feat: proper Order Book Imbalance strategy for BTC-USD on HL

strategies/nt/obi_nt.py:
- Dual-mode OBI: candle proxy (backtest) + real L2 orderbook (live)
- Volume-based imbalance: buy_vol / (buy_vol + sell_vol) over rolling window
- Entry when |imbalance| > 0.35, exit on reversion < 0.10
- Stop-loss 2%, take-profit 0.5%, cooldown 3 bars
- compute_signal(price, orderbook=None) for paper trader integration

backtests/vbt_runner.py:
- Replaced placeholder z-score with proper volume-based OBI
- Buy vol = volume where close > open, sell vol = volume where close < open
- Rolling window imbalance computation
- Parameter sweep support with 12 combos tested

Registered across: deploy.py, nt_runner.py, dashboard, strategies/nt/__init__

Verified:
- VectorBT OBI backtest: 15 trades, -7.2% on default (window=20)
- Param sweep best: w=30 t=0.35 → sharpe -0.82, 49% win, 23% DD
- Real L2 orderbook signal: BUY obi=0.880 (bids 88% of depth)
- NT backtest engine: 201 bars, 8 days, 236ms

framework/deploy.py

@@ -44,8 +44,8 @@ STRATEGY_REGISTRY = {
     },
     "obi": {
         "name": "Order Book Imbalance",
-        "description": "L2 bid/ask volume skew reversal",
-        "class": None,  # Not yet ported
+        "description": "Volume-weighted bid/ask skew — enters when L2 imbalance heavy",
+        "class": "strategies.nt.obi_nt.OBINT",
     },
     "funding_arb": {
         "name": "Funding Rate Arb",