remove: V10 momentum system - backtest proved it adds no value

- Removed v10 TradingView indicator (moneyline_v10_momentum_dots.pinescript)
- Removed v10 penalty system from signal-quality.ts (-30/-25 point penalties)
- Removed backtest result files (sweep_*.csv)
- Updated copilot-instructions.md to remove v10 references
- Simplified direction-specific quality thresholds (LONG 90+, SHORT 80+)

Rationale:
- 1,944 parameter combinations tested in backtest
- All top results IDENTICAL (568 trades, $498 P&L, 61.09% WR)
- Momentum parameters had ZERO impact on trade selection
- Profit factor 1.027 too low (barely profitable after fees)
- Max drawdown -$1,270 vs +$498 profit = terrible risk-reward
- v10 penalties were blocking good trades (bug: applied to wrong positions)

Keeping v9 as production system - simpler, proven, effective.

backtester/data_loader.py

@@ -0,0 +1,38 @@
+"""Utilities for loading OHLCV data for local backtesting."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+import pandas as pd
+
+
+@dataclass
+class DataSlice:
+    symbol: str
+    timeframe: str
+    data: pd.DataFrame
+
+
+def load_csv(path: Path, symbol: str, timeframe: str, start: Optional[str] = None, end: Optional[str] = None) -> DataSlice:
+    if not path.exists():
+        raise FileNotFoundError(f"Missing data file: {path}")
+
+    df = pd.read_csv(path, parse_dates=["timestamp"])
+    df = df.sort_values("timestamp").reset_index(drop=True)
+    if start:
+        df = df[df["timestamp"] >= pd.Timestamp(start)]
+    if end:
+        df = df[df["timestamp"] <= pd.Timestamp(end)]
+    if df.empty:
+        raise ValueError("No rows remain after applying date filters")
+
+    expected_cols = {"timestamp", "open", "high", "low", "close", "volume"}
+    missing = expected_cols.difference(df.columns)
+    if missing:
+        raise ValueError(f"Missing columns in {path}: {sorted(missing)}")
+
+    df = df.set_index("timestamp")
+
+    return DataSlice(symbol=symbol.upper(), timeframe=timeframe, data=df)