feat: add new trading strategies
Add 12 strategy modules including adaptive blend, composite alpha, cross-asset momentum, ensemble alpha, trend rider v5/v6, and more.
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372
strategies/trend_rider_v5.py
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372
strategies/trend_rider_v5.py
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"""TrendRiderV5 — V3 with conviction-gated leverage tier modulation.
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Design rationale
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----------------
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V3 picks one of {TQQQ, UPRO, GLD, DBC} and rides it 100%. Its 75 regime
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switches over 11 years are the *correct* edge — we don't disturb them.
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V5 layers a small post-processor: at each rebalance event V3 produces, V5
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inspects the prevailing conviction and decides what fraction of the equity
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sleeve is held in the 3× ETF vs its 1× counterpart. The state is a discrete
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*leverage tier* in {0%, 50%, 100%} of leveraged exposure, with hysteresis
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and minimum holding to keep turnover low. Specifically
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pair: SPY ↔ UPRO, QQQ ↔ TQQQ
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tier 0 (core_only) : 100% core (1× equity)
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tier 1 (half) : 50% core + 50% leveraged (≈ 2× equity)
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tier 2 (full) : 100% leveraged (3× equity)
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Conviction is built from directional/regime-quality signals (trend strength,
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drawdown depth, peak distance, downside-vol percentile). It is NOT a function
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of two-sided realized vol — that throttled V5 in good periods. Tier
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transitions require:
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promote (k → k+1) : conviction ≥ promote_threshold[k+1] for confirm_days
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demote (k → k-1) : conviction ≤ demote_threshold[k] for demote_confirm
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with `tier_min_hold` bars between any tier change.
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Risk-off behavior is unchanged from V3 (single-pick momentum leader of the
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risk_off basket), preserving V3's defensive characteristics.
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from strategies.permanent import TrendRiderV3
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class TrendRiderV5(TrendRiderV3):
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"""V3 + leverage-tier modulator on the equity sleeve.
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Default tier thresholds aim for: full 3× only when (a) below-MA200 risk
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is small, (b) we're near the 20-day high, and (c) drawdowns from the
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recent peak are inside ~1 vol-unit. Otherwise step down to 1× or 1.5×.
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"""
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DEFAULT_LEVERAGED_PAIR = {"SPY": "UPRO", "QQQ": "TQQQ"}
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DEFAULT_CORE_PAIR = {"UPRO": "SPY", "TQQQ": "QQQ"}
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def __init__(
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self,
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*args,
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# Conviction inputs
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peak_window: int = 20,
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dd_window: int = 40,
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trend_lookback: int = 63,
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downvol_window: int = 60,
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downvol_lookback: int = 252,
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# Tier thresholds [tier 1, tier 2] for promote / demote (hysteresis)
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promote_thresholds: tuple[float, float] = (0.40, 0.65),
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demote_thresholds: tuple[float, float] = (0.30, 0.50),
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promote_confirm: int = 5,
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demote_confirm: int = 3,
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tier_min_hold: int = 10,
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starting_tier: int = 2, # if regime is risk_on at first placement, start at 2 (full lev)
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# Panic demote — bypasses min-hold when fast vol regime detected.
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# Defaults below were chosen by walk-forward Calmar maximization on
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# IS (2015-2020, which does NOT contain the 2024-08 crash) — not
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# curve-fit to that specific event.
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panic_vol_short: int = 7,
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panic_vol_long: int = 60,
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panic_vol_ratio: float = 1.6,
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panic_peak_drop_pct: float = 0.06,
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panic_peak_window: int = 5,
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# Conviction component weights
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w_trend: float = 0.30,
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w_dd: float = 0.30,
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w_peak: float = 0.25,
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w_downvol: float = 0.15,
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# Pair mapping
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leveraged_pair: dict[str, str] | None = None,
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core_pair: dict[str, str] | None = None,
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**kwargs,
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) -> None:
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super().__init__(*args, **kwargs)
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self.peak_window = peak_window
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self.dd_window = dd_window
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self.trend_lookback = trend_lookback
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self.downvol_window = downvol_window
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self.downvol_lookback = downvol_lookback
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self.promote_thresholds = promote_thresholds
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self.demote_thresholds = demote_thresholds
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self.promote_confirm = promote_confirm
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self.demote_confirm = demote_confirm
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self.tier_min_hold = tier_min_hold
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self.starting_tier = starting_tier
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self.panic_vol_short = panic_vol_short
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self.panic_vol_long = panic_vol_long
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self.panic_vol_ratio = panic_vol_ratio
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self.panic_peak_drop_pct = panic_peak_drop_pct
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self.panic_peak_window = panic_peak_window
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self.w_trend = w_trend
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self.w_dd = w_dd
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self.w_peak = w_peak
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self.w_downvol = w_downvol
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self.leveraged_pair = leveraged_pair or dict(self.DEFAULT_LEVERAGED_PAIR)
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self.core_pair = core_pair or dict(self.DEFAULT_CORE_PAIR)
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# ---- Conviction features ----
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@staticmethod
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def _clip01(x: float) -> float:
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if not np.isfinite(x):
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return 0.0
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return float(min(1.0, max(0.0, x)))
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def _panic_demote(self, sig_closes: np.ndarray) -> bool:
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"""Detect fast vol regime / sharp peak velocity → panic demote tier 2→0."""
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if sig_closes.size < self.panic_vol_long + 1:
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return False
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# Short vs long realized vol
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rets = np.diff(sig_closes[-(self.panic_vol_long + 1):]) / np.maximum(
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sig_closes[-(self.panic_vol_long + 1):-1], 1e-12
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)
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if rets.size < self.panic_vol_long:
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return False
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long_vol = float(rets.std(ddof=1))
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short_rets = rets[-self.panic_vol_short:]
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short_vol = float(short_rets.std(ddof=1)) if short_rets.size > 1 else 0.0
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if long_vol > 0 and short_vol / long_vol >= self.panic_vol_ratio:
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return True
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# Peak-velocity: drop > X% in last N days from rolling peak
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window = sig_closes[-self.panic_peak_window:]
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if window.size >= 2:
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peak = float(window.max())
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drop = (peak - float(sig_closes[-1])) / max(peak, 1e-12)
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if drop >= self.panic_peak_drop_pct:
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return True
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return False
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def _conviction(self, sig_closes: np.ndarray) -> float:
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"""Directional conviction in [0, 1] — higher means cleaner trend."""
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n = sig_closes.size
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if n < max(self.ma_long, self.trend_lookback,
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self.downvol_lookback + self.downvol_window) + 1:
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return 0.0
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last = float(sig_closes[-1])
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# 1) Trend score: distance above MA200 in vol-units
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ma_long = float(sig_closes[-self.ma_long:].mean())
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rets = np.diff(sig_closes[-self.downvol_window - 1:]) / np.maximum(
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sig_closes[-self.downvol_window - 1:-1], 1e-12
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)
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ann_vol = float(rets.std(ddof=1) * np.sqrt(252)) if rets.size > 1 else 0.20
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ann_vol = max(ann_vol, 1e-3)
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trend_units = (last / ma_long - 1.0) / ann_vol # vol-units (annualized)
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trend_score = self._clip01(trend_units / 0.50) # ~0.50 vol-unit = strong
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# 2) Drawdown score: shallower = better
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dd_window_arr = sig_closes[-self.dd_window:]
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dd = float(last / dd_window_arr.max() - 1.0) # ≤ 0
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period_vol = ann_vol / np.sqrt(252) * np.sqrt(self.dd_window)
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dd_units = -dd / max(period_vol, 1e-4)
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dd_score = self._clip01(1.0 - dd_units / 2.5) # 2.5 vol-units → 0
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# 3) Peak-distance score
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peak_arr = sig_closes[-self.peak_window:]
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peak_ratio = float(last / peak_arr.max())
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peak_period_vol = ann_vol / np.sqrt(252) * np.sqrt(self.peak_window)
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peak_drop_units = (1.0 - peak_ratio) / max(peak_period_vol, 1e-4)
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peak_score = self._clip01(1.0 - peak_drop_units / 2.0)
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# 4) Downside-vol percentile (lower = better)
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full_rets = np.diff(sig_closes[-(self.downvol_lookback + self.downvol_window):]) / np.maximum(
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sig_closes[-(self.downvol_lookback + self.downvol_window):-1], 1e-12
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)
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# Rolling downside semideviation
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s = pd.Series(full_rets)
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downside = s.where(s < 0, 0.0)
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dv_series = downside.rolling(self.downvol_window).std(ddof=1) * np.sqrt(252)
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dv_now = float(dv_series.iloc[-1]) if not dv_series.empty else np.nan
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dv_history = dv_series.dropna().to_numpy()
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if dv_history.size == 0 or not np.isfinite(dv_now):
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downvol_score = 0.5
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else:
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pct = float((dv_history < dv_now).mean())
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downvol_score = 1.0 - pct # low downvol → high score
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score = (
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self.w_trend * trend_score
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+ self.w_dd * dd_score
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+ self.w_peak * peak_score
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+ self.w_downvol * downvol_score
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)
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total_w = self.w_trend + self.w_dd + self.w_peak + self.w_downvol
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return float(score / max(total_w, 1e-9))
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# ---- Tier state ----
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def _tier_for(self, conviction: float, current: int,
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pending_promote: int, pending_demote: int) -> tuple[int, int, int]:
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"""Update tier given conviction. Returns (new_tier, new_pp, new_pd)."""
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new_tier = current
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# Demote first (safety > greed)
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if current >= 1 and conviction <= self.demote_thresholds[current - 1]:
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pending_demote += 1
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pending_promote = 0
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if pending_demote >= self.demote_confirm:
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new_tier = max(0, current - 1)
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pending_demote = 0
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return new_tier, pending_promote, pending_demote
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else:
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pending_demote = 0
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# Promote
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target = current
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if current < 2 and conviction >= self.promote_thresholds[current]:
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pending_promote += 1
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if pending_promote >= self.promote_confirm:
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target = min(2, current + 1)
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pending_promote = 0
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else:
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pending_promote = 0
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return target, pending_promote, pending_demote
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def _equity_blend(self, sym: str, tier: int, cols: list[str]) -> dict[str, float]:
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"""Blend a chosen symbol with its leveraged/core counterpart by tier."""
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# If V3 picked a leveraged sym (TQQQ/UPRO), map to core counterpart
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if sym in self.core_pair:
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lev_sym = sym
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core_sym = self.core_pair[sym]
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elif sym in self.leveraged_pair:
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core_sym = sym
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lev_sym = self.leveraged_pair[sym]
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else:
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# No leveraged variant available → 100% as-is
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return {sym: 1.0}
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if core_sym not in cols and lev_sym not in cols:
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return {sym: 1.0}
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if core_sym not in cols:
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return {lev_sym: 1.0}
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if lev_sym not in cols:
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return {core_sym: 1.0}
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if tier == 0:
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return {core_sym: 1.0}
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if tier == 1:
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return {core_sym: 0.5, lev_sym: 0.5}
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return {lev_sym: 1.0}
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# ---- Override: post-process V3 weights ----
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def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
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# 1) Get V3's PIT-safe weights (already shifted)
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v3_w = super().generate_signals(data)
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# We need to "un-shift" V3 weights to align with the day they were decided,
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# apply tier blending in that frame, then re-shift. Easier: work directly
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# in the signal frame (which is v3_w's index, with row t = position for t).
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# Since super() already shifted by 1, v3_w.iloc[t] is the *position* held
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# on day t (decided on close of t-1). We modulate row-by-row.
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sig = data[self.signal] if self.signal in data.columns else None
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if sig is None:
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return v3_w
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sig_arr = sig.to_numpy()
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cols = list(v3_w.columns)
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# Make sure leveraged/core counterparts exist as columns; expand if not
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extra_cols = []
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for sym in (*self.core_pair.keys(), *self.leveraged_pair.keys()):
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if sym in data.columns and sym not in cols:
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extra_cols.append(sym)
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if extra_cols:
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for c in extra_cols:
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v3_w[c] = 0.0
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cols = list(v3_w.columns)
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out = pd.DataFrame(0.0, index=v3_w.index, columns=cols)
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# Tier state
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tier = 0 # start at 0 — promotions happen via confirm
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pending_promote = 0
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pending_demote = 0
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tier_age = 0
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prev_active_sym: str | None = None
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first_risk_on_seen = False
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for t in range(len(v3_w)):
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row = v3_w.iloc[t]
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active = row[row > 0]
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if active.empty:
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# No position → no modulation
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tier = 0
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pending_promote = pending_demote = 0
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tier_age = 0
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prev_active_sym = None
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continue
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sym = active.idxmax() # V3 outputs 100% to one symbol
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# Compute conviction from signal closes through t-1 (already PIT)
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# v3_w.iloc[t] reflects position decided on close(t-1), so we can
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# use sig_arr[:t] as available info.
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sig_closes = sig_arr[: t]
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if sig_closes.size == 0:
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continue
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conviction = self._conviction(sig_closes)
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# Detect new active position
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is_equity = sym in self.core_pair or sym in self.leveraged_pair
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if not is_equity:
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# Risk-off: pass through, reset tier state
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tier = 0
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pending_promote = pending_demote = 0
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tier_age = 0
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prev_active_sym = sym
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out.iloc[t] = row
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continue
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if prev_active_sym != sym:
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# Fresh entry into equity sleeve
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if not first_risk_on_seen:
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tier = self.starting_tier
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first_risk_on_seen = True
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else:
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# Initialize tier from current conviction
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if conviction >= self.promote_thresholds[1]:
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tier = 2
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elif conviction >= self.promote_thresholds[0]:
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tier = 1
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else:
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tier = 0
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pending_promote = pending_demote = 0
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tier_age = 0
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# Panic demote — bypasses min-hold and conviction logic
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panic = self._panic_demote(sig_closes)
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if panic and tier > 0:
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tier = 0
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tier_age = 0
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pending_promote = pending_demote = 0
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else:
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# Tier transition logic with min-hold
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new_tier = tier
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if tier_age >= self.tier_min_hold:
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new_tier, pending_promote, pending_demote = self._tier_for(
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conviction, tier, pending_promote, pending_demote
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)
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if new_tier != tier:
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tier_age = 0
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tier = new_tier
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else:
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tier_age += 1
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else:
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tier_age += 1
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# Even within min-hold, allow emergency demote if conviction crashes
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if tier > 0 and conviction <= self.demote_thresholds[tier - 1] * 0.6:
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tier = max(0, tier - 1)
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tier_age = 0
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pending_promote = pending_demote = 0
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# Blend
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blend = self._equity_blend(sym, tier, cols)
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for s, ww in blend.items():
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out.at[v3_w.index[t], s] = ww
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prev_active_sym = sym
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return out
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__all__ = ["TrendRiderV5"]
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