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.

.venv/lib/python3.7/site-packages/pandas/io/spss.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Sequence
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.inference import is_list_like
+
+from pandas.core.api import DataFrame
+
+from pandas.io.common import stringify_path
+
+
+def read_spss(
+    path: str | Path,
+    usecols: Sequence[str] | None = None,
+    convert_categoricals: bool = True,
+) -> DataFrame:
+    """
+    Load an SPSS file from the file path, returning a DataFrame.
+
+    .. versionadded:: 0.25.0
+
+    Parameters
+    ----------
+    path : str or Path
+        File path.
+    usecols : list-like, optional
+        Return a subset of the columns. If None, return all columns.
+    convert_categoricals : bool, default is True
+        Convert categorical columns into pd.Categorical.
+
+    Returns
+    -------
+    DataFrame
+    """
+    pyreadstat = import_optional_dependency("pyreadstat")
+
+    if usecols is not None:
+        if not is_list_like(usecols):
+            raise TypeError("usecols must be list-like.")
+        else:
+            usecols = list(usecols)  # pyreadstat requires a list
+
+    df, _ = pyreadstat.read_sav(
+        stringify_path(path), usecols=usecols, apply_value_formats=convert_categoricals
+    )
+    return df