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Strategy Execution Engine.
Interprets structured strategy configurations, applies entry/exit rules,
position sizing, and generates trade signals for the backtester.
Strategy configs are JSON-serialized dicts with:
- universe: target tickers
- entry_rules: conditions to open positions
- exit_rules: conditions to close positions
- signals: which signal types to use
- position_sizing: method and constraints
- rebalance_frequency: how often to rebalance
- constraints: sector limits, position limits
- risk_management: stop loss, take profit
"""
from __future__ import annotations
import json
import logging
from datetime import date
from typing import Any, Dict, List, Optional
import numpy as np
import pandas as pd
from app.services.data_ingestion.yahoo import yahoo_adapter
from app.services.feature_engineering.pipeline import feature_pipeline
from app.services.signals.engine import signal_engine
logger = logging.getLogger(__name__)
class StrategyEngine:
"""Execute strategies against historical data to produce position targets."""
async def evaluate_strategy(
self,
config: Dict[str, Any],
as_of_date: Optional[date] = None,
period: str = "1y",
) -> Dict[str, Any]:
"""
Evaluate a strategy config and produce position recommendations.
Returns:
Dict with target_positions, signals_used, and evaluation metadata.
"""
universe = config.get("universe", [])
if not universe:
return {"target_positions": {}, "signals": [], "error": "Empty universe"}
signal_types = config.get("signals", ["momentum", "mean_reversion", "volatility"])
entry_rules = config.get("entry_rules", [])
exit_rules = config.get("exit_rules", [])
position_sizing = config.get("position_sizing", {"method": "equal_weight"})
constraints = config.get("constraints", {})
risk_mgmt = config.get("risk_management", {})
# 1. Generate signals for universe
all_signals = await signal_engine.generate_signals(universe, signal_types, period)
# 2. Evaluate entry/exit rules for each ticker
position_targets: Dict[str, Dict[str, Any]] = {}
for ticker in universe:
ticker_signals = all_signals.get(ticker, [])
if not ticker_signals:
continue
# Compute composite score from signals
composite_score = self._compute_composite_score(ticker_signals)
# Apply entry rules
should_enter = self._evaluate_rules(entry_rules, composite_score, ticker_signals)
should_exit = self._evaluate_rules(exit_rules, composite_score, ticker_signals)
if should_enter and not should_exit:
direction = "long" if composite_score > 0 else "short"
position_targets[ticker] = {
"direction": direction,
"score": round(composite_score, 4),
"strength": round(min(abs(composite_score), 1.0), 4),
"signals_count": len(ticker_signals),
}
# 3. Apply position sizing
sized_positions = self._apply_position_sizing(
position_targets, position_sizing, constraints
)
return {
"target_positions": sized_positions,
"signals_used": {
ticker: sigs for ticker, sigs in all_signals.items()
if ticker in sized_positions
},
"universe": universe,
"evaluation_date": str(as_of_date or date.today()),
"total_signals": sum(len(s) for s in all_signals.values()),
}
def _compute_composite_score(self, signals: List[Dict[str, Any]]) -> float:
"""Compute weighted composite score from multiple signals."""
if not signals:
return 0.0
total_score = 0.0
total_weight = 0.0
for sig in signals:
value = sig.get("value", 0)
strength = sig.get("strength", 0.5)
direction = sig.get("direction", "neutral")
# Normalize to [-1, 1]
if direction == "long":
dir_multiplier = 1.0
elif direction == "short":
dir_multiplier = -1.0
else:
dir_multiplier = 0.0
score = dir_multiplier * strength
total_score += score
total_weight += 1.0
return total_score / total_weight if total_weight > 0 else 0.0
def _evaluate_rules(
self,
rules: List[Dict[str, Any]],
composite_score: float,
signals: List[Dict[str, Any]],
) -> bool:
"""Evaluate entry or exit rules against current signals."""
if not rules:
# Default: enter if composite score is significant
return abs(composite_score) > 0.2
for rule in rules:
rule_type = rule.get("type", "threshold")
if rule_type == "threshold":
threshold = rule.get("value", 0.2)
operator = rule.get("operator", "gt")
if operator == "gt" and composite_score > threshold:
return True
elif operator == "lt" and composite_score < -threshold:
return True
elif rule_type == "signal_count":
min_signals = rule.get("min", 2)
direction = rule.get("direction", "long")
count = sum(1 for s in signals if s.get("direction") == direction)
if count >= min_signals:
return True
elif rule_type == "signal_strength":
min_strength = rule.get("min_strength", 0.5)
strong_signals = [s for s in signals if s.get("strength", 0) >= min_strength]
if len(strong_signals) >= rule.get("min_count", 1):
return True
return False
def _apply_position_sizing(
self,
targets: Dict[str, Dict[str, Any]],
sizing_config: Dict[str, Any],
constraints: Dict[str, Any],
) -> Dict[str, Dict[str, Any]]:
"""Apply position sizing rules and constraints."""
if not targets:
return {}
method = sizing_config.get("method", "equal_weight")
max_position = sizing_config.get("max_position_pct", 0.1)
min_positions = constraints.get("min_positions", 1)
max_sector_exposure = constraints.get("max_sector_exposure", 0.3)
n_positions = max(len(targets), min_positions)
if method == "equal_weight":
weight = min(1.0 / n_positions, max_position)
for ticker in targets:
targets[ticker]["weight"] = round(weight, 4)
elif method == "score_weighted":
scores = {t: abs(d["score"]) for t, d in targets.items()}
total_score = sum(scores.values()) or 1.0
for ticker, data in targets.items():
raw_weight = scores[ticker] / total_score
targets[ticker]["weight"] = round(min(raw_weight, max_position), 4)
elif method == "risk_parity":
# Equal risk contribution (simplified)
weight = min(1.0 / n_positions, max_position)
for ticker in targets:
targets[ticker]["weight"] = round(weight, 4)
# Normalize weights to sum to <= 1.0
total_weight = sum(d.get("weight", 0) for d in targets.values())
if total_weight > 1.0:
for ticker in targets:
targets[ticker]["weight"] = round(
targets[ticker]["weight"] / total_weight, 4
)
return targets
@staticmethod
def convert_visual_graph_to_config(graph: Dict[str, Any]) -> Dict[str, Any]:
"""
Convert a visual strategy builder graph (nodes + edges) into
a structured strategy configuration JSON.
"""
nodes = graph.get("nodes", [])
edges = graph.get("edges", [])
config: Dict[str, Any] = {
"universe": [],
"entry_rules": [],
"exit_rules": [],
"signals": [],
"position_sizing": {"method": "equal_weight", "max_position_pct": 0.1},
"rebalance_frequency": "monthly",
"constraints": {},
"risk_management": {},
}
for node in nodes:
node_type = node.get("type", "")
node_config = node.get("config", {})
if node_type == "data":
tickers = node_config.get("tickers", [])
config["universe"].extend(tickers)
elif node_type == "indicator":
signal_name = node_config.get("signal_type", "momentum")
config["signals"].append(signal_name)
elif node_type == "factor":
config["signals"].append("factor")
elif node_type == "condition":
rule = {
"type": node_config.get("rule_type", "threshold"),
"value": node_config.get("threshold", 0.2),
"operator": node_config.get("operator", "gt"),
}
if node_config.get("is_exit"):
config["exit_rules"].append(rule)
else:
config["entry_rules"].append(rule)
elif node_type == "allocation":
config["position_sizing"] = {
"method": node_config.get("method", "equal_weight"),
"max_position_pct": node_config.get("max_position_pct", 0.1),
}
elif node_type == "risk":
config["risk_management"] = {
"stop_loss_pct": node_config.get("stop_loss_pct", 0.05),
"take_profit_pct": node_config.get("take_profit_pct", 0.15),
}
# Deduplicate
config["universe"] = list(set(config["universe"]))
config["signals"] = list(set(config["signals"]))
return config
strategy_engine = StrategyEngine()
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