Add price signals and cross-symbol signal support

optopsy/signals/__init__.py

@@ -50,6 +50,7 @@
     _get_close,
     _get_high,
     _get_low,
+    _get_open,
     _get_volume,
     _groupby_symbol,
     _ohlcv_crossover_signal,
@@ -152,6 +153,24 @@
     zlma_cross_below,
 )
 
+# --- Price signals ---
+from .price import (
+    consecutive_down,
+    consecutive_up,
+    daily_return_above,
+    daily_return_below,
+    drawdown_from_high,
+    gap_down,
+    gap_up,
+    high_of_n_days,
+    low_of_n_days,
+    price_above,
+    price_below,
+    price_cross_above,
+    price_cross_below,
+    rally_from_low,
+)
+
 # --- Trend signals ---
 from .trend import (
     # ADX
@@ -224,6 +243,21 @@
     "Signal",
     "signal",
     "signal_dates",
+    # Price
+    "price_above",
+    "price_below",
+    "price_cross_above",
+    "price_cross_below",
+    "gap_up",
+    "gap_down",
+    "high_of_n_days",
+    "low_of_n_days",
+    "daily_return_above",
+    "daily_return_below",
+    "drawdown_from_high",
+    "rally_from_low",
+    "consecutive_up",
+    "consecutive_down",
     # Momentum
     "rsi_below",
     "rsi_above",

optopsy/signals/_helpers.py

@@ -290,6 +290,13 @@ def _get_hl(group: pd.DataFrame) -> "tuple[pd.Series | None, pd.Series | None] |
     return _get_high(group), _get_low(group)
 
 
+def _get_open(group: pd.DataFrame) -> "pd.Series | None":
+    """Get open prices if available, else None."""
+    if "open" in group.columns:
+        return group["open"]
+    return None
+
+
 def _get_volume(group: pd.DataFrame) -> "pd.Series | None":
     """Get volume if available, else None."""
     if "volume" in group.columns:

optopsy/signals/price.py

@@ -0,0 +1,297 @@
+"""Price signals: levels, crossovers, gaps, breakouts, returns, drawdowns."""
+
+import pandas as pd
+
+from ._helpers import (
+    SignalFunc,
+    _crossover_signal,
+    _get_close,
+    _get_high,
+    _get_low,
+    _get_open,
+    _groupby_symbol,
+    _per_symbol_signal,
+)
+
+# ---------------------------------------------------------------------------
+# State-based: price above/below a fixed level
+# ---------------------------------------------------------------------------
+
+
+def price_above(level: float) -> SignalFunc:
+    """True every bar where the close price is above *level*."""
+    level = float(level)
+    return _per_symbol_signal(
+        lambda p: pd.Series(level, index=p.index),
+        lambda prices, lvl: prices > lvl,
+    )
+
+
+def price_below(level: float) -> SignalFunc:
+    """True every bar where the close price is below *level*."""
+    level = float(level)
+    return _per_symbol_signal(
+        lambda p: pd.Series(level, index=p.index),
+        lambda prices, lvl: prices < lvl,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Event-based: price crosses a fixed level
+# ---------------------------------------------------------------------------
+
+
+def price_cross_above(level: float) -> SignalFunc:
+    """True on the bar where close crosses above *level*."""
+    level = float(level)
+    return _crossover_signal(
+        lambda prices: (prices, pd.Series(level, index=prices.index)),
+        above=True,
+    )
+
+
+def price_cross_below(level: float) -> SignalFunc:
+    """True on the bar where close crosses below *level*."""
+    level = float(level)
+    return _crossover_signal(
+        lambda prices: (prices, pd.Series(level, index=prices.index)),
+        above=False,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Gap signals (need open + close)
+# ---------------------------------------------------------------------------
+
+
+def gap_up(pct: float = 0.5) -> SignalFunc:
+    """True when today's open gaps above yesterday's close by at least *pct* %."""
+    pct = float(pct)
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            open_ = _get_open(group)
+            if close is None or open_ is None:
+                return pd.Series(False, index=group.index)
+            threshold = close.shift(1) * (1 + pct / 100)
+            return (open_ > threshold).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+def gap_down(pct: float = 0.5) -> SignalFunc:
+    """True when today's open gaps below yesterday's close by at least *pct* %."""
+    pct = float(pct)
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            open_ = _get_open(group)
+            if close is None or open_ is None:
+                return pd.Series(False, index=group.index)
+            threshold = close.shift(1) * (1 - pct / 100)
+            return (open_ < threshold).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+# ---------------------------------------------------------------------------
+# N-period high/low breakout signals
+# ---------------------------------------------------------------------------
+
+
+def high_of_n_days(period: int = 252) -> SignalFunc:
+    """True when close reaches or exceeds the N-bar rolling high (breakout).
+
+    The rolling window is shifted by 1 to avoid look-ahead bias — the
+    comparison is against the highest high of the *previous* N bars.
+    """
+    period = int(period)
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            high = _get_high(group)
+            if close is None or high is None:
+                return pd.Series(False, index=group.index)
+            rolling_high = high.rolling(period, min_periods=1).max().shift(1)
+            return (close >= rolling_high).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+def low_of_n_days(period: int = 252) -> SignalFunc:
+    """True when close reaches or falls below the N-bar rolling low (breakdown).
+
+    The rolling window is shifted by 1 to avoid look-ahead bias — the
+    comparison is against the lowest low of the *previous* N bars.
+    """
+    period = int(period)
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            low = _get_low(group)
+            if close is None or low is None:
+                return pd.Series(False, index=group.index)
+            rolling_low = low.rolling(period, min_periods=1).min().shift(1)
+            return (close <= rolling_low).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+# ---------------------------------------------------------------------------
+# Daily return signals
+# ---------------------------------------------------------------------------
+
+
+def daily_return_above(pct: float = 1.0) -> SignalFunc:
+    """True when the daily close-to-close return exceeds *pct* %.
+
+    Example: ``daily_return_above(2.0)`` fires on days the stock gains > 2%.
+    """
+    threshold = float(pct) / 100  # compare in decimal form
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            if close is None:
+                return pd.Series(False, index=group.index)
+            ret = close.pct_change()
+            return (ret > threshold).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+def daily_return_below(pct: float = -1.0) -> SignalFunc:
+    """True when the daily close-to-close return is below *pct* %.
+
+    Use negative values for drops: ``daily_return_below(-3.0)`` fires on
+    days the stock falls more than 3%.
+    """
+    threshold = float(pct) / 100  # compare in decimal form
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            if close is None:
+                return pd.Series(False, index=group.index)
+            ret = close.pct_change()
+            return (ret < threshold).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+# ---------------------------------------------------------------------------
+# Drawdown / rally signals
+# ---------------------------------------------------------------------------
+
+
+def drawdown_from_high(period: int = 20, pct: float = 5.0) -> SignalFunc:
+    """True when close is down at least *pct* % from its *period*-bar rolling high.
+
+    Measures how far the current close has fallen from the highest close
+    over the last *period* bars (inclusive of the current bar).
+    """
+    period = int(period)
+    threshold = float(pct) / 100  # convert to decimal
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            if close is None:
+                return pd.Series(False, index=group.index)
+            rolling_high = close.rolling(period, min_periods=1).max()
+            dd_ratio = (close - rolling_high) / rolling_high  # negative values
+            return (dd_ratio <= -threshold).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+def rally_from_low(period: int = 20, pct: float = 5.0) -> SignalFunc:
+    """True when close is up at least *pct* % from its *period*-bar rolling low.
+
+    Measures how far the current close has risen from the lowest close
+    over the last *period* bars (inclusive of the current bar).
+    """
+    period = int(period)
+    threshold = float(pct) / 100  # convert to decimal
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            if close is None:
+                return pd.Series(False, index=group.index)
+            rolling_low = close.rolling(period, min_periods=1).min()
+            rally_ratio = (close - rolling_low) / rolling_low
+            return (rally_ratio >= threshold).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+# ---------------------------------------------------------------------------
+# Consecutive up/down day signals
+# ---------------------------------------------------------------------------
+
+
+def consecutive_up(days: int = 3) -> SignalFunc:
+    """True on the bar completing *days* consecutive closes above prior close.
+
+    Example: ``consecutive_up(3)`` fires after 3 straight up-closes.
+    """
+    days = int(days)
+    if days < 1:
+        raise ValueError(f"days must be >= 1, got {days}")
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            if close is None:
+                return pd.Series(False, index=group.index)
+            up = (close > close.shift(1)).astype(int)
+            streak = up.rolling(days, min_periods=days).sum()
+            return (streak == days).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal
+
+
+def consecutive_down(days: int = 3) -> SignalFunc:
+    """True on the bar completing *days* consecutive closes below prior close.
+
+    Example: ``consecutive_down(3)`` fires after 3 straight down-closes.
+    """
+    days = int(days)
+    if days < 1:
+        raise ValueError(f"days must be >= 1, got {days}")
+
+    def _signal(data: pd.DataFrame) -> "pd.Series[bool]":
+        def _compute_group(group: pd.DataFrame) -> "pd.Series[bool]":
+            close = _get_close(group)
+            if close is None:
+                return pd.Series(False, index=group.index)
+            down = (close < close.shift(1)).astype(int)
+            streak = down.rolling(days, min_periods=days).sum()
+            return (streak == days).fillna(False)
+
+        return _groupby_symbol(data, _compute_group)
+
+    return _signal