5a6b20c8f1
I1: Add graceful shutdown (SIGTERM/SIGINT) to all 5 background services I2: Fix Dockerfile healthcheck to use curl on /metrics endpoint I3: Fix StreamConsumer.ensure_group() to only catch BUSYGROUP errors I4: Fix SimulatedBroker to reject orders with insufficient cash/shares I5: Move ORM attribute access inside DB session context in trades routes I6: Add Redis-based rate limiting (10 req/min/IP) on all auth endpoints I8: Prevent backtest background task garbage collection I9: Use Numeric(16,6) instead of Float for financial columns in migration I10: Add index on trades.created_at for time-range queries I11: Bind infrastructure ports to 127.0.0.1 in docker-compose I12: Add migrations init service; all app services depend on it I13: Fix user enumeration in login_begin (return options for non-existent users)
317 lines
10 KiB
Python
317 lines
10 KiB
Python
"""Learning Engine service -- main entry point.
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Consumes ``trades:executed`` from Redis Streams, evaluates closed positions,
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attributes credit to contributing strategies, adjusts strategy weights via
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a multi-armed bandit approach, and stores all adjustments for auditability.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import signal
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from datetime import datetime, timezone
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from uuid import UUID
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from redis.asyncio import Redis
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from services.learning_engine.config import LearningEngineConfig
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from services.learning_engine.evaluator import TradeEvaluator
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from services.learning_engine.weight_adjuster import WeightAdjuster
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from shared.redis_streams import StreamConsumer
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from shared.schemas.learning import TradeOutcomeSchema, WeightAdjustment
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from shared.schemas.trading import OrderSide, TradeExecution
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from shared.telemetry import setup_telemetry
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logger = logging.getLogger(__name__)
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# Redis key for cached strategy weights
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_STRATEGY_WEIGHTS_KEY = "strategy:weights"
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async def _load_strategy_weights(redis: Redis) -> dict[str, float]:
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"""Load current strategy weights from Redis cache.
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Falls back to equal weights for the three default strategies
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if no cached weights exist.
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"""
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raw = await redis.get(_STRATEGY_WEIGHTS_KEY)
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if raw:
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return json.loads(raw)
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# Default equal weights
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return {
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"momentum": 0.333,
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"mean_reversion": 0.333,
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"news_driven": 0.334,
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}
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async def _save_strategy_weights(redis: Redis, weights: dict[str, float]) -> None:
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"""Persist strategy weights to Redis cache."""
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await redis.set(_STRATEGY_WEIGHTS_KEY, json.dumps(weights))
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async def _find_opening_trade(
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redis: Redis,
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ticker: str,
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closing_side: OrderSide,
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) -> dict | None:
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"""Look up the opening trade for a position close.
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Searches the ``positions:history`` Redis hash for the ticker.
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Returns the stored entry data or None if not found.
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"""
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raw = await redis.hget("positions:history", ticker)
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if raw:
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return json.loads(raw)
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return None
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async def _store_opening_trade(
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redis: Redis,
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ticker: str,
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trade_data: dict,
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) -> None:
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"""Store a trade as the opening trade for a ticker."""
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await redis.hset("positions:history", ticker, json.dumps(trade_data))
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async def _clear_opening_trade(redis: Redis, ticker: str) -> None:
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"""Clear the stored opening trade after position close."""
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await redis.hdel("positions:history", ticker)
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def _is_position_close(trade: TradeExecution, opening: dict | None) -> bool:
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"""Determine if a trade closes a position.
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A trade closes a position if there is an existing opening trade
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on the opposite side for the same ticker.
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"""
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if opening is None:
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return False
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opening_side = opening.get("side", "")
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if trade.side == OrderSide.SELL and opening_side == OrderSide.BUY.value:
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return True
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if trade.side == OrderSide.BUY and opening_side == OrderSide.SELL.value:
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return True
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return False
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async def process_trade(
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trade: TradeExecution,
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redis: Redis,
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evaluator: TradeEvaluator,
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adjuster: WeightAdjuster,
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counters: dict,
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) -> list[WeightAdjustment]:
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"""Process a single trade execution.
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If the trade closes a position:
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1. Evaluate the trade outcome (P&L, ROI)
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2. Attribute credit to contributing strategies
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3. Adjust weights for strategies with enough trades
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4. Normalize all weights
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5. Store adjustments and update cached weights
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Returns a list of weight adjustments made (empty if none).
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"""
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adjustments: list[WeightAdjustment] = []
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# Look up opening trade
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opening = await _find_opening_trade(redis, trade.ticker, trade.side)
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if not _is_position_close(trade, opening):
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# This is an opening trade -- store it for later reference
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await _store_opening_trade(
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redis,
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trade.ticker,
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{
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"trade_id": str(trade.trade_id),
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"side": trade.side.value,
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"price": trade.price,
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"qty": trade.qty,
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"timestamp": trade.timestamp.isoformat(),
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"strategy_sources": [], # would come from signal
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},
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)
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return adjustments
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# --- Position close detected ---
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entry_price = opening["price"]
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entry_qty = opening.get("qty", trade.qty)
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entry_time_str = opening.get("timestamp", "")
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strategy_sources = opening.get("strategy_sources", [])
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# Determine direction sign
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opening_side = opening.get("side", "")
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direction_sign = 1.0 if opening_side == OrderSide.BUY.value else -1.0
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# Compute hold duration
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hold_duration_seconds = 0.0
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if entry_time_str:
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try:
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entry_time = datetime.fromisoformat(entry_time_str)
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hold_duration_seconds = (trade.timestamp - entry_time).total_seconds()
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except (ValueError, TypeError):
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hold_duration_seconds = 0.0
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# Step 1: Evaluate trade
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outcome = evaluator.evaluate_trade(
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trade_id=trade.trade_id,
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entry_price=entry_price,
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exit_price=trade.price,
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qty=min(trade.qty, entry_qty),
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direction_sign=direction_sign,
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hold_duration_seconds=max(hold_duration_seconds, 0.0),
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)
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logger.info(
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"Trade outcome: %s PnL=%.2f ROI=%.2f%% profitable=%s",
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trade.ticker,
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outcome.realized_pnl,
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outcome.roi_pct,
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outcome.was_profitable,
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)
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# Step 2: Attribute credit
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rewards = evaluator.attribute_credit(outcome, strategy_sources)
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# Record trades and rewards for each strategy
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for strategy_name, reward in rewards.items():
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adjuster.record_trade(strategy_name)
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adjuster.record_reward(strategy_name, reward)
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# Step 3: Load current weights
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weights = await _load_strategy_weights(redis)
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# Step 4: Adjust weights for strategies with enough trades
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any_adjusted = False
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for strategy_name, reward in rewards.items():
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if not adjuster.should_adjust(strategy_name):
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logger.debug(
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"Strategy %s has %d trades (need %d) -- skipping adjustment",
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strategy_name,
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adjuster.trade_counts.get(strategy_name, 0),
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adjuster.config.min_trades_before_adjustment,
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)
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continue
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old_weight = weights.get(strategy_name, adjuster.config.weight_floor)
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decayed_reward = adjuster.get_decayed_reward(strategy_name)
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new_weight = adjuster.adjust_weight(old_weight, decayed_reward)
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weights[strategy_name] = new_weight
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any_adjusted = True
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adjustment = WeightAdjustment(
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strategy_id=UUID(int=0), # placeholder -- DB would assign real ID
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strategy_name=strategy_name,
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old_weight=old_weight,
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new_weight=new_weight,
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reason=f"bandit_adjustment roi={outcome.roi_pct:.2f}%",
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reward_signal=reward,
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timestamp=datetime.now(timezone.utc),
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)
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adjustments.append(adjustment)
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counters["adjustments_made"].add(1)
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logger.info(
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"Weight adjusted: %s %.4f -> %.4f (reward=%.4f)",
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strategy_name,
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old_weight,
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new_weight,
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reward,
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)
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# Step 5: Normalize weights
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if any_adjusted:
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weights = adjuster.normalize_weights(weights)
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await _save_strategy_weights(redis, weights)
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# Track weight drift
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for name, weight in weights.items():
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default = 1.0 / len(weights)
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drift = abs(weight - default)
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counters["weight_drift"].record(drift, {"strategy": name})
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# Clean up opening trade
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await _clear_opening_trade(redis, trade.ticker)
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return adjustments
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async def run(config: LearningEngineConfig | None = None) -> None:
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"""Main service loop.
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Connects to Redis, initialises evaluator and weight adjuster, then
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continuously consumes from ``trades:executed`` and processes closed
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positions through the learning pipeline.
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"""
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if config is None:
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config = LearningEngineConfig()
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logging.basicConfig(level=config.log_level)
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logger.info("Starting Learning Engine service")
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# --- Telemetry ---
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meter = setup_telemetry("learning-engine", config.otel_metrics_port)
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counters = {
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"adjustments_made": meter.create_counter(
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"adjustments_made",
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description="Total strategy weight adjustments performed",
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),
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"weight_drift": meter.create_histogram(
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"weight_drift",
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description="Absolute deviation of each strategy weight from equal weight",
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),
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}
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# --- Redis ---
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redis = Redis.from_url(config.redis_url, decode_responses=False)
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consumer = StreamConsumer(redis, "trades:executed", "learning-engine", "worker-1")
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# --- Components ---
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evaluator = TradeEvaluator()
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adjuster = WeightAdjuster(config)
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logger.info("Consuming from trades:executed")
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# Graceful shutdown on SIGTERM/SIGINT
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shutdown_event = asyncio.Event()
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loop = asyncio.get_running_loop()
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for sig in (signal.SIGTERM, signal.SIGINT):
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loop.add_signal_handler(sig, shutdown_event.set)
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# --- Consume loop ---
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try:
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async for _msg_id, data in consumer.consume():
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if shutdown_event.is_set():
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break
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try:
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trade = TradeExecution.model_validate(data)
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if trade.status.value != "FILLED":
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logger.debug("Skipping non-filled trade: %s", trade.status.value)
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continue
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adjustments = await process_trade(trade, redis, evaluator, adjuster, counters)
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if adjustments:
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logger.info(
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"Made %d weight adjustment(s) for %s",
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len(adjustments),
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trade.ticker,
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)
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except Exception:
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logger.exception("Error processing trade execution: %s", data)
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finally:
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await redis.aclose()
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logger.info("Learning engine stopped gracefully")
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def main() -> None:
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"""CLI entry point."""
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asyncio.run(run())
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if __name__ == "__main__":
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main()
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