Harwinder Sekhon
9769a0c84d
Prompt-only change (static constants; plain-language output contract byte-identical). Ported from virattt/ai-hedge-fund's persona rubric: - _TAKE_CONTRACT hard rules: reason only from the JSON given, never invent numbers/events/levels; thin or missing evidence -> neutral with confidence below 30 (deferring to role-specific rules). - Anchored confidence bands in the contract and synthesizer, replacing the unanchored "how sure you are". - Per-stance support/oppose/neutral criteria for macro, technical, and risk (risk's missing-setup->oppose folded into its oppose bullet). - One plain-language support example beside each oppose example for macro/technical/risk, fixing the all-oppose few-shot bias. Contrarian's designed skeptic tilt untouched. News-engine prompts untouched (pinned by 78/78 + 18/18 validations). 374 tests green; real-DeepSeek smoke: macro correctly answered neutral/20 on empty news context, confidences spread 20-60 across the bands, all notes <=25 words, no jargon. Claude-Session: https://claude.ai/code/session_01TyZx9iRJwfbPTPnCBqcwTR
583 lines
25 KiB
Python
583 lines
25 KiB
Python
"""
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ForexBot2 — LLM agent panel (advisory consensus)
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=================================================
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ai-hedge-fund-style persona panel for XAU/USD signals: four DeepSeek
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personas (macro, technical, risk, contrarian) each review a finished
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SignalReport from their own angle, then a synthesizer chair weighs the
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takes and issues a consensus verdict (AGREE / NEUTRAL / OPPOSE with
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confidence and a short rationale).
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Two invariants, both load-bearing (CLAUDE.md rules 1 and 4):
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- ADVISORY / DISPLAY-ONLY. The verdict is written onto SignalReport
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fields that no gate and no scoring path ever reads. Any failure —
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LLM down, cap exhausted, garbage reply — degrades to "no panel",
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never to a changed or blocked signal.
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- NEVER runs inside ``analyze_from_dataframes``. The backtester replays
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that function; consumers (telegram_bot, main --panel,
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dashboard_publisher) attach the panel POST-HOC via ``attach_panel``,
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so replayed books can never see a panel field. Backtest parity holds
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by construction.
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Cost model: every full panel spends 5 requests (4 personas + 1
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synthesizer) against the shared DeepSeek daily cap (llm_client). Two
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layers bound the spend on top of that cap:
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- ``max_panels_per_day`` (config, default 20) — a UTC-day budget of
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panel *attempts*, mirroring the client's request cap.
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- A reuse TTL (default 3600 s) keyed on (symbol, timeframe, direction):
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a persisting signal re-serves its cached verdict across bars instead
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of re-spending. Without this, one sticky M5 dashboard signal would
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burn 5 calls per 5-minute bar — 1,440/day against the 500 cap.
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Prompts are STATIC module constants (DeepSeek prefix-cache pricing —
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see news_engine.py); all variable content travels in the user payload.
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"""
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import datetime as _dt
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import logging
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import threading
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import time
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from collections import OrderedDict
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from concurrent.futures import ThreadPoolExecutor, wait as _fwait
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from dataclasses import dataclass, field
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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from app_config import get_app_config
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from llm_client import DEFAULT_TIMEOUT, get_llm_client
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if TYPE_CHECKING: # type hints only — keep the coupling one-way
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from news_engine import FundamentalContext
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from signal_aggregator import SignalReport
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logger = logging.getLogger(__name__)
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DEFAULT_MIN_QUALITY = "B"
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DEFAULT_MAX_PANELS_PER_DAY = 20
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DEFAULT_REUSE_TTL_SECONDS = 3600.0
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# Quality ranks for the min_quality gate. Deliberately a local copy —
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# telegram_bot's ranking is private and dashboard_publisher must not
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# import telegram_bot (hard independence rule).
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_QUALITY_RANK = {"A+": 4, "A": 3, "B": 2, "C": 1}
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# ── DeepSeek prompts ─────────────────────────────────────────────────
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# STATIC constants on purpose: DeepSeek prices a repeated byte-identical
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# prompt prefix at the cache-hit rate (~50x cheaper), so nothing here may
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# be interpolated or timestamped. All variable content travels in the
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# user payload. Panel output is display-only — it never gates signals.
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_GOLD_DOCTRINE = """\
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Gold doctrine: hawkish Fed / rate hikes / rising real yields / strong
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dollar are gold-bearish; easing / rate cuts / safe-haven flows /
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geopolitical stress / weak dollar are gold-bullish.
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"""
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_TAKE_CONTRACT = """\
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Your review is advisory research on an already-generated signal; you are
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one voice on a panel, not the decision maker.
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Hard rules: use ONLY the data in the JSON you receive — do not invent
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numbers, events, or price levels. If the evidence your role needs is
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thin or missing, answer "neutral" with confidence below 30, unless your
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role's instructions say otherwise.
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Confidence scale (0-100): 80-100 strong one-sided evidence; 50-79 solid
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case with some doubt; 30-49 mixed evidence; below 30 little basis to
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judge.
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Write the note in plain everyday English for someone who has never
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traded: short simple sentences, no trading jargon, no acronyms, no
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abbreviations. Say "profit versus loss", not "RR"; "the 4-hour chart",
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not "H4"; describe what an indicator MEANS ("the price has climbed
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unusually fast"), never its name or number.
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Reply with ONLY JSON:
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{"stance": "support"|"oppose"|"neutral", "confidence": <0-100>,
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"note": "<max 25 words>"}
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"stance" says whether YOUR analysis supports the proposed trade
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direction, opposes it, or is genuinely mixed; "confidence" is how sure
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you are of your stance; "note" is your single most important point.
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"""
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_MACRO_SYSTEM = """\
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You are the macro analyst on a gold (XAU/USD) trade-review panel. You
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receive JSON: a proposed trade brief ("brief": direction, timeframe,
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quality, price, regime) plus the current fundamental picture — recent
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"headlines", today's high-impact "events" (released: true means the
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number is out), a news "sentiment" score for gold, and an optional
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"news_summary" blurb.
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Judge ONE thing: does the macro backdrop support the proposed direction?
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Weigh released surprises over stale headlines; an imminent unreleased
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high-impact event is a reason for lower confidence, not a direction.
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Stance rules:
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- support: the released data and news flow clearly push gold in the
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proposed direction.
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- oppose: they clearly push gold the other way.
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- neutral: the picture is mixed or stale — or there are no meaningful
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headlines or events to judge (score that low confidence).
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""" + _GOLD_DOCTRINE + """\
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Example: brief direction BUY, headlines dominated by hawkish Fed
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repricing and a hot CPI print -> {"stance": "oppose", "confidence": 70,
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"note": "Inflation is running hot, so interest rates should stay high — that usually pushes gold down."}
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Example: brief direction BUY, headlines led by an escalating conflict
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and a soft jobs report -> {"stance": "support", "confidence": 75,
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"note": "Worries about war and a cooling economy usually send money into gold."}
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""" + _TAKE_CONTRACT
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_TECH_SYSTEM = """\
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You are the technical analyst on a gold (XAU/USD) trade-review panel.
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You receive JSON: a proposed trade brief ("brief") plus the technical
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evidence — per-"indicators" signal/confidence, multi-timeframe
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"mtf" signals and scores, "key_levels" versus the current price, and an
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"exhaustion" count of consecutive same-direction bars flagged by the
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engine.
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Judge ONE thing: is the technical picture internally coherent for the
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proposed direction? Look for indicators fighting each other, the signal
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fighting the regime (e.g. a BUY in trending_down), higher timeframes
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disagreeing, price pressed into a key level, or an exhausted move being
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chased.
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Stance rules:
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- support: the indicators, higher timeframes, and regime mostly agree
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with the proposed direction and none of the conflicts above appear.
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- oppose: one or more of those conflicts is present and serious.
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- neutral: the evidence genuinely cuts both ways, or too much of it is
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missing to judge (score that low confidence).
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Example: brief direction SELL in regime trending_up with H1 and H4 both
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BUY -> {"stance": "oppose", "confidence": 80,
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"note": "This bets on gold falling while the bigger picture still points up."}
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Example: brief direction BUY in regime trending_up with M15 and H1 both
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BUY and exhaustion 0 -> {"stance": "support", "confidence": 70,
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"note": "Every way of looking at the chart points up, so buying goes with the flow."}
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""" + _TAKE_CONTRACT
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_RISK_SYSTEM = """\
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You are the risk manager on a gold (XAU/USD) trade-review panel. You
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receive JSON: a proposed trade brief ("brief") plus the risk facts —
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the trade "setup" (entry, stop_loss, take_profit_1/2, risk_reward,
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risk_dollars), the "volatility" regime, engine "warnings", the calendar
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"event_risk" level, and the assumed "spread".
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Judge ONE thing: is this setup's risk acceptable as constructed? Check
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reward-to-risk adequacy, stop distance versus the volatility regime
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(a tight stop in extreme vol dies to noise; a huge stop in low vol is
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oversized), spread as a fraction of the stop, and event landmines about
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to hit.
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Stance rules:
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- support: the likely reward clearly justifies the risk, the stop fits
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the current volatility, and no event landmine is imminent.
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- oppose: reward too thin for the risk, a stop mismatched to the
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volatility, spread eating a big share of the stop, an imminent
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high-impact event — or no setup at all (a missing setup means the
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signal graded too low for one; oppose unless the rest is
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exceptional).
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- neutral: acceptable but unremarkable — nothing dangerous, nothing
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attractive.
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Example: RR 1.4 with volatility extreme and a high-impact event due ->
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{"stance": "oppose", "confidence": 65,
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"note": "The possible profit is small for such wild price swings, and big economic news is due soon."}
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Example: RR 2.4 with normal volatility and no events due ->
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{"stance": "support", "confidence": 70,
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"note": "The possible profit is more than double the possible loss, and conditions look calm."}
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""" + _TAKE_CONTRACT
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_CONTRARIAN_SYSTEM = """\
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You are the contrarian on a gold (XAU/USD) trade-review panel — the
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designated skeptic. You receive JSON: a proposed trade brief ("brief"),
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the engine's own "bullish_reasons" and "bearish_reasons", its
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"warnings", and an optional "news_summary".
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Your job: construct the strongest honest case AGAINST the proposed
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trade, then score how damaging that case is. Use "oppose" only when the
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counter-case is genuinely strong, "neutral" when it is real but modest,
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and "support" when even the skeptic finds nothing that bites.
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""" + _GOLD_DOCTRINE + """\
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Example: a BUY whose own engine lists three bearish reasons and a
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counter-trend warning -> {"stance": "oppose", "confidence": 60,
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"note": "The system's own list of negatives looks stronger than its reason to buy."}
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""" + _TAKE_CONTRACT
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_SYNTH_SYSTEM = """\
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You are the chair of a gold (XAU/USD) trade-review panel. You receive
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JSON: the proposed trade "direction" and the panel's "takes" — one per
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analyst ("persona", "stance", "confidence", "note").
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Weigh the takes into ONE consensus on whether to act on the signal:
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"AGREE" (the panel backs the trade), "OPPOSE" (the panel is against it),
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or "NEUTRAL" (genuinely split or low conviction). Weigh a high-
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confidence oppose from any single analyst seriously — one strong veto
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can outweigh two lukewarm supports. Do not average blindly.
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Base the verdict and rationale ONLY on the takes given — do not add
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outside analysis.
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Confidence scale (0-100): 80-100 the panel clearly leans one way; 50-79
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a lean with real dissent; below 50 split or low conviction.
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Write the rationale in plain everyday English for someone who has never
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traded: short simple sentences, no trading jargon, no acronyms, no
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abbreviations.
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Example rationale: "Three of four analysts are against this: the
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possible profit is too small and the price has already climbed
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unusually fast. Only the news backdrop supports buying."
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Reply with ONLY JSON:
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{"verdict": "AGREE"|"NEUTRAL"|"OPPOSE", "confidence": <0-100>,
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"rationale": "<max 40 words>"}
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"""
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# ── Result types ─────────────────────────────────────────────────────
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@dataclass
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class PersonaTake:
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persona: str # "macro" | "technical" | "risk" | "contrarian"
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stance: str # "support" | "oppose" | "neutral"
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confidence: float # 0-100
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note: str
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@dataclass
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class PanelVerdict:
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verdict: str # "AGREE" | "NEUTRAL" | "OPPOSE"
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confidence: float # 0-100
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rationale: str
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takes: List[PersonaTake] = field(default_factory=list)
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created_ts: float = 0.0
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# ── Payload builders (pure functions; llm_client json-dumps sorted) ──
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def _direction(report: "SignalReport") -> Optional[str]:
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"""BUY/SELL the panel should review; None = nothing to review."""
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if report.trade_setup is not None:
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return report.trade_setup.direction
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if report.final_signal.value > 0:
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return "BUY"
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if report.final_signal.value < 0:
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return "SELL"
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return None
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def _brief(report: "SignalReport") -> Dict[str, Any]:
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analysis = report.primary_analysis
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return {
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"pair": report.display_name,
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"timeframe": analysis.timeframe if analysis else "",
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"direction": _direction(report),
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"quality": report.signal_quality,
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"score": round(report.final_score, 1),
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"confidence": round(report.final_confidence),
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"price": round(report.current_price, 2),
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"market_regime": report.market_regime,
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"volatility_regime": report.volatility_regime,
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"mtf_agreement": report.mtf_agreement,
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}
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|
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def _macro_payload(report: "SignalReport",
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context: Optional["FundamentalContext"]) -> Dict[str, Any]:
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headlines: List[str] = []
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events: List[Dict[str, Any]] = []
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if context is not None:
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headlines = [h.headline for h in context.news_headlines[:10]]
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events = [
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{"event": e.event, "currency": e.currency,
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"actual": e.actual, "forecast": e.forecast,
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"released": not e.is_upcoming}
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for e in context.events_today if e.is_high_impact
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][:8]
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return {
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"brief": _brief(report),
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"headlines": headlines,
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"events": events,
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"sentiment": round(report.news_sentiment, 3),
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"news_summary": report.news_summary,
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}
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|
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def _technical_payload(report: "SignalReport") -> Dict[str, Any]:
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indicators: Dict[str, Dict[str, Any]] = {}
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key_levels: Dict[str, float] = {}
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analysis = report.primary_analysis
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if analysis is not None:
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# Same visibility filter as main.report_to_dict: private series
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# ride as _-prefixed pseudo-indicators, weight-0 = context-only.
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indicators = {
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name: {"signal": ind.signal.name,
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"confidence": round(ind.confidence)}
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for name, ind in analysis.indicators.items()
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if not name.startswith("_") and ind.weight != 0
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}
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key_levels = {k: round(v, 2) for k, v in analysis.key_levels.items()}
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return {
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"brief": _brief(report),
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"indicators": indicators,
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"mtf": {tf: {"signal": sig.name,
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"score": round(report.mtf_scores.get(tf, 0.0), 1)}
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for tf, sig in report.mtf_signals.items()},
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"key_levels": key_levels,
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"exhaustion": report.exhaustion_count,
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}
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|
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def _risk_payload(report: "SignalReport") -> Dict[str, Any]:
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payload: Dict[str, Any] = {
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"brief": _brief(report),
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"volatility": report.volatility_regime,
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"warnings": list(report.warnings),
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"event_risk": report.event_risk,
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"event_description": report.event_description,
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"spread": round(report.spread_estimate, 2),
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}
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ts = report.trade_setup
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if ts is not None:
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payload["setup"] = {
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"direction": ts.direction,
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"entry": round(ts.entry_price, 2),
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"stop_loss": round(ts.stop_loss, 2),
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"take_profit_1": round(ts.take_profit_1, 2),
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"take_profit_2": round(ts.take_profit_2, 2),
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"risk_reward": round(ts.risk_reward, 2),
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"risk_dollars": round(ts.risk_pips * ts.pip_value, 2),
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}
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return payload
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|
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def _contrarian_payload(report: "SignalReport") -> Dict[str, Any]:
|
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return {
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"brief": _brief(report),
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"bullish_reasons": report.bullish_reasons[:3],
|
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"bearish_reasons": report.bearish_reasons[:3],
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"warnings": list(report.warnings),
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"news_summary": report.news_summary,
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}
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|
|
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# ── Orchestration ────────────────────────────────────────────────────
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|
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# Panel-private pool: the 4 persona calls run concurrently WITHOUT
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# occupying telegram_bot's shared scan executor (which has 4 workers —
|
|
# borrowing them would stall every other scan for the panel's duration).
|
|
_pool = ThreadPoolExecutor(max_workers=4, thread_name_prefix="agent-panel")
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_VALID_STANCES = ("support", "oppose", "neutral")
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_VALID_VERDICTS = ("AGREE", "NEUTRAL", "OPPOSE")
|
|
|
|
|
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def _clamp_confidence(raw: Any) -> Optional[float]:
|
|
try:
|
|
return max(0.0, min(100.0, float(raw)))
|
|
except (TypeError, ValueError):
|
|
return None
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|
|
|
|
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def _validate_take(persona: str, reply: Any) -> Optional[PersonaTake]:
|
|
if not isinstance(reply, dict):
|
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return None
|
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stance = reply.get("stance")
|
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if stance not in _VALID_STANCES:
|
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return None
|
|
confidence = _clamp_confidence(reply.get("confidence"))
|
|
if confidence is None:
|
|
return None
|
|
note = str(reply.get("note", "")).strip()[:160]
|
|
return PersonaTake(persona=persona, stance=stance,
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confidence=confidence, note=note)
|
|
|
|
|
|
def _ask_persona(persona: str, system: str,
|
|
payload: Dict[str, Any]) -> Optional[PersonaTake]:
|
|
# Client resolved at call time: tests install stubs via lc._client.
|
|
reply = get_llm_client().chat_json(system, payload)
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|
return _validate_take(persona, reply)
|
|
|
|
|
|
def run_panel(report: "SignalReport",
|
|
context: Optional["FundamentalContext"] = None,
|
|
) -> Optional[PanelVerdict]:
|
|
"""Run 4 personas + synthesizer; None = no verdict (fail-safe).
|
|
|
|
Requires ≥2 valid persona takes before spending the synthesizer
|
|
request; the synthesizer's reply is validated strictly and there is
|
|
deliberately NO programmatic fallback verdict — a panel that could
|
|
not really convene reports nothing rather than something invented.
|
|
"""
|
|
direction = _direction(report)
|
|
if direction is None:
|
|
return None
|
|
jobs = (
|
|
("macro", _MACRO_SYSTEM, _macro_payload(report, context)),
|
|
("technical", _TECH_SYSTEM, _technical_payload(report)),
|
|
("risk", _RISK_SYSTEM, _risk_payload(report)),
|
|
("contrarian", _CONTRARIAN_SYSTEM, _contrarian_payload(report)),
|
|
)
|
|
futures = [_pool.submit(_ask_persona, name, system, payload)
|
|
for name, system, payload in jobs]
|
|
# The HTTP timeout inside the client bounds each call; the margin
|
|
# covers pool queueing. Stragglers past the wait are abandoned (their
|
|
# threads finish harmlessly) and simply don't vote.
|
|
client_timeout = getattr(get_llm_client(), "_timeout", DEFAULT_TIMEOUT)
|
|
done, _ = _fwait(futures, timeout=client_timeout + 8.0)
|
|
takes = [f.result() for f in done if not f.exception()]
|
|
takes = [t for t in takes if t is not None]
|
|
if len(takes) < 2:
|
|
logger.debug("agent panel: %d/4 persona takes — skipping synthesizer",
|
|
len(takes))
|
|
return None
|
|
order = {"macro": 0, "technical": 1, "risk": 2, "contrarian": 3}
|
|
takes.sort(key=lambda t: order.get(t.persona, 9))
|
|
reply = get_llm_client().chat_json(_SYNTH_SYSTEM, {
|
|
"direction": direction,
|
|
"takes": [{"persona": t.persona, "stance": t.stance,
|
|
"confidence": t.confidence, "note": t.note}
|
|
for t in takes],
|
|
})
|
|
if not isinstance(reply, dict):
|
|
return None
|
|
verdict = reply.get("verdict")
|
|
confidence = _clamp_confidence(reply.get("confidence"))
|
|
if verdict not in _VALID_VERDICTS or confidence is None:
|
|
return None
|
|
rationale = str(reply.get("rationale", "")).strip()[:240]
|
|
return PanelVerdict(verdict=verdict, confidence=confidence,
|
|
rationale=rationale, takes=takes,
|
|
created_ts=time.time())
|
|
|
|
|
|
# ── Cache, budget, attach ────────────────────────────────────────────
|
|
|
|
_state_lock = threading.Lock()
|
|
# Exact result per reviewed bar: repeated attaches on the same signal
|
|
# (re-scan, multi-consumer) are free.
|
|
_cache: "OrderedDict[Tuple[str, str, str, str], PanelVerdict]" = OrderedDict()
|
|
_CACHE_MAX = 32
|
|
# Reuse layer: a persisting signal (same symbol/tf/direction, newer bar)
|
|
# re-serves its last verdict for reuse_ttl_seconds instead of re-spending
|
|
# 5 requests per bar (see module docstring for the M5 dashboard math).
|
|
_last_by_dir: Dict[Tuple[str, str, str], PanelVerdict] = {}
|
|
# UTC-day budget of panel attempts (mirrors llm_client._under_cap).
|
|
_panel_day: Optional[_dt.date] = None
|
|
_panel_count: int = 0
|
|
_warned_budget = False
|
|
|
|
|
|
def reset_panel_state() -> None:
|
|
"""Drop caches and counters (tests / config reloads)."""
|
|
global _panel_day, _panel_count, _warned_budget
|
|
with _state_lock:
|
|
_cache.clear()
|
|
_last_by_dir.clear()
|
|
_panel_day = None
|
|
_panel_count = 0
|
|
_warned_budget = False
|
|
|
|
|
|
def panel_health() -> Dict[str, Any]:
|
|
"""Read-only counters for status displays."""
|
|
settings = get_app_config().agent_panel_settings
|
|
cap = int(settings.get("max_panels_per_day", DEFAULT_MAX_PANELS_PER_DAY))
|
|
today = _dt.datetime.now(_dt.timezone.utc).date()
|
|
with _state_lock:
|
|
used = _panel_count if _panel_day == today else 0
|
|
return {"used_today": used, "cap": cap}
|
|
|
|
|
|
def _under_panel_budget(cap: int) -> bool:
|
|
"""Count one panel attempt against the UTC-day budget."""
|
|
global _panel_day, _panel_count, _warned_budget
|
|
today = _dt.datetime.now(_dt.timezone.utc).date()
|
|
with _state_lock:
|
|
if _panel_day != today:
|
|
_panel_day = today
|
|
_panel_count = 0
|
|
_warned_budget = False
|
|
if _panel_count >= cap:
|
|
if not _warned_budget:
|
|
_warned_budget = True
|
|
logger.warning(
|
|
"agent panel: daily budget (%d) exhausted — no more "
|
|
"panels until UTC midnight", cap)
|
|
return False
|
|
_panel_count += 1
|
|
return True
|
|
|
|
|
|
def _write_fields(report: "SignalReport", v: PanelVerdict) -> None:
|
|
report.panel_verdict = v.verdict
|
|
report.panel_confidence = float(v.confidence)
|
|
report.panel_rationale = v.rationale
|
|
report.panel_takes = [
|
|
{"persona": t.persona, "stance": t.stance,
|
|
"confidence": t.confidence, "note": t.note} for t in v.takes]
|
|
|
|
|
|
def attach_panel(report: "SignalReport",
|
|
settings: Optional[Dict[str, Any]] = None,
|
|
surface: str = "telegram",
|
|
force: bool = False,
|
|
context: Optional["FundamentalContext"] = None) -> bool:
|
|
"""Best-effort panel attach; True iff panel fields were populated.
|
|
|
|
Advisory feature contract: NEVER raises — an exploding panel must
|
|
not cost an alert send or a dashboard publish. ``force`` (CLI
|
|
--panel) bypasses the enabled/surface/quality toggles but never the
|
|
daily budget or the shared LLM cap.
|
|
"""
|
|
try:
|
|
if settings is None:
|
|
settings = get_app_config().agent_panel_settings
|
|
if not force:
|
|
if not settings.get("enabled", False):
|
|
return False
|
|
surface_on = {
|
|
"telegram": settings.get("telegram", True),
|
|
"dashboard": settings.get("dashboard", False),
|
|
}.get(surface, True)
|
|
if not surface_on:
|
|
return False
|
|
min_quality = settings.get("min_quality", DEFAULT_MIN_QUALITY)
|
|
if (_QUALITY_RANK.get(report.signal_quality, -1)
|
|
< _QUALITY_RANK.get(min_quality, 2)):
|
|
return False
|
|
direction = _direction(report)
|
|
if direction is None:
|
|
return False
|
|
|
|
analysis = report.primary_analysis
|
|
tf = analysis.timeframe if analysis else ""
|
|
exact_key = (report.symbol, tf, report.timestamp, direction)
|
|
dir_key = (report.symbol, tf, direction)
|
|
ttl = float(settings.get("reuse_ttl_seconds",
|
|
DEFAULT_REUSE_TTL_SECONDS))
|
|
with _state_lock:
|
|
hit = _cache.get(exact_key)
|
|
if hit is None:
|
|
prior = _last_by_dir.get(dir_key)
|
|
if prior is not None and time.time() - prior.created_ts < ttl:
|
|
hit = prior
|
|
if hit is not None:
|
|
_write_fields(report, hit)
|
|
return True
|
|
|
|
cap = int(settings.get("max_panels_per_day",
|
|
DEFAULT_MAX_PANELS_PER_DAY))
|
|
if not _under_panel_budget(cap):
|
|
return False
|
|
|
|
if context is None:
|
|
# Best-effort; the news engine's class-level 600 s caches make
|
|
# this nearly free when warm in both bot and dashboard.
|
|
try:
|
|
from config import PlatformConfig
|
|
from news_engine import NewsEngine
|
|
context = NewsEngine(
|
|
PlatformConfig().news).get_fundamental_context()
|
|
except Exception as e:
|
|
logger.debug("agent panel: news context unavailable: %s", e)
|
|
context = None
|
|
|
|
verdict = run_panel(report, context)
|
|
if verdict is None:
|
|
return False
|
|
_write_fields(report, verdict)
|
|
with _state_lock:
|
|
_cache[exact_key] = verdict
|
|
while len(_cache) > _CACHE_MAX:
|
|
_cache.popitem(last=False)
|
|
_last_by_dir[dir_key] = verdict
|
|
return True
|
|
except Exception:
|
|
logger.exception("agent panel: attach failed — signal unaffected")
|
|
return False
|