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New service at services/signal_engine/ implementing concurrent heuristic (deterministic scoring) and probabilistic (Bayesian inference) pipelines that evaluate technical signals across 6 timeframes (M30-M) and produce independent BUY/WATCH/SKIP verdicts per ticker per evaluation tick. Components: - Input Normalizer: multi-source data assembly with sentinel fallbacks - Signal Library: Fibonacci, MA Stack, RSI, Cup & Handle, Elliott Wave - Multi-Timeframe Confluence Engine: weighted scoring with D/W/M anchors - Hard Filter Engine: macro_bias, valuation, earnings proximity gating - Heuristic Pipeline: S_total scoring with confidence-gated verdicts - Probabilistic Pipeline: Bayesian log-odds with regime priors, entropy gating, EV_R calculation, and signal correlation penalty - Exit Engine: stop-loss, targets, trailing ATR-based stops - Delta Analyzer: pipeline agreement tracking with rolling Redis metrics - Output Formatter: SignalOutput contract + Recommendation schema mapping - Worker orchestrator: concurrent pipelines with failure isolation - Main entry point: queue polling with fail-safe config loading Infrastructure: - Migration 039: signal_engine_outputs table with 3 indexes - Helm chart: signalEngine service entry (processing tier) - Redis key: QUEUE_SIGNAL_ENGINE constant Tests: 390 tests (unit + property-based) covering all components Config: dual_pipeline_enabled=false by default (safe rollout)
128 lines
3.6 KiB
Python
128 lines
3.6 KiB
Python
"""Base protocol and common helpers for signal evaluators.
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Defines the ``SignalEvaluator`` protocol that every signal in the Signal
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Library must satisfy, plus shared utility functions for swing detection,
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lookback validation, and simple moving average computation.
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"""
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from __future__ import annotations
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from typing import Protocol
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from services.signal_engine.models import OHLCVBar, SignalResult
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# ---------------------------------------------------------------------------
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# Signal evaluator protocol
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# ---------------------------------------------------------------------------
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class SignalEvaluator(Protocol):
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"""Protocol for all signal evaluators in the Signal Library.
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Each evaluator receives a list of OHLCV bars for a single timeframe
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and returns a ``SignalResult`` when the signal triggers, or ``None``
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when insufficient data is available or the signal does not fire.
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"""
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def evaluate(
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self,
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bars: list[OHLCVBar],
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timeframe: str,
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) -> SignalResult | None:
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"""Evaluate a signal on a single timeframe's bar data.
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Returns ``None`` when insufficient data is available.
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"""
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...
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# ---------------------------------------------------------------------------
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# Common helper functions
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# ---------------------------------------------------------------------------
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def find_swing_high(
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bars: list[OHLCVBar],
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lookback: int,
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) -> tuple[int, float] | None:
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"""Find the highest high in the last *lookback* bars.
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Args:
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bars: OHLCV bar series (oldest-first).
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lookback: Number of recent bars to search.
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Returns:
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``(index, price)`` of the bar with the highest high within the
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lookback window, or ``None`` if *bars* has fewer than *lookback*
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entries.
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"""
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if len(bars) < lookback or lookback <= 0:
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return None
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window = bars[-lookback:]
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offset = len(bars) - lookback
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best_idx = 0
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best_price = window[0].high
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for i, bar in enumerate(window):
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if bar.high >= best_price:
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best_idx = i
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best_price = bar.high
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return (offset + best_idx, best_price)
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def find_swing_low(
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bars: list[OHLCVBar],
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lookback: int,
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) -> tuple[int, float] | None:
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"""Find the lowest low in the last *lookback* bars.
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Args:
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bars: OHLCV bar series (oldest-first).
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lookback: Number of recent bars to search.
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Returns:
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``(index, price)`` of the bar with the lowest low within the
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lookback window, or ``None`` if *bars* has fewer than *lookback*
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entries.
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"""
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if len(bars) < lookback or lookback <= 0:
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return None
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window = bars[-lookback:]
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offset = len(bars) - lookback
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best_idx = 0
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best_price = window[0].low
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for i, bar in enumerate(window):
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if bar.low <= best_price:
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best_idx = i
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best_price = bar.low
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return (offset + best_idx, best_price)
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def validate_lookback(bars: list[OHLCVBar], min_bars: int) -> bool:
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"""Return ``True`` if *bars* contains at least *min_bars* entries."""
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return len(bars) >= min_bars
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def compute_sma(bars: list[OHLCVBar], period: int) -> float | None:
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"""Compute the simple moving average of close prices over the last *period* bars.
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Args:
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bars: OHLCV bar series (oldest-first).
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period: Number of recent bars to average.
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Returns:
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The arithmetic mean of the last *period* close prices, or ``None``
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if *bars* has fewer than *period* entries or *period* is not
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positive.
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"""
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if period <= 0 or len(bars) < period:
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return None
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total = sum(bar.close for bar in bars[-period:])
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return total / period
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