Daily UpCap+MomGap trading on an IBKR paper account

Connects to IB Gateway through ib_async and trades the S&P 500 with the
UpCap+MomGap factor combo ported from quant-trading's
fc_up_cap_mom_gap_daily: signal on opens through today's open, equal
weight top 10, MOC orders filled at today's close, 10% resize band.

- make universe/bars/check/plan/submit/fills for the daily loop; refuses
  non-paper accounts unless allow_live, open orders, stale bars, and
  orders outside 09:31-15:45 ET
- make backtest replays the live loop day by day with plan_orders and
  IBKR Fixed commissions; selection uses each day's index members
- make bars fails fast when the Gateway's historical farm is down

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

src/ibkr_quant/strategy.py

@@ -0,0 +1,76 @@
+"""UpCap+MomGap factor combo, ported from quant-trading `strategies/factor_combo.py`
+(`fc_up_cap_mom_gap_daily`).
+
+The source runs on pandas 2, whose `pct_change` forward-fills gaps first;
+`_pct_change` keeps that behaviour under pandas 3.
+"""
+
+import numpy as np
+import pandas as pd
+
+# Rows the source zeroes before trading: 12-1 momentum needs 21 + 231 bars.
+WARMUP = 252
+
+
+def _pct_change(p: pd.DataFrame, periods: int = 1) -> pd.DataFrame:
+    return p.ffill().pct_change(periods)
+
+
+def _rank(df: pd.DataFrame) -> pd.DataFrame:
+    return df.rank(axis=1, pct=True, na_option="keep")
+
+
+def _mom_12_1(p: pd.DataFrame) -> pd.DataFrame:
+    return _pct_change(p.shift(21), 231)
+
+
+def _gap_up_freq(p: pd.DataFrame) -> pd.DataFrame:
+    ret = _pct_change(p)
+    return (ret > 0.01).astype(float).rolling(60, min_periods=40).mean()
+
+
+def _members(df: pd.DataFrame, member: pd.DataFrame | None) -> pd.DataFrame:
+    return df if member is None else df.where(member)
+
+
+def _up_capture(p: pd.DataFrame, member: pd.DataFrame | None = None) -> pd.DataFrame:
+    ret = _pct_change(p)
+    mkt = _members(ret, member).mean(axis=1)
+    up_mkt = mkt > 0
+    arr = ret.values.copy()
+    arr[~up_mkt.values, :] = np.nan
+    stock_up = pd.DataFrame(arr, index=ret.index, columns=ret.columns)
+    mkt_up_vals = mkt.where(up_mkt, np.nan)
+    stock_avg = stock_up.rolling(60, min_periods=20).mean()
+    mkt_avg = mkt_up_vals.rolling(60, min_periods=20).mean()
+    return stock_avg.div(mkt_avg, axis=0)
+
+
+def _mom_x_gap(p: pd.DataFrame, member: pd.DataFrame | None = None) -> pd.DataFrame:
+    return _rank(_members(_mom_12_1(p), member)) * _rank(_members(_gap_up_freq(p), member))
+
+
+def signal_up_cap_mom_gap(p: pd.DataFrame, member: pd.DataFrame | None = None) -> pd.DataFrame:
+    """`member` (dates x symbols) limits the cross-section (ranks, market return) to each
+    day's index members; factors still use each name's full price history. Live, every
+    column is a current member."""
+    return 0.5 * _rank(_members(_up_capture(p, member), member)) + 0.5 * _rank(_mom_x_gap(p, member))
+
+
+def up_cap_mom_gap_weights(opens: pd.DataFrame, top_n: int) -> dict[str, float]:
+    """Equal-weight top_n names for trading at the close of the last row's session.
+
+    `opens` holds opening prices (dates x symbols) up to and including today's open,
+    matching the source's live timing: signal from opens, fills at the same day's close.
+    """
+    if len(opens) <= WARMUP:
+        raise ValueError(f"need more than {WARMUP} sessions of opens, have {len(opens)}")
+    return select_top_n(signal_up_cap_mom_gap(opens).iloc[-1], top_n)
+
+
+def select_top_n(sig: pd.Series, top_n: int) -> dict[str, float]:
+    if sig.notna().sum() < top_n:
+        return {}
+    rank = sig.rank(ascending=False, na_option="bottom")
+    winners = rank.index[rank <= top_n]
+    return {symbol: 1.0 / len(winners) for symbol in winners}