feat: funding arb strategy, queue-aware paper fills, WQI live integration

- strategies/funding_arb_strategy.py: full backtestable funding rate carry module
  with entry/exit thresholds, position tracking, funding payment accounting,
  basis stop-loss, max-hold timeout. Includes backtest_funding_arb() and
  run_funding_discovery() for threshold optimization
- live/node_v2.py: replaced naive random fills with QueueAwareFillModel (sim/fills.py)
  with queue-priority simulation; integrated WQI predictor and funding arb strategies;
  per-coin WQI signal generation every 3 ticks; funding arb metrics in dashboard
- cli.py: added 'funding' command for funding rate distribution analysis and
  threshold backtesting
- tests/test_funding_arb.py: 20 tests covering entry/exit logic, fee accounting,
  signal generation, backtesting, and node integration

321 tests passing (20 new).

cli.py

@@ -499,6 +499,52 @@ def cmd_discover(args):
     print(f"\nPipeline ready. Run 'python -m cli tick' to backtest strategies on this data.")
 
 
+def cmd_funding(args):
+    """Funding rate arb discovery — analyze historical funding rates and run backtests."""
+    from strategies.funding_arb_strategy import run_funding_discovery
+    import json as _json
+
+    result = run_funding_discovery(
+        data_dir=args.data_dir,
+        coin=args.coin,
+        start_date=args.start_date,
+        end_date=args.end_date,
+    )
+
+    if "error" in result:
+        print(f"Error: {result['error']}")
+        return
+
+    print(f"\n{'═' * 60}")
+    print(f"  Funding Rate Analysis — {args.coin}")
+    print(f"  {result['n_observations']:,} observations")
+    print(f"{'═' * 60}")
+
+    dist = result["rate_distribution"]
+    print(f"\n  Rate Distribution (annualized):")
+    print(f"    Mean:  {dist['mean_apr_pct']:>8.2f}%")
+    print(f"    Std:   {dist['std_apr_pct']:>8.2f}%")
+    print(f"    Max:   {dist['max_apr_pct']:>8.2f}%")
+    print(f"    Min:   {dist['min_apr_pct']:>8.2f}%")
+    print(f"\n  Absolute Rate Percentiles:")
+    for k, v in dist["abs_percentiles"].items():
+        print(f"    {k}: {v:>8.2f}%")
+
+    print(f"\n{'─' * 60}")
+    print(f"  Backtest Results by Threshold:")
+    print(f"  {'Threshold':>12s}  {'Trades':>7s}  {'Win Rate':>9s}  "
+          f"{'Net PnL':>10s}  {'Avg PnL':>10s}  {'Avg Hold':>9s}")
+    print(f"  {'─' * 60}")
+    for label, bt in result.get("backtests", {}).items():
+        print(f"  {label:>12s}  {bt['total_trades']:>7d}  "
+              f"{bt['win_rate']:>8.1%}  "
+              f"${bt['total_net_pnl']:>9.4f}  ${bt['avg_net_pnl']:>9.4f}  "
+              f"{bt['avg_hold_hours']:>8.1f}h")
+
+    print(f"\n  Run 'python -m cli collect --mainnet' to gather data.")
+    print(f"  Then 'python -m cli funding --coin BTC' to re-run.")
+
+
 def main():
     import argparse
     p = argparse.ArgumentParser(description="FTDT Quant Lab CLI")
@@ -578,6 +624,13 @@ def main():
     pd.add_argument("--end-date", default="2026-08-07")
     pd.add_argument("--horizons", default="100,500,1000,5000,10000", help="Comma-separated ms horizons")
 
+    # funding
+    pf = sp.add_parser("funding", help="Funding rate arb discovery — analyze historical funding rates")
+    pf.add_argument("--data-dir", default="data/raw")
+    pf.add_argument("--coin", default="BTC")
+    pf.add_argument("--start-date", default="2026-01-01")
+    pf.add_argument("--end-date", default="2030-01-01")
+
     args = p.parse_args()
 
     import json as _json
@@ -598,6 +651,8 @@ def main():
         cmd_tick_backtest(args)
     elif args.command == "discover":
         cmd_discover(args)
+    elif args.command == "funding":
+        cmd_funding(args)
 
 
 if __name__ == "__main__":