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STRATA-LOOP: Closed-Loop Weight Adaptation
-------------------------------------------
Receives trade outcomes (P&L signals) and adapts DEFAULT_WEIGHTS
incrementally using a gradient-free, sign-based update rule.
Design principles:
- No backprop. No neural net. Fully auditable.
- Each weight nudged ±STEP based on which direction reduced error.
- Adaptation rate decays over time (cooling schedule).
- Weights stay within [MIN, MAX] bounds — never drift to degenerate values.
- All updates logged with reason for transparency.
Integration:
loop = StrataLOOP()
# After each closed trade:
loop.record(outcome) # outcome: dict from StrataOUTCOME
# Periodically:
new_weights = loop.weights # inject into update_state(weights=...)
Outcome dict schema:
{
"action": "LONG" | "SHORT" | "HOLD",
"pnl": float, # signed P&L in price units
"pnl_pct": float, # P&L as % of entry price
"regime": str, # regime label at entry
"confidence": float, # confidence at entry
"state_snap": dict, # full state at entry
"sense_snap": dict, # sense signals at entry
}
"""
import math
import copy
from typing import Dict, List, Optional
from .core import DEFAULT_WEIGHTS, WEIGHTS_V1
# ---------------------------------------------------------------------------
# Adaptation config
# ---------------------------------------------------------------------------
LOOP_CONFIG = {
"step": 0.005, # base nudge per update
"min_step": 0.001, # minimum step after cooling
"cooling": 0.995, # step multiplies by this each update
"window": 50, # rolling window for outcome averaging
"update_every": 10, # adapt weights every N outcomes
"max_adapt": 0.30, # max allowed deviation from V2 seed per weight
"min_weight": 0.01, # absolute minimum for any weight
}
# Which weights are adaptable and in which direction signal improvement
# "+" = higher value → better performance, "-" = lower value → better
ADAPTABLE_WEIGHTS = {
"w_trend_bias": "+",
"w_vol_uncertainty": "+",
"w_break_momentum": "+",
"w_fake_break_bias": "+",
"w_trend_str_bias": "+",
"alpha": None, # sum alpha+beta+gamma kept ~1.0, handled specially
"beta": None,
"gamma": None,
"c_bias": "+",
"c_momentum": "+",
"c_trap": "-", # higher trap penalty = more conservative
"decay_bias": None, # decay not adapted (structural, not performance-driven)
"decay_momentum": None,
"decay_trap_risk": None,
"decay_uncertainty": None,
"regime_momentum_mix": None,
}
class StrataLOOP:
"""
Closed-loop weight adaptation engine.
Observes trade outcomes and nudges weights toward configurations
that historically produced better P&L in the current regime.
"""
def __init__(self, seed_weights: Optional[Dict] = None):
self.weights = copy.deepcopy(seed_weights or DEFAULT_WEIGHTS)
self._seed = copy.deepcopy(self.weights)
self._step = LOOP_CONFIG["step"]
self._outcomes: List[Dict] = []
self._update_count = 0
self._log: List[Dict] = []
# -----------------------------------------------------------------------
# Public API
# -----------------------------------------------------------------------
def record(self, outcome: Dict) -> None:
"""Record a trade outcome. Triggers adaptation every N outcomes."""
self._outcomes.append(outcome)
window = LOOP_CONFIG["window"]
if len(self._outcomes) > window:
self._outcomes = self._outcomes[-window:]
if len(self._outcomes) % LOOP_CONFIG["update_every"] == 0:
self._adapt()
def snapshot(self) -> Dict:
"""Return current weights for injection into update_state."""
return copy.deepcopy(self.weights)
def reset(self) -> None:
"""Reset weights back to seed (V2 defaults)."""
self.weights = copy.deepcopy(self._seed)
self._outcomes.clear()
self._step = LOOP_CONFIG["step"]
self._update_count = 0
def summary(self) -> Dict:
"""Return adaptation statistics."""
diffs = {
k: round(self.weights[k] - self._seed[k], 5)
for k in self.weights
}
recent_pnl = [o["pnl_pct"] for o in self._outcomes if "pnl_pct" in o]
return {
"update_count": self._update_count,
"current_step": round(self._step, 6),
"outcomes_seen": len(self._outcomes),
"recent_win_rate": self._win_rate(self._outcomes),
"weight_deltas": diffs,
"log_tail": self._log[-5:],
}
# -----------------------------------------------------------------------
# Internal adaptation logic
# -----------------------------------------------------------------------
def _adapt(self) -> None:
"""Core adaptation step — sign-based gradient-free update."""
outcomes = self._outcomes
if len(outcomes) < LOOP_CONFIG["update_every"]:
return
recent = outcomes[-LOOP_CONFIG["update_every"]:]
prior = outcomes[:-LOOP_CONFIG["update_every"]] or outcomes
recent_wr = self._win_rate(recent)
prior_wr = self._win_rate(prior)
improving = recent_wr >= prior_wr
# Regime breakdown of recent outcomes
regime_pnl = {}
for o in recent:
r = o.get("regime", "TRANSITIONING")
regime_pnl.setdefault(r, []).append(o.get("pnl_pct", 0.0))
# Adapt each eligible weight
for key, direction in ADAPTABLE_WEIGHTS.items():
if direction is None or key not in self.weights:
continue
self._nudge_weight(key, direction, improving, recent, prior_wr)
# Keep alpha+beta+gamma summing to ~1.0 (renormalise)
self._normalise_conflict_weights()
# Cooling — step size shrinks over time
self._step = max(LOOP_CONFIG["min_step"], self._step * LOOP_CONFIG["cooling"])
self._update_count += 1
self._log.append({
"update": self._update_count,
"recent_wr": round(recent_wr, 4),
"prior_wr": round(prior_wr, 4),
"improving": improving,
"step": round(self._step, 6),
})
def _nudge_weight(
self,
key: str,
direction: str,
improving: bool,
recent: List[Dict],
prior_wr: float,
) -> None:
"""Nudge a single weight based on performance signal."""
current = self.weights[key]
seed = self._seed[key]
max_dev = LOOP_CONFIG["max_adapt"]
# Compute per-weight relevance: how strongly did this weight
# correlate with winning trades in the recent window?
win_signal = self._weight_win_correlation(key, recent)
# Direction: "+" means we want more of this weight when winning
if direction == "+":
nudge = +self._step if win_signal > 0 else -self._step
else: # "-"
nudge = -self._step if win_signal > 0 else +self._step
# If overall performance is degrading, reverse the nudge
if not improving:
nudge = -nudge
new_val = current + nudge
# Bound: stay within max_adapt of seed, never go below min_weight
new_val = max(LOOP_CONFIG["min_weight"], new_val)
new_val = max(seed - max_dev, min(seed + max_dev, new_val))
self.weights[key] = round(new_val, 6)
def _normalise_conflict_weights(self) -> None:
"""Keep alpha + beta + gamma = 1.0 after adaptation."""
a = self.weights.get("alpha", 0.30)
b = self.weights.get("beta", 0.35)
g = self.weights.get("gamma", 0.35)
total = a + b + g
if abs(total - 1.0) > 0.001 and total > 0:
self.weights["alpha"] = round(a / total, 6)
self.weights["beta"] = round(b / total, 6)
self.weights["gamma"] = round(g / total, 6)
def _weight_win_correlation(self, key: str, outcomes: List[Dict]) -> float:
"""
Heuristic: compare average relevant state/sense value in winning
vs losing trades. Returns sign of correlation.
Positive → weight should be increased when winning.
"""
# Map weight key to the state/sense field it most influences
weight_to_field = {
"w_trend_bias": ("sense_snap", "trend"),
"w_vol_uncertainty": ("sense_snap", "vol"),
"w_break_momentum": ("sense_snap", "break_structure"),
"w_fake_break_bias": ("sense_snap", "break_structure"),
"w_trend_str_bias": ("sense_snap", "trend"),
"c_bias": ("state_snap", "bias"),
"c_momentum": ("state_snap", "momentum"),
"c_trap": ("state_snap", "trap_risk"),
}
if key not in weight_to_field:
return 0.0
snap_type, field = weight_to_field[key]
wins = [o for o in outcomes if o.get("pnl_pct", 0) > 0]
loses = [o for o in outcomes if o.get("pnl_pct", 0) <= 0]
def avg_field(group):
vals = [o.get(snap_type, {}).get(field, 0.0) for o in group]
return sum(vals) / len(vals) if vals else 0.0
if not wins or not loses:
return 0.0
return avg_field(wins) - avg_field(loses)
@staticmethod
def _win_rate(outcomes: List[Dict]) -> float:
if not outcomes:
return 0.0
wins = sum(1 for o in outcomes if o.get("pnl_pct", 0) > 0)
return wins / len(outcomes)
def __repr__(self):
s = self.summary()
return (
f"STRATA-LOOP | updates={s['update_count']}"
f" | recent_wr={s['recent_win_rate']:.2%}"
f" | step={s['current_step']:.5f}"
f" | outcomes={s['outcomes_seen']}"
)
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