.32057, "z_entry": -3.02, "size": 0.01, "exit_price": 0.32404, "pnl": 0.0108, "exit_z": -0.18}, {"time": "2026-08-06T07:34:45.061851", "side": "SELL", "entry_price": 0.32752, "z_entry": 2.03, "size": 0.01, "exit_price": 0.32466, "pnl": 0.0087, "exit_z": 0.26}, {"time": "2026-08-06T07:34:45.061952", "side": "SELL", "entry_price": 0.32729, "z_entry": 1.58, "size": 0.01, "exit_price": 0.32752, "pnl": -0.0007, "exit_z": 0.28}, {"time": "2026-08-06T07:34:45.062542", "side": "BUY", "entry_price": 0.32456, "z_entry": -1.89, "size": 0.01, "exit_price": 0.3315, "pnl": 0.0214, "exit_z": 2.35}, {"time": "2026-08-06T07:34:45.062693", "side": "SELL", "entry_price": 0.33111, "z_entry": 2.07, "size": 0.01, "exit_price": 0.3259, "pnl": 0.0158, "exit_z": -2.79}, {"time": "2026-08-06T07:34:45.063074", "side": "BUY", "entry_price": 0.32525, "z_entry": -2.84, "size": 0.01, "exit_price": 0.33141, "pnl": 0.019, "exit_z": -0.06}], "data_source": "TradeXYZ / Hyperliquid SPX"}
@@ -0,0 +1,335 @@
+"""
+VectorBT backtest runner — fast vectorized backtesting on Hyperliquid candle data.
+
+Fetches real candles from Hyperliquid, converts to signals, and runs
+through VectorBT's Portfolio simulator for instant results.
+
+Supports parameter sweeps, walk-forward optimization, and full metrics.
+"""
+from __future__ import annotations
+
+import json
+import logging
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import vectorbt as vbt
+
+sys_path = str(Path(__file__).resolve().parent.parent)
+if sys_path not in __import__("sys").path:
+    __import__("sys").path.insert(0, sys_path)
+
+from framework.data import HyperliquidDataProvider, INTERVAL_MAP
+
+logger = logging.getLogger(__name__)
+
+RESULTS_DIR = Path(__file__).resolve().parent / "results"
+RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+
+
+# ═══════════════════════════════════════════════════════════════
+# Strategy signal generators
+# ═══════════════════════════════════════════════════════════════
+
+def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.Series, pd.Series]:
+    """Generate entry/exit signals for a strategy from candle data.
+
+    Returns (entries, exits) as boolean pandas Series.
+    Each strategy uses the primary coin's close prices.
+    """
+    main_coin = {"pairs": "ETH", "hurst_vpin": "BTC", "as_mm": "BTC",
+                 "obi": "BTC", "funding_arb": "BTC", "momentum": "BTC",
+                 "mean_rev": "BTC"}.get(strategy, "BTC")
+    df = data.get(main_coin)
+    if df is None or df.empty:
+        return pd.Series(dtype=bool), pd.Series(dtype=bool)
+
+    close = df["close"]
+    entries = pd.Series(False, index=close.index)
+    exits = pd.Series(False, index=close.index)
+
+    if strategy == "pairs":
+        btc_df = data.get("BTC")
+        if btc_df is not None and not btc_df.empty:
+            ratio = btc_df["close"] / close
+            mu = ratio.rolling(20).mean()
+            std = ratio.rolling(20).std()
+            z = (ratio - mu) / std
+            entries = z < -1.5
+            exits = z.shift(1) >= -0.5
+
+    elif strategy == "hurst_vpin":
+        returns = close.pct_change().dropna()
+        hurst = returns.rolling(64).apply(_hurst_rs_series, raw=False)
+        entries = hurst > 0.55
+        exits = hurst.shift(1) < 0.45
+
+    elif strategy == "as_mm":
+        spread = (df["high"] - df["low"]) / df["close"]
+        vol = close.pct_change().rolling(20).std()
+        favorable = (spread > spread.rolling(100).mean()) & (vol < 0.02)
+        entries = favorable
+        exits = favorable.shift(3)
+
+    elif strategy == "momentum":
+        sma = close.rolling(20).mean()
+        std = close.rolling(20).std()
+        upper = sma + 2 * std
+        lower = sma - 2 * std
+        entries = (close > upper) | (close < lower)
+        exits = (close.shift(1) > sma.shift(1)) & (close < sma)
+
+    elif strategy in ("mean_rev", "obi"):
+        sma = close.rolling(20).mean()
+        std = close.rolling(20).std()
+        entries = (close < sma - 1.0 * std) | (close > sma + 1.0 * std)
+        exits = abs((close - sma) / std) < 0.3
+
+    elif strategy == "funding_arb":
+        entries[:] = False
+        exits[:] = False
+
+    entries.fillna(False, inplace=True)
+    exits.fillna(False, inplace=True)
+    return entries, exits
+
+
+def _hurst_rs_series(returns_series: pd.Series) -> float:
+    """Hurst exponent via R/S on a window of log returns."""
+    rets = returns_series.dropna().values
+    if len(rets) < 32:
+        return 0.5
+    n = len(rets)
+    max_lag = min(n // 2, 64)
+    lags = []
+    rs_vals = []
+    for lag in range(4, max_lag):
+        segs = n // lag
+        if segs < 2:
+            continue
+        vals = []
+        for s in range(segs):
+            seg = rets[s * lag:(s + 1) * lag]
+            mean = np.mean(seg)
+            dev = np.cumsum(seg - mean)
+            r = float(np.max(dev) - np.min(dev))
+            sd = float(np.std(seg, ddof=1))
+            if sd > 1e-12:
+                vals.append(r / sd)
+        if vals:
+            lags.append(np.log(lag))
+            rs_vals.append(np.log(np.mean(vals)))
+    if len(lags) < 4:
+        return 0.5
+    slope = float(np.polyfit(lags, rs_vals, 1)[0])
+    return max(0.2, min(0.8, slope))
+
+
+# ═══════════════════════════════════════════════════════════════
+# VBT Backtest Runner
+# ═══════════════════════════════════════════════════════════════
+
+class VBTBacktestRunner:
+    """VectorBT-powered backtesting on Hyperliquid candle data."""
+
+    def __init__(self, fee_rate: float = 0.0005):
+        self._provider = HyperliquidDataProvider()
+        self._fee_rate = fee_rate
+
+    def run_strategy(
+        self,
+        strategy: str = "pairs",
+        interval: str = "1h",
+        testnet: bool = False,
+        limit: int = 5000,
+    ) -> dict[str, Any] | None:
+        """Fetch candles, generate signals, run VBT backtest, return metrics."""
+        coins = self._get_coins(strategy)
+        provider = HyperliquidDataProvider(testnet=testnet)
+
+        data = {}
+        for coin in coins:
+            try:
+                df = provider.fetch_candles(coin, interval=interval, limit=limit)
+                if not df.empty:
+                    data[coin] = df
+            except Exception as e:
+                logger.warning("Failed to fetch %s: %s", coin, e)
+
+        if not data:
+            logger.error("No candle data fetched for strategy: %s", strategy)
+            return None
+
+        entries, exits = _generate_signals(strategy, data)
+
+        primary = list(data.values())[0]
+        close = primary["close"]
+
+        # Align indices
+        common_idx = entries.index.intersection(close.index)
+        entries = entries.reindex(common_idx).fillna(False)
+        exits = exits.reindex(common_idx).fillna(False)
+        close = close.reindex(common_idx)
+
+        if entries.sum() == 0:
+            logger.warning("No signals generated for %s", strategy)
+            return self._empty_result(strategy, interval)
+
+        try:
+            pf = vbt.Portfolio.from_signals(
+                close=close,
+                entries=entries,
+                exits=exits,
+                fees=self._fee_rate,
+                slippage=0.001,
+                freq=INTERVAL_MAP.get(interval, "1h"),
+                init_cash=10000.0,
+            )
+        except Exception as e:
+            logger.error("VBT portfolio error: %s", e)
+            return self._empty_result(strategy, interval)
+
+        stats = pf.stats()
+        result = self._extract_metrics(pf, stats, strategy, interval, len(close))
+
+        # Save equity curve
+        eq_curve = pf.value().dropna()
+        result["equity_curve"] = [
+            {"t": idx.isoformat(), "v": round(float(v), 2)}
+            for idx, v in eq_curve.to_dict().items()
+        ]
+        result["total_trades"] = int(pf.trades.count())
+        result["generated_at"] = datetime.now(timezone.utc).isoformat()
+
+        return result
+
+    def param_sweep(
+        self,
+        strategy: str = "pairs",
+        param_grid: dict[str, list] | None = None,
+    ) -> pd.DataFrame | None:
+        """Grid search over parameters using VBT."""
+
+        coins = self._get_coins(strategy)
+        data = {}
+        for coin in coins:
+            df = self._provider.fetch_candles(coin, interval="1h", limit=2000)
+            if not df.empty:
+                data[coin] = df
+
+        if not data:
+            return None
+
+        primary = list(data.values())[0]
+        close = primary["close"]
+
+        if param_grid is None:
+            param_grid = {
+                "window": [10, 20, 30, 50],
+                "threshold": [1.0, 1.5, 2.0, 2.5],
+            }
+
+        results_rows = []
+        for window in param_grid.get("window", [20]):
+            for threshold in param_grid.get("threshold", [1.5]):
+                entries, exits = _generate_signals_sweep(strategy, data, window, threshold)
+                try:
+                    pf = vbt.Portfolio.from_signals(
+                        close=close,
+                        entries=entries,
+                        exits=exits,
+                        fees=self._fee_rate,
+                        init_cash=10000.0,
+                    )
+                    stats = pf.stats()
+                    results_rows.append({
+                        "window": window,
+                        "threshold": threshold,
+                        "sharpe": stats.get("Sharpe Ratio", 0),
+                        "total_return": stats.get("Total Return [%]", 0),
+                        "max_drawdown": stats.get("Max Drawdown [%]", 0),
+                        "win_rate": stats.get("Win Rate [%]", 0),
+                        "trades": int(pf.trades.count()),
+                    })
+                except Exception:
+                    pass
+
+        return pd.DataFrame(results_rows) if results_rows else None
+
+    # ── Helpers ─────────────────────────────────────────────────
+
+    def _get_coins(self, strategy: str) -> list[str]:
+        coin_map = {
+            "pairs": ["BTC", "ETH"],
+            "hurst_vpin": ["BTC"],
+            "as_mm": ["BTC"],
+            "obi": ["BTC"],
+            "funding_arb": ["BTC"],
+            "momentum": ["BTC"],
+            "mean_rev": ["BTC"],
+        }
+        return coin_map.get(strategy, ["BTC"])
+
+    def _extract_metrics(self, pf, stats, strategy, interval, n_bars) -> dict:
+        return {
+            "strategy": strategy,
+            "interval": interval,
+            "n_bars": n_bars,
+            "start_equity": 10000.0,
+            "end_equity": round(float(pf.value().iloc[-1]), 2),
+            "total_return_pct": round(float(stats.get("Total Return [%]", 0)), 2),
+            "pnl": round(float(pf.value().iloc[-1]) - 10000, 2),
+            "sharpe": round(float(stats.get("Sharpe Ratio", 0)), 3),
+            "sortino": round(float(stats.get("Sortino Ratio", 0)), 3),
+            "max_drawdown_pct": round(float(stats.get("Max Drawdown [%]", 0)), 2),
+            "win_rate": round(float(stats.get("Win Rate [%]", 0)) / 100, 3),
+            "profit_factor": round(float(stats.get("Profit Factor", 0)), 3),
+            "expectancy": round(float(stats.get("Expectancy", 0)), 3),
+        }
+
+    def _empty_result(self, strategy: str, interval: str) -> dict:
+        return {
+            "strategy": strategy,
+            "interval": interval,
+            "n_bars": 0,
+            "start_equity": 10000.0,
+            "end_equity": 10000.0,
+            "total_return_pct": 0.0,
+            "pnl": 0.0,
+            "sharpe": 0.0,
+            "sortino": 0.0,
+            "max_drawdown_pct": 0.0,
+            "win_rate": 0.0,
+            "total_trades": 0,
+            "generated_at": datetime.now(timezone.utc).isoformat(),
+        }
+
+
+def _generate_signals_sweep(
+    strategy: str,
+    data: dict[str, pd.DataFrame],
+    window: int,
+    threshold: float,
+) -> tuple[pd.Series, pd.Series]:
+    """Variant of signal generator for parameter sweeps with configurable params."""
+    main_coin = {"pairs": "ETH", "hurst_vpin": "BTC", "as_mm": "BTC"}.get(strategy, "BTC")
+    df = data.get(main_coin)
+    if df is None or df.empty:
+        return pd.Series(dtype=bool), pd.Series(dtype=bool)
+
+    close = df["close"]
+    entries = pd.Series(False, index=close.index)
+    exits = pd.Series(False, index=close.index)
+
+    sma = close.rolling(window).mean()
+    std = close.rolling(window).std()
+    entries = (close < sma - threshold * std) | (close > sma + threshold * std)
+    exits = abs((close - sma) / (std + 1e-10)) < 0.3 * threshold
+
+    entries.fillna(False, inplace=True)
+    exits.fillna(False, inplace=True)
+    return entries, exits
@@ -40,5 +40,7 @@ def max_drawdown(equity: list[float]) -> float:
 def win_rate(trades: list[dict]) -> float:
    if not trades:
        return 0.0
-    tp = sum(1 for t in trades if (t.get("pnl_net") or t.get("pnl_gross") or t.get("pnl", 0)) > 0)
+    tp = sum(1 for t in trades if (
+        (t.get("pnl_net") or t.get("net_pnl") or t.get("pnl_gross") or t.get("gross_pnl") or t.get("pnl", 0)) > 0
+    ))
    return tp / len(trades)
@@ -0,0 +1,5 @@
+node_modules/
+.next/
+out/
+out-www/
+_next-app-backup/
@@ -0,0 +1,21 @@
+{
+  "$schema": "https://ui.shadcn.com/schema.json",
+  "style": "new-york",
+  "rsc": true,
+  "tsx": true,
+  "tailwind": {
+    "config": "",
+    "css": "src/app/globals.css",
+    "baseColor": "slate",
+    "cssVariables": true,
+    "prefix": ""
+  },
+  "iconLibrary": "lucide",
+  "aliases": {
+    "components": "@/components",
+    "utils": "@/lib/utils",
+    "ui": "@/components/ui",
+    "lib": "@/lib",
+    "hooks": "@/hooks"
+  }
+}
@@ -0,0 +1,10 @@
+import type { NextConfig } from "next";
+
+const nextConfig: NextConfig = {
+  output: "export",
+  images: { unoptimized: true },
+  trailingSlash: true,
+  basePath: "/cv",
+};
+
+export default nextConfig;
@@ -0,0 +1,23 @@
+1:"$Sreact.fragment"
+2:I[47257,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ClientPageRoot"]
+3:I[52683,["/cv/_next/static/chunks/0h2qsuyze9ds1.js","/cv/_next/static/chunks/0oyhbmfwzlodf.js"],"default"]
+6:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"OutletBoundary"]
+7:"$Sreact.suspense"
+b:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ViewportBoundary"]
+c:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"MetadataBoundary"]
+d:I[27201,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"IconMark"]
+f:I[39756,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+10:I[37457,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+:HL["/cv/_next/static/chunks/3wqi32p1v2fl1.css","style"]
+a:X
+0:{"buildId":"CxC9DIW0Ude54_bbbG52g","data":[{"rsc":["$","$1","c",{"children":[["$","$L2",null,{"Component":"$3","serverProvidedParams":{"searchParams":{},"params":{},"promises":["$@4","$@5"]}}],[["$","script","script-0",{"src":"/cv/_next/static/chunks/0oyhbmfwzlodf.js","async":true}]],["$","$L6",null,{"children":["$","$7",null,{"name":"Next.MetadataOutlet","children":"$@8"}]}]]}],"isPartial":"$@9","staleTime":"$a","varyParams":null},{"rsc":["$","$1","h",{"children":[null,["$","$Lb",null,{"children":[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]}],["$","div",null,{"hidden":true,"children":["$","$Lc",null,{"children":["$","$7",null,{"name":"Next.Metadata","children":[["$","title","0",{"children":"FTDT Quant Lab"}],["$","meta","1",{"name":"description","content":"Professional quantitative trading dashboard — Live, Paper, Backtest, Historical"}],["$","link","2",{"rel":"icon","href":"/cv/favicon.ico?favicon.2vob68tjqpejf.ico","sizes":"256x256","type":"image/x-icon"}],["$","$Ld","3",{}]]}]}]}],["$","meta",null,{"name":"next-size-adjust","content":""}]]}],"isPartial":"$@e","staleTime":"$a","varyParams":null},{"rsc":["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next"}],["$","script","script-0",{"src":"/cv/_next/static/chunks/0h2qsuyze9ds1.js","async":true}]],["$","html",null,{"lang":"en","className":"dark","children":["$","body",null,{"className":"inter_b2991b2-module__9mH_6q__variable jetbrains_mono_d5591ac2-module__D88TVW__variable antialiased","children":["$","$Lf",null,{"parallelRouterKey":"children","template":["$","$L10",null,{}],"notFound":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":404}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],[]]}]}]}]]}],"isPartial":"$@11","staleTime":"$a","varyParams":null}],"isUpgradeableISRFallback":false,"a":"$@12","rootVaryParams":null,"needsRuntimeRequest":"$@13"}
+4:{}
+5:"$0:data:0:rsc:props:children:0:props:serverProvidedParams:params"
+8:null
+a:300
+13:true
+a:C
+12:0
+e:"$undefined"
+11:"$undefined"
+9:"$undefined"
@@ -0,0 +1,20 @@
+1:"$Sreact.fragment"
+2:I[39756,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+3:I[37457,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+4:I[47257,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ClientPageRoot"]
+5:I[52683,["/cv/_next/static/chunks/0h2qsuyze9ds1.js","/cv/_next/static/chunks/0oyhbmfwzlodf.js"],"default"]
+8:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"OutletBoundary"]
+9:"$Sreact.suspense"
+b:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ViewportBoundary"]
+d:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"MetadataBoundary"]
+f:I[68027,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default",1]
+:HL["/cv/_next/static/chunks/3wqi32p1v2fl1.css","style"]
+:HL["/cv/_next/static/media/70bc3e132a0a741e-s.p.3t6q91iet4nsy.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
+:HL["/cv/_next/static/media/83afe278b6a6bb3c-s.p.2bn3s6zvc0dyp.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
+0:{"P":null,"c":["",""],"q":"","i":false,"f":[[["",{"children":["__PAGE__",{},"$undefined","$undefined",4608]},"$undefined","$undefined",4624],[["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}],["$","script","script-0",{"src":"/cv/_next/static/chunks/0h2qsuyze9ds1.js","async":true,"nonce":"$undefined"}]],["$","html",null,{"lang":"en","className":"dark","children":["$","body",null,{"className":"inter_b2991b2-module__9mH_6q__variable jetbrains_mono_d5591ac2-module__D88TVW__variable antialiased","children":["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":404}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],[]],"forbidden":"$undefined","unauthorized":"$undefined"}]}]}]]}],{"children":[["$","$1","c",{"children":[["$","$L4",null,{"Component":"$5","serverProvidedParams":{"searchParams":{},"params":{},"promises":["$@6","$@7"]}}],[["$","script","script-0",{"src":"/cv/_next/static/chunks/0oyhbmfwzlodf.js","async":true,"nonce":"$undefined"}]],["$","$L8",null,{"children":["$","$9",null,{"name":"Next.MetadataOutlet","children":"$@a"}]}]]}],{},null,false,null]},null,false,null],["$","$1","h",{"children":[null,["$","$Lb",null,{"children":"$Lc"}],["$","div",null,{"hidden":true,"children":["$","$Ld",null,{"children":["$","$9",null,{"name":"Next.Metadata","children":"$Le"}]}]}],["$","meta",null,{"name":"next-size-adjust","content":""}]]}],false]],"m":"$undefined","G":["$f",[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]]],"S":true,"h":null,"r":"$undefined","s":"$undefined","a":"$undefined","l":"$undefined","p":"$undefined","d":"$undefined","b":"CxC9DIW0Ude54_bbbG52g"}
+6:{}
+7:"$0:f:0:1:1:children:0:props:children:0:props:serverProvidedParams:params"
+c:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]
+10:I[27201,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"IconMark"]
+a:null
+e:[["$","title","0",{"children":"FTDT Quant Lab"}],["$","meta","1",{"name":"description","content":"Professional quantitative trading dashboard — Live, Paper, Backtest, Historical"}],["$","link","2",{"rel":"icon","href":"/cv/favicon.ico?favicon.2vob68tjqpejf.ico","sizes":"256x256","type":"image/x-icon"}],["$","$L10","3",{}]]
@@ -0,0 +1,4 @@
+:HL["/cv/_next/static/chunks/3wqi32p1v2fl1.css","style"]
+:HL["/cv/_next/static/media/70bc3e132a0a741e-s.p.3t6q91iet4nsy.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
+:HL["/cv/_next/static/media/83afe278b6a6bb3c-s.p.2bn3s6zvc0dyp.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
+0:{"tree":{"name":"","param":null,"prefetchHints":4176,"slots":{"children":{"name":"__PAGE__","param":null,"prefetchHints":4256,"slots":null}}},"staleTime":300,"buildId":"CxC9DIW0Ude54_bbbG52g"}
@@ -0,0 +1,11 @@
+self.__BUILD_MANIFEST = {
+  "__rewrites": {
+    "afterFiles": [],
+    "beforeFiles": [],
+    "fallback": []
+  },
+  "sortedPages": [
+    "/_app",
+    "/_error"
+  ]
+};self.__BUILD_MANIFEST_CB && self.__BUILD_MANIFEST_CB()
@@ -0,0 +1 @@
+self.__MIDDLEWARE_MATCHERS = [];self.__MIDDLEWARE_MATCHERS_CB && self.__MIDDLEWARE_MATCHERS_CB()
@@ -0,0 +1 @@
+self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()
@@ -0,0 +1,11 @@
+self.__BUILD_MANIFEST = {
+  "__rewrites": {
+    "afterFiles": [],
+    "beforeFiles": [],
+    "fallback": []
+  },
+  "sortedPages": [
+    "/_app",
+    "/_error"
+  ]
+};self.__BUILD_MANIFEST_CB && self.__BUILD_MANIFEST_CB()
@@ -0,0 +1 @@
+self.__MIDDLEWARE_MATCHERS = [];self.__MIDDLEWARE_MATCHERS_CB && self.__MIDDLEWARE_MATCHERS_CB()
@@ -0,0 +1 @@
+self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()
@@ -0,0 +1,16 @@
+1:"$Sreact.fragment"
+2:I[39756,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+3:I[37457,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+4:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"OutletBoundary"]
+5:"$Sreact.suspense"
+8:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ViewportBoundary"]
+a:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"MetadataBoundary"]
+c:I[68027,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default",1]
+:HL["/cv/_next/static/chunks/3wqi32p1v2fl1.css","style"]
+7:X
+0:{"P":null,"c":["","_not-found",""],"q":"","i":false,"f":[[["",{"children":["/_not-found",{"children":["__PAGE__",{},"$undefined","$undefined",4608]},"$undefined","$undefined",4608]},"$undefined","$undefined",4624],[["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}],["$","script","script-0",{"src":"/cv/_next/static/chunks/0h2qsuyze9ds1.js","async":true,"nonce":"$undefined"}]],["$","html",null,{"lang":"en","className":"dark","children":["$","body",null,{"className":"inter_b2991b2-module__9mH_6q__variable jetbrains_mono_d5591ac2-module__D88TVW__variable antialiased","children":["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":404}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],[]],"forbidden":"$undefined","unauthorized":"$undefined"}]}]}]]}],{"children":[["$","$1","c",{"children":[null,["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}]]}],{"children":[["$","$1","c",{"children":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":"$0:f:0:1:0:props:children:1:props:children:props:children:props:notFound:0:1:props:style","children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":"$0:f:0:1:0:props:children:1:props:children:props:children:props:notFound:0:1:props:children:props:children:1:props:style","children":404}],["$","div",null,{"style":"$0:f:0:1:0:props:children:1:props:children:props:children:props:notFound:0:1:props:children:props:children:2:props:style","children":["$","h2",null,{"style":"$0:f:0:1:0:props:children:1:props:children:props:children:props:notFound:0:1:props:children:props:children:2:props:children:props:style","children":"This page could not be found."}]}]]}]}]],null,["$","$L4",null,{"children":["$","$5",null,{"name":"Next.MetadataOutlet","children":"$@6"}]}]]}],{},null,false,null]},null,false,"$7"]},null,false,null],["$","$1","h",{"children":[["$","meta",null,{"name":"robots","content":"noindex"}],["$","$L8",null,{"children":"$L9"}],["$","div",null,{"hidden":true,"children":["$","$La",null,{"children":["$","$5",null,{"name":"Next.Metadata","children":"$Lb"}]}]}],["$","meta",null,{"name":"next-size-adjust","content":""}]]}],false]],"m":"$undefined","G":["$c",[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]]],"S":true,"h":null,"r":"$undefined","s":"$undefined","a":"$undefined","l":"$undefined","p":"$undefined","d":"$undefined","b":"CxC9DIW0Ude54_bbbG52g"}
+7:C
+9:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]
+d:I[27201,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"IconMark"]
+6:null
+b:[["$","title","0",{"children":"FTDT Quant Lab"}],["$","meta","1",{"name":"description","content":"Professional quantitative trading dashboard — Live, Paper, Backtest, Historical"}],["$","link","2",{"rel":"icon","href":"/cv/favicon.ico?favicon.2vob68tjqpejf.ico","sizes":"256x256","type":"image/x-icon"}],["$","$Ld","3",{}]]
@@ -0,0 +1,22 @@
+1:"$Sreact.fragment"
+2:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"OutletBoundary"]
+3:"$Sreact.suspense"
+7:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ViewportBoundary"]
+8:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"MetadataBoundary"]
+9:I[27201,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"IconMark"]
+b:I[39756,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+c:I[37457,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+:HL["/cv/_next/static/chunks/3wqi32p1v2fl1.css","style"]
+6:X
+e:X
+e:C
+0:{"buildId":"CxC9DIW0Ude54_bbbG52g","data":[{"rsc":["$","$1","c",{"children":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":404}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],null,["$","$L2",null,{"children":["$","$3",null,{"name":"Next.MetadataOutlet","children":"$@4"}]}]]}],"isPartial":"$@5","staleTime":"$6","varyParams":null},{"rsc":["$","$1","h",{"children":[["$","meta",null,{"name":"robots","content":"noindex"}],["$","$L7",null,{"children":[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]}],["$","div",null,{"hidden":true,"children":["$","$L8",null,{"children":["$","$3",null,{"name":"Next.Metadata","children":[["$","title","0",{"children":"FTDT Quant Lab"}],["$","meta","1",{"name":"description","content":"Professional quantitative trading dashboard — Live, Paper, Backtest, Historical"}],["$","link","2",{"rel":"icon","href":"/cv/favicon.ico?favicon.2vob68tjqpejf.ico","sizes":"256x256","type":"image/x-icon"}],["$","$L9","3",{}]]}]}]}],["$","meta",null,{"name":"next-size-adjust","content":""}]]}],"isPartial":"$@a","staleTime":"$6","varyParams":null},{"rsc":["$","$1","c",{"children":[null,["$","$Lb",null,{"parallelRouterKey":"children","template":["$","$Lc",null,{}]}]]}],"isPartial":"$@d","staleTime":"$6","varyParams":"$e"},{"rsc":["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next"}],["$","script","script-0",{"src":"/cv/_next/static/chunks/0h2qsuyze9ds1.js","async":true}]],["$","html",null,{"lang":"en","className":"dark","children":["$","body",null,{"className":"inter_b2991b2-module__9mH_6q__variable jetbrains_mono_d5591ac2-module__D88TVW__variable antialiased","children":["$","$Lb",null,{"parallelRouterKey":"children","template":["$","$Lc",null,{}],"notFound":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":"$0:data:0:rsc:props:children:0:1:props:style","children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":"$0:data:0:rsc:props:children:0:1:props:children:props:children:1:props:style","children":404}],["$","div",null,{"style":"$0:data:0:rsc:props:children:0:1:props:children:props:children:2:props:style","children":["$","h2",null,{"style":"$0:data:0:rsc:props:children:0:1:props:children:props:children:2:props:children:props:style","children":"This page could not be found."}]}]]}]}]],[]]}]}]}]]}],"isPartial":"$@f","staleTime":"$6","varyParams":null}],"isUpgradeableISRFallback":false,"a":"$@10","rootVaryParams":null,"needsRuntimeRequest":"$@11"}
+4:null
+6:300
+11:true
+6:C
+10:0
+a:"$undefined"
+d:"$undefined"
+f:"$undefined"
+5:"$undefined"
@@ -0,0 +1,2 @@
+:HL["/cv/_next/static/chunks/3wqi32p1v2fl1.css","style"]
+0:{"tree":{"name":"","param":null,"prefetchHints":4176,"slots":{"children":{"name":"/_not-found","param":null,"prefetchHints":4192,"slots":{"children":{"name":"__PAGE__","param":null,"prefetchHints":4256,"slots":null}}}}},"staleTime":300,"buildId":"CxC9DIW0Ude54_bbbG52g"}
@@ -0,0 +1,16 @@
+1:"$Sreact.fragment"
+2:I[39756,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+3:I[37457,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+4:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"OutletBoundary"]
+5:"$Sreact.suspense"
+8:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ViewportBoundary"]
+a:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"MetadataBoundary"]
+c:I[68027,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default",1]
+:HL["/cv/_next/static/chunks/3wqi32p1v2fl1.css","style"]
+7:X
+0:{"P":null,"c":["","_not-found",""],"q":"","i":false,"f":[[["",{"children":["/_not-found",{"children":["__PAGE__",{},"$undefined","$undefined",4608]},"$undefined","$undefined",4608]},"$undefined","$undefined",4624],[["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}],["$","script","script-0",{"src":"/cv/_next/static/chunks/0h2qsuyze9ds1.js","async":true,"nonce":"$undefined"}]],["$","html",null,{"lang":"en","className":"dark","children":["$","body",null,{"className":"inter_b2991b2-module__9mH_6q__variable jetbrains_mono_d5591ac2-module__D88TVW__variable antialiased","children":["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":404}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],[]],"forbidden":"$undefined","unauthorized":"$undefined"}]}]}]]}],{"children":[["$","$1","c",{"children":[null,["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}]]}],{"children":[["$","$1","c",{"children":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":"$0:f:0:1:0:props:children:1:props:children:props:children:props:notFound:0:1:props:style","children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":"$0:f:0:1:0:props:children:1:props:children:props:children:props:notFound:0:1:props:children:props:children:1:props:style","children":404}],["$","div",null,{"style":"$0:f:0:1:0:props:children:1:props:children:props:children:props:notFound:0:1:props:children:props:children:2:props:style","children":["$","h2",null,{"style":"$0:f:0:1:0:props:children:1:props:children:props:children:props:notFound:0:1:props:children:props:children:2:props:children:props:style","children":"This page could not be found."}]}]]}]}]],null,["$","$L4",null,{"children":["$","$5",null,{"name":"Next.MetadataOutlet","children":"$@6"}]}]]}],{},null,false,null]},null,false,"$7"]},null,false,null],["$","$1","h",{"children":[["$","meta",null,{"name":"robots","content":"noindex"}],["$","$L8",null,{"children":"$L9"}],["$","div",null,{"hidden":true,"children":["$","$La",null,{"children":["$","$5",null,{"name":"Next.Metadata","children":"$Lb"}]}]}],["$","meta",null,{"name":"next-size-adjust","content":""}]]}],false]],"m":"$undefined","G":["$c",[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]]],"S":true,"h":null,"r":"$undefined","s":"$undefined","a":"$undefined","l":"$undefined","p":"$undefined","d":"$undefined","b":"CxC9DIW0Ude54_bbbG52g"}
+7:C
+9:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]
+d:I[27201,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"IconMark"]
+6:null
+b:[["$","title","0",{"children":"FTDT Quant Lab"}],["$","meta","1",{"name":"description","content":"Professional quantitative trading dashboard — Live, Paper, Backtest, Historical"}],["$","link","2",{"rel":"icon","href":"/cv/favicon.ico?favicon.2vob68tjqpejf.ico","sizes":"256x256","type":"image/x-icon"}],["$","$Ld","3",{}]]
@@ -0,0 +1 @@
+<svg fill="none" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg"><path d="M14.5 13.5V5.41a1 1 0 0 0-.3-.7L9.8.29A1 1 0 0 0 9.08 0H1.5v13.5A2.5 2.5 0 0 0 4 16h8a2.5 2.5 0 0 0 2.5-2.5m-1.5 0v-7H8v-5H3v12a1 1 0 0 0 1 1h8a1 1 0 0 0 1-1M9.5 5V2.12L12.38 5zM5.13 5h-.62v1.25h2.12V5zm-.62 3h7.12v1.25H4.5zm.62 3h-.62v1.25h7.12V11z" clip-rule="evenodd" fill="#666" fill-rule="evenodd"/></svg>
@@ -0,0 +1 @@
+<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><g clip-path="url(#a)"><path fill-rule="evenodd" clip-rule="evenodd" d="M10.27 14.1a6.5 6.5 0 0 0 3.67-3.45q-1.24.21-2.7.34-.31 1.83-.97 3.1M8 16A8 8 0 1 0 8 0a8 8 0 0 0 0 16m.48-1.52a7 7 0 0 1-.96 0H7.5a4 4 0 0 1-.84-1.32q-.38-.89-.63-2.08a40 40 0 0 0 3.92 0q-.25 1.2-.63 2.08a4 4 0 0 1-.84 1.31zm2.94-4.76q1.66-.15 2.95-.43a7 7 0 0 0 0-2.58q-1.3-.27-2.95-.43a18 18 0 0 1 0 3.44m-1.27-3.54a17 17 0 0 1 0 3.64 39 39 0 0 1-4.3 0 17 17 0 0 1 0-3.64 39 39 0 0 1 4.3 0m1.1-1.17q1.45.13 2.69.34a6.5 6.5 0 0 0-3.67-3.44q.65 1.26.98 3.1M8.48 1.5l.01.02q.41.37.84 1.31.38.89.63 2.08a40 40 0 0 0-3.92 0q.25-1.2.63-2.08a4 4 0 0 1 .85-1.32 7 7 0 0 1 .96 0m-2.75.4a6.5 6.5 0 0 0-3.67 3.44 29 29 0 0 1 2.7-.34q.31-1.83.97-3.1M4.58 6.28q-1.66.16-2.95.43a7 7 0 0 0 0 2.58q1.3.27 2.95.43a18 18 0 0 1 0-3.44m.17 4.71q-1.45-.12-2.69-.34a6.5 6.5 0 0 0 3.67 3.44q-.65-1.27-.98-3.1" fill="#666"/></g><defs><clipPath id="a"><path fill="#fff" d="M0 0h16v16H0z"/></clipPath></defs></svg>
@@ -0,0 +1,20 @@
+1:"$Sreact.fragment"
+2:I[39756,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+3:I[37457,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default"]
+4:I[47257,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ClientPageRoot"]
+5:I[52683,["/cv/_next/static/chunks/0h2qsuyze9ds1.js","/cv/_next/static/chunks/0oyhbmfwzlodf.js"],"default"]
+8:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"OutletBoundary"]
+9:"$Sreact.suspense"
+b:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"ViewportBoundary"]
+d:I[97367,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"MetadataBoundary"]
+f:I[68027,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"default",1]
+:HL["/cv/_next/static/chunks/3wqi32p1v2fl1.css","style"]
+:HL["/cv/_next/static/media/70bc3e132a0a741e-s.p.3t6q91iet4nsy.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
+:HL["/cv/_next/static/media/83afe278b6a6bb3c-s.p.2bn3s6zvc0dyp.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
+0:{"P":null,"c":["",""],"q":"","i":false,"f":[[["",{"children":["__PAGE__",{},"$undefined","$undefined",4608]},"$undefined","$undefined",4624],[["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}],["$","script","script-0",{"src":"/cv/_next/static/chunks/0h2qsuyze9ds1.js","async":true,"nonce":"$undefined"}]],["$","html",null,{"lang":"en","className":"dark","children":["$","body",null,{"className":"inter_b2991b2-module__9mH_6q__variable jetbrains_mono_d5591ac2-module__D88TVW__variable antialiased","children":["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":404}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],[]],"forbidden":"$undefined","unauthorized":"$undefined"}]}]}]]}],{"children":[["$","$1","c",{"children":[["$","$L4",null,{"Component":"$5","serverProvidedParams":{"searchParams":{},"params":{},"promises":["$@6","$@7"]}}],[["$","script","script-0",{"src":"/cv/_next/static/chunks/0oyhbmfwzlodf.js","async":true,"nonce":"$undefined"}]],["$","$L8",null,{"children":["$","$9",null,{"name":"Next.MetadataOutlet","children":"$@a"}]}]]}],{},null,false,null]},null,false,null],["$","$1","h",{"children":[null,["$","$Lb",null,{"children":"$Lc"}],["$","div",null,{"hidden":true,"children":["$","$Ld",null,{"children":["$","$9",null,{"name":"Next.Metadata","children":"$Le"}]}]}],["$","meta",null,{"name":"next-size-adjust","content":""}]]}],false]],"m":"$undefined","G":["$f",[["$","link","0",{"rel":"stylesheet","href":"/cv/_next/static/chunks/3wqi32p1v2fl1.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]]],"S":true,"h":null,"r":"$undefined","s":"$undefined","a":"$undefined","l":"$undefined","p":"$undefined","d":"$undefined","b":"CxC9DIW0Ude54_bbbG52g"}
+6:{}
+7:"$0:f:0:1:1:children:0:props:children:0:props:serverProvidedParams:params"
+c:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]
+10:I[27201,["/cv/_next/static/chunks/0h2qsuyze9ds1.js"],"IconMark"]
+a:null
+e:[["$","title","0",{"children":"FTDT Quant Lab"}],["$","meta","1",{"name":"description","content":"Professional quantitative trading dashboard — Live, Paper, Backtest, Historical"}],["$","link","2",{"rel":"icon","href":"/cv/favicon.ico?favicon.2vob68tjqpejf.ico","sizes":"256x256","type":"image/x-icon"}],["$","$L10","3",{}]]
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 394 80"><path fill="#000" d="M262 0h68.5v12.7h-27.2v66.6h-13.6V12.7H262V0ZM149 0v12.7H94v20.4h44.3v12.6H94v21h55v12.6H80.5V0h68.7zm34.3 0h-17.8l63.8 79.4h17.9l-32-39.7 32-39.6h-17.9l-23 28.6-23-28.6zm18.3 56.7-9-11-27.1 33.7h17.8l18.3-22.7z"/><path fill="#000" d="M81 79.3 17 0H0v79.3h13.6V17l50.2 62.3H81Zm252.6-.4c-1 0-1.8-.4-2.5-1s-1.1-1.6-1.1-2.6.3-1.8 1-2.5 1.6-1 2.6-1 1.8.3 2.5 1a3.4 3.4 0 0 1 .6 4.3 3.7 3.7 0 0 1-3 1.8zm23.2-33.5h6v23.3c0 2.1-.4 4-1.3 5.5a9.1 9.1 0 0 1-3.8 3.5c-1.6.8-3.5 1.3-5.7 1.3-2 0-3.7-.4-5.3-1s-2.8-1.8-3.7-3.2c-.9-1.3-1.4-3-1.4-5h6c.1.8.3 1.6.7 2.2s1 1.2 1.6 1.5c.7.4 1.5.5 2.4.5 1 0 1.8-.2 2.4-.6a4 4 0 0 0 1.6-1.8c.3-.8.5-1.8.5-3V45.5zm30.9 9.1a4.4 4.4 0 0 0-2-3.3 7.5 7.5 0 0 0-4.3-1.1c-1.3 0-2.4.2-3.3.5-.9.4-1.6 1-2 1.6a3.5 3.5 0 0 0-.3 4c.3.5.7.9 1.3 1.2l1.8 1 2 .5 3.2.8c1.3.3 2.5.7 3.7 1.2a13 13 0 0 1 3.2 1.8 8.1 8.1 0 0 1 3 6.5c0 2-.5 3.7-1.5 5.1a10 10 0 0 1-4.4 3.5c-1.8.8-4.1 1.2-6.8 1.2-2.6 0-4.9-.4-6.8-1.2-2-.8-3.4-2-4.5-3.5a10 10 0 0 1-1.7-5.6h6a5 5 0 0 0 3.5 4.6c1 .4 2.2.6 3.4.6 1.3 0 2.5-.2 3.5-.6 1-.4 1.8-1 2.4-1.7a4 4 0 0 0 .8-2.4c0-.9-.2-1.6-.7-2.2a11 11 0 0 0-2.1-1.4l-3.2-1-3.8-1c-2.8-.7-5-1.7-6.6-3.2a7.2 7.2 0 0 1-2.4-5.7 8 8 0 0 1 1.7-5 10 10 0 0 1 4.3-3.5c2-.8 4-1.2 6.4-1.2 2.3 0 4.4.4 6.2 1.2 1.8.8 3.2 2 4.3 3.4 1 1.4 1.5 3 1.5 5h-5.8z"/></svg>
@@ -0,0 +1 @@
+<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1155 1000"><path d="m577.3 0 577.4 1000H0z" fill="#fff"/></svg>
@@ -0,0 +1 @@
+<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path fill-rule="evenodd" clip-rule="evenodd" d="M1.5 2.5h13v10a1 1 0 0 1-1 1h-11a1 1 0 0 1-1-1zM0 1h16v11.5a2.5 2.5 0 0 1-2.5 2.5h-11A2.5 2.5 0 0 1 0 12.5zm3.75 4.5a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5M7 4.75a.75.75 0 1 1-1.5 0 .75.75 0 0 1 1.5 0m1.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5" fill="#666"/></svg>
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
				{"strategy": "Hurst VPIN", "strategy_key": "hurst_vpin", "coin": "ETH", "allocation": 99.9999907962162, "start_time": "2026-08-06T07:09:06.405305", "end_time": "2026-08-06T07:09:06.405333", "start_equity": 100.0, "end_equity": 99.9374907962162, "pnl": -0.06, "pnl_pct": -0.06, "sharpe": 0.35, "sortino": 0.64, "max_dd": 0.0003, "win_rate": 0.0, "total_trades": 1, "trades": [{"time": "2026-08-06T07:03:31.457823", "side": "SELL", "entry_price": 1868.6, "size": 0.00024, "hurst": 0.5751, "vpin": 0.7986, "bar_count": 236, "exit_price": 1856.9195301809586, "pnl": -0.0625}], "equity_curve": [{"t": 0, "v": 100.0}, {"t": 1000, "v": 99.9374907962162}], "signals_generated": 15385, "data_source": "hyperliquid_mainnet"}
				`@@ -0,0 +1 @@`
				{"strategy": "SPX Mean Reversion", "strategy_key": "spx_mean_reversion", "coin": "SPX", "allocation": 100.0, "start_time": "2026-07-30T07:30:00", "end_time": "2026-08-06T07:30:00", "start_equity": 100.0, "end_equity": 100.14, "pnl": 0.14, "pnl_pct": 0.14, "sharpe": 0.63, "sortino": 1.0, "max_dd": 0.01, "win_rate": 0.7727, "total_trades": 22, "trades": [{"time": "2026-08-06T07:34:45.055488", "side": "BUY", "entry_price": 0.33078, "z_entry": -1.58, "size": 0.01, "exit_price": 0.33383, "pnl": 0.0092, "exit_z": -0.28}, {"time": "2026-08-06T07:34:45.055850", "side": "SELL", "entry_price": 0.33624, "z_entry": 2.24, "size": 0.01, "exit_price": 0.32967, "pnl": 0.0195, "exit_z": -1.78}, {"time": "2026-08-06T07:34:45.055974", "side": "BUY", "entry_price": 0.32918, "z_entry": -1.83, "size": 0.01, "exit_price": 0.32434, "pnl": -0.0147, "exit_z": -0.22}, {"time": "2026-08-06T07:34:45.056641", "side": "BUY", "entry_price": 0.32171, "z_entry": -1.5, "size": 0.01, "exit_price": 0.32165, "pnl": -0.0002, "exit_z": 0.18}, {"time": "2026-08-06T07:34:45.057239", "side": "SELL", "entry_price": 0.32372, "z_entry": 1.84, "size": 0.01, "exit_price": 0.3218, "pnl": 0.0059, "exit_z": 0.19}, {"time": "2026-08-06T07:34:45.057798", "side": "SELL", "entry_price": 0.32404, "z_entry": 2.08, "size": 0.01, "exit_price": 0.32262, "pnl": 0.0044, "exit_z": -0.06}, {"time": "2026-08-06T07:34:45.058058", "side": "SELL", "entry_price": 0.32373, "z_entry": 1.62, "size": 0.01, "exit_price": 0.32046, "pnl": 0.0101, "exit_z": -3.19}, {"time": "2026-08-06T07:34:45.058229", "side": "BUY", "entry_price": 0.31678, "z_entry": -8.2, "size": 0.01, "exit_price": 0.31904, "pnl": 0.0071, "exit_z": -0.06}, {"time": "2026-08-06T07:34:45.058593", "side": "SELL", "entry_price": 0.32421, "z_entry": 2.0, "size": 0.01, "exit_price": 0.32154, "pnl": 0.0082, "exit_z": 0.09}, {"time": "2026-08-06T07:34:45.059004", "side": "BUY", "entry_price": 0.31961, "z_entry": -1.91, "size": 0.01, "exit_price": 0.32139, "pnl": 0.0056, "exit_z": -0.24}, {"time": "2026-08-06T07:34:45.059289", "side": "SELL", "entry_price": 0.32407, "z_entry": 2.02, "size": 0.01, "exit_price": 0.32727, "pnl": -0.0099, "exit_z": 0.04}, {"time": "2026-08-06T07:34:45.059931", "side": "BUY", "entry_price": 0.32388, "z_entry": -1.66, "size": 0.01, "exit_price": 0.32533, "pnl": 0.0045, "exit_z": -0.16}, {"time": "2026-08-06T07:34:45.060187", "side": "SELL", "entry_price": 0.33477, "z_entry": 4.22, "size": 0.01, "exit_price": 0.3297, "pnl": 0.0152, "exit_z": -0.11}, {"time": "2026-08-06T07:34:45.060590", "side": "BUY", "entry_price": 0.32663, "z_entry": -1.55, "size": 0.01, "exit_price": 0.32146, "pnl": -0.0158, "exit_z": 0.27}, {"time": "2026-08-06T07:34:45.061181", "side": "SELL", "entry_price": 0.32413, "z_entry": 2.06, "size": 0.01, "exit_price": 0.32194, "pnl": 0.0068, "exit_z": -0.01}, {"time": "2026-08-06T07:34:45.061481", "side": "SELL", "entry_price": 0.32694, "z_entry": 2.25, "size": 0.01, "exit_price": 0.32449, "pnl": 0.0075, "exit_z": 0.2}, {"time": "2026-08-06T07:34:45.061750", "side": "BUY", "entry_price": 0.32057, "z_entry": -3.02, "size": 0.01, "exit_price": 0.32404, "pnl": 0.0108, "exit_z": -0.18}, {"time": "2026-08-06T07:34:45.061851", "side": "SELL", "entry_price": 0.32752, "z_entry": 2.03, "size": 0.01, "exit_price": 0.32466, "pnl": 0.0087, "exit_z": 0.26}, {"time": "2026-08-06T07:34:45.061952", "side": "SELL", "entry_price": 0.32729, "z_entry": 1.58, "size": 0.01, "exit_price": 0.32752, "pnl": -0.0007, "exit_z": 0.28}, {"time": "2026-08-06T07:34:45.062542", "side": "BUY", "entry_price": 0.32456, "z_entry": -1.89, "size": 0.01, "exit_price": 0.3315, "pnl": 0.0214, "exit_z": 2.35}, {"time": "2026-08-06T07:34:45.062693", "side": "SELL", "entry_price": 0.33111, "z_entry": 2.07, "size": 0.01, "exit_price": 0.3259, "pnl": 0.0158, "exit_z": -2.79}, {"time": "2026-08-06T07:34:45.063074", "side": "BUY", "entry_price": 0.32525, "z_entry": -2.84, "size": 0.01, "exit_price": 0.33141, "pnl": 0.019, "exit_z": -0.06}], "data_source": "TradeXYZ / Hyperliquid SPX"}
				`@@ -0,0 +1 @@`
				`self.__MIDDLEWARE_MATCHERS = [];self.__MIDDLEWARE_MATCHERS_CB && self.__MIDDLEWARE_MATCHERS_CB()`
				`@@ -0,0 +1 @@`
				`self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()`
				`@@ -0,0 +1 @@`
				`<svg fill="none" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg"><path d="M14.5 13.5V5.41a1 1 0 0 0-.3-.7L9.8.29A1 1 0 0 0 9.08 0H1.5v13.5A2.5 2.5 0 0 0 4 16h8a2.5 2.5 0 0 0 2.5-2.5m-1.5 0v-7H8v-5H3v12a1 1 0 0 0 1 1h8a1 1 0 0 0 1-1M9.5 5V2.12L12.38 5zM5.13 5h-.62v1.25h2.12V5zm-.62 3h7.12v1.25H4.5zm.62 3h-.62v1.25h7.12V11z" clip-rule="evenodd" fill="#666" fill-rule="evenodd"/></svg>`
				`@@ -0,0 +1 @@`
				<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><g clip-path="url(#a)"><path fill-rule="evenodd" clip-rule="evenodd" d="M10.27 14.1a6.5 6.5 0 0 0 3.67-3.45q-1.24.21-2.7.34-.31 1.83-.97 3.1M8 16A8 8 0 1 0 8 0a8 8 0 0 0 0 16m.48-1.52a7 7 0 0 1-.96 0H7.5a4 4 0 0 1-.84-1.32q-.38-.89-.63-2.08a40 40 0 0 0 3.92 0q-.25 1.2-.63 2.08a4 4 0 0 1-.84 1.31zm2.94-4.76q1.66-.15 2.95-.43a7 7 0 0 0 0-2.58q-1.3-.27-2.95-.43a18 18 0 0 1 0 3.44m-1.27-3.54a17 17 0 0 1 0 3.64 39 39 0 0 1-4.3 0 17 17 0 0 1 0-3.64 39 39 0 0 1 4.3 0m1.1-1.17q1.45.13 2.69.34a6.5 6.5 0 0 0-3.67-3.44q.65 1.26.98 3.1M8.48 1.5l.01.02q.41.37.84 1.31.38.89.63 2.08a40 40 0 0 0-3.92 0q.25-1.2.63-2.08a4 4 0 0 1 .85-1.32 7 7 0 0 1 .96 0m-2.75.4a6.5 6.5 0 0 0-3.67 3.44 29 29 0 0 1 2.7-.34q.31-1.83.97-3.1M4.58 6.28q-1.66.16-2.95.43a7 7 0 0 0 0 2.58q1.3.27 2.95.43a18 18 0 0 1 0-3.44m.17 4.71q-1.45-.12-2.69-.34a6.5 6.5 0 0 0 3.67 3.44q-.65-1.27-.98-3.1" fill="#666"/></g><defs><clipPath id="a"><path fill="#fff" d="M0 0h16v16H0z"/></clipPath></defs></svg>
				`@@ -0,0 +1 @@`
				<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 394 80"><path fill="#000" d="M262 0h68.5v12.7h-27.2v66.6h-13.6V12.7H262V0ZM149 0v12.7H94v20.4h44.3v12.6H94v21h55v12.6H80.5V0h68.7zm34.3 0h-17.8l63.8 79.4h17.9l-32-39.7 32-39.6h-17.9l-23 28.6-23-28.6zm18.3 56.7-9-11-27.1 33.7h17.8l18.3-22.7z"/><path fill="#000" d="M81 79.3 17 0H0v79.3h13.6V17l50.2 62.3H81Zm252.6-.4c-1 0-1.8-.4-2.5-1s-1.1-1.6-1.1-2.6.3-1.8 1-2.5 1.6-1 2.6-1 1.8.3 2.5 1a3.4 3.4 0 0 1 .6 4.3 3.7 3.7 0 0 1-3 1.8zm23.2-33.5h6v23.3c0 2.1-.4 4-1.3 5.5a9.1 9.1 0 0 1-3.8 3.5c-1.6.8-3.5 1.3-5.7 1.3-2 0-3.7-.4-5.3-1s-2.8-1.8-3.7-3.2c-.9-1.3-1.4-3-1.4-5h6c.1.8.3 1.6.7 2.2s1 1.2 1.6 1.5c.7.4 1.5.5 2.4.5 1 0 1.8-.2 2.4-.6a4 4 0 0 0 1.6-1.8c.3-.8.5-1.8.5-3V45.5zm30.9 9.1a4.4 4.4 0 0 0-2-3.3 7.5 7.5 0 0 0-4.3-1.1c-1.3 0-2.4.2-3.3.5-.9.4-1.6 1-2 1.6a3.5 3.5 0 0 0-.3 4c.3.5.7.9 1.3 1.2l1.8 1 2 .5 3.2.8c1.3.3 2.5.7 3.7 1.2a13 13 0 0 1 3.2 1.8 8.1 8.1 0 0 1 3 6.5c0 2-.5 3.7-1.5 5.1a10 10 0 0 1-4.4 3.5c-1.8.8-4.1 1.2-6.8 1.2-2.6 0-4.9-.4-6.8-1.2-2-.8-3.4-2-4.5-3.5a10 10 0 0 1-1.7-5.6h6a5 5 0 0 0 3.5 4.6c1 .4 2.2.6 3.4.6 1.3 0 2.5-.2 3.5-.6 1-.4 1.8-1 2.4-1.7a4 4 0 0 0 .8-2.4c0-.9-.2-1.6-.7-2.2a11 11 0 0 0-2.1-1.4l-3.2-1-3.8-1c-2.8-.7-5-1.7-6.6-3.2a7.2 7.2 0 0 1-2.4-5.7 8 8 0 0 1 1.7-5 10 10 0 0 1 4.3-3.5c2-.8 4-1.2 6.4-1.2 2.3 0 4.4.4 6.2 1.2 1.8.8 3.2 2 4.3 3.4 1 1.4 1.5 3 1.5 5h-5.8z"/></svg>
				`@@ -0,0 +1 @@`
				`<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1155 1000"><path d="m577.3 0 577.4 1000H0z" fill="#fff"/></svg>`