Conviction campaign 2026-09-27 (FAILED, research knob kept) + plain-words alert wording
Owner question: can score and confidence be combined into one number that is easier to understand and for the system to decide on? Research (pre-registered, nothing ships to the signal path): - signals.grading_mode: "pair" (default, the shipped two-floor rule, byte- identical: all five reference MD5s reproduce on both scripts), "min" (min(|score|, 0.6*conf)), "product" (|score|*conf/100); thresholds fixed from the control's corner points, no tuning. Helpers _final_signal_and_grade / _transitioning_boost / conviction / CONVICTION_THRESHOLDS in signal_aggregator; mirrored in K2 (GRADING_MODE_CODES, PF_conv_* packed from the live module); twin refuses other values; M15 OOS MD5 equal on both scripts for both modes. - Verdict: FAILED on M5 and M15. K-MIN moves 43-54 trades for -1.2R / -2.9R (clauses a,b,e / a,b,d); K-PROD -29R on M15 (PF 1.29, W3 DD 28.9R), neutral on M5. H1/H4 product +11.9R / +17.0R on W3 from extra low-score trades (H1 PF 1.98 -> 1.75, DD doubles): hypothesis only. - Code review (high) before the runs: 6 findings fixed, 1 recorded (engine_config_fingerprint omits the switch under "pair", so live rows keep their fingerprint). Delivery (books untouched): alerts lead with the grade and print the two raw numbers in words (main.plain_words: "Direction: strong buy · Reliability: good · timeframes agree", cut-offs = the grade table's own) on Telegram, /status, the CLI and the dashboard card (plain_words in the snapshot); "MTF" label replaced by "Other timeframes"; guides updated. Suite 566 passed. Evidence: backtest/research/conviction_campaign_2026-09-27.md Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
This commit is contained in:
Harwinder Sekhon
+141
-30
@@ -33,6 +33,128 @@ import signal_log
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logger = logging.getLogger(__name__)
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# ── Grading rule (score + confidence -> final signal and grade) ─────────
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# "pair" is the shipped rule; "min" / "product" are the 2026-09-27 research
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# candidates (backtest/research/conviction_campaign_2026-09-27.md). The CUDA
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# twin (backtest/cuda_signal_kernels.py, K2) mirrors these helpers and packs
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# CONVICTION_THRESHOLDS from here, so this module is the single source.
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GRADING_MODES = ("pair", "min", "product")
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# (aplus, a, b, c, strong, buy, boost_a, boost_b) on the conviction scale.
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# "min": the control's corners on the |score| axis (0.6 = the control's
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# score / confidence ratio). "product": the control's corner products
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# 40x65, 33x55, 28x50, 25x45, 35x55, 25x50, 30x50, 25x45, each / 100.
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CONVICTION_THRESHOLDS = {
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"min": (40.0, 33.0, 28.0, 25.0, 35.0, 25.0, 30.0, 25.0),
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"product": (26.0, 18.15, 14.0, 11.25, 19.25, 12.5, 15.0, 11.25),
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}
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def grading_mode_of(signals_cfg) -> str:
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"""The validated `signals.grading_mode`; an unknown value is a hard error."""
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mode = signals_cfg.grading_mode
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if mode not in GRADING_MODES:
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raise ValueError(
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f"signals.grading_mode={mode!r}; expected one of {GRADING_MODES}"
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)
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return mode
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def conviction(abs_score: float, confidence: float, mode: str) -> float:
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"""The single decision number of the research modes. Evaluation order is
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load-bearing for the CUDA mirror: min(a, b) with b = 0.6 * confidence in
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that argument order (PYMIN), product as (abs_score * confidence) / 100."""
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if mode == "min":
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return min(abs_score, 0.6 * confidence)
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if mode == "product":
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return abs_score * confidence / 100.0
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raise ValueError(f"conviction() has no formula for grading_mode={mode!r}")
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def _signal_pair(score: float, confidence: float) -> SignalStrength:
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"""The shipped final-signal rule (validated literals; do not edit)."""
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if score >= 35 and confidence >= 55:
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return SignalStrength.STRONG_BUY
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elif score >= 25 and confidence >= 50:
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return SignalStrength.BUY
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elif score <= -35 and confidence >= 55:
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return SignalStrength.STRONG_SELL
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elif score <= -25 and confidence >= 50:
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return SignalStrength.SELL
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return SignalStrength.NEUTRAL
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def _quality_pair(abs_score: float, confidence: float, mtf: bool) -> str:
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"""The shipped grade table (validated literals; do not edit)."""
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if abs_score >= 40 and confidence >= 65 and mtf:
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return "A+"
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elif abs_score >= 33 and confidence >= 55 and mtf:
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return "A"
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elif abs_score >= 28 and confidence >= 50:
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return "B"
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elif abs_score >= 25 and confidence >= 45:
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return "C"
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elif abs_score >= 15:
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return "D"
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return "F"
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def _signal_conv(score: float, conv: float, mode: str) -> SignalStrength:
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t_strong, t_buy = CONVICTION_THRESHOLDS[mode][4:6]
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if score > 0 and conv >= t_strong:
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return SignalStrength.STRONG_BUY
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elif score > 0 and conv >= t_buy:
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return SignalStrength.BUY
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elif score < 0 and conv >= t_strong:
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return SignalStrength.STRONG_SELL
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elif score < 0 and conv >= t_buy:
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return SignalStrength.SELL
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return SignalStrength.NEUTRAL
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def _quality_conv(abs_score: float, conv: float, mtf: bool, mode: str) -> str:
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t_aplus, t_a, t_b, t_c = CONVICTION_THRESHOLDS[mode][0:4]
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if conv >= t_aplus and mtf:
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return "A+"
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elif conv >= t_a and mtf:
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return "A"
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elif conv >= t_b:
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return "B"
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elif conv >= t_c:
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return "C"
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elif abs_score >= 15:
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return "D"
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return "F"
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def _final_signal_and_grade(score: float, confidence: float, mtf: bool, mode: str):
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"""(final_signal, signal_quality) for the post-penalty score / confidence.
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"pair" is the shipped rule; the research modes grade on one conviction
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number. The single dispatch point the CUDA K2 twin mirrors."""
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if mode == "pair":
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return _signal_pair(score, confidence), _quality_pair(abs(score), confidence, mtf)
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conv = conviction(abs(score), confidence, mode)
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return _signal_conv(score, conv, mode), _quality_conv(abs(score), conv, mtf, mode)
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def _transitioning_boost(abs_score: float, confidence: float, mtf: bool, mode: str) -> Optional[str]:
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"""Gate 6c's grade lift in a transitioning regime: "A", "B" or None.
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The "pair" branch is the shipped rule (validated literals; do not edit)."""
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if mode == "pair":
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if abs_score >= 30 and confidence >= 50 and mtf:
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return "A"
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elif abs_score >= 25 and confidence >= 45:
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return "B"
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return None
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conv = conviction(abs_score, confidence, mode)
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t_boost_a, t_boost_b = CONVICTION_THRESHOLDS[mode][6:8]
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if conv >= t_boost_a and mtf:
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return "A"
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elif conv >= t_boost_b:
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return "B"
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return None
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def _is_forex_weekend(now_utc: datetime) -> bool:
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"""Gold/FX market closed for the weekend: Friday 17:00 → Sunday 18:00
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NEW YORK time (= Fri 21:00 UTC in US summer, 22:00 in winter —
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@@ -169,6 +291,7 @@ class SignalAggregator:
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def __init__(self, config: PlatformConfig):
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self.config = config
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grading_mode_of(config.signals) # fail at start, not at the first bar
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self.data_provider = make_data_provider(config)
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self.tech_engine = TechnicalEngine(config.technical)
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self._news_engine: Optional[NewsEngine] = None # lazy
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@@ -431,9 +554,15 @@ class SignalAggregator:
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multipliers actually in force (override included) and the
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timeframe set. Logged on every row so indicator-weight research
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can tell which rows share a scoring configuration."""
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signals = asdict(self.config.signals)
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# The research grading switch enters the hash only when it is in
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# force: under the shipped "pair" rule the scoring is byte-identical
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# to the pre-2026-09-27 code, so live rows keep the same fingerprint.
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if signals.get("grading_mode") == "pair":
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signals.pop("grading_mode")
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payload = {
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"technical": asdict(self.config.technical),
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"signals": asdict(self.config.signals),
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"signals": signals,
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"regime_weights": regime_weight_table(self.config.technical),
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"primary": self.config.primary_timeframe.value,
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"confirm": [tf.value for tf in self.config.confirmation_timeframes],
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@@ -1147,10 +1276,14 @@ class SignalAggregator:
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# ── 6c. Transitioning regime quality boost ──────────────────
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if report.market_regime == "transitioning" and report.signal_quality in ("B", "C", "D"):
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abs_score = abs(report.final_score)
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if abs_score >= 30 and report.final_confidence >= 50 and report.mtf_agreement:
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boost = _transitioning_boost(
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abs_score, report.final_confidence, report.mtf_agreement,
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grading_mode_of(self.config.signals),
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)
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if boost == "A":
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report.signal_quality = "A"
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report.action_recommendation = f"{report.final_signal.name.replace('_', ' ')} — Trend emerging, early entry"
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elif abs_score >= 25 and report.final_confidence >= 45:
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elif boost == "B":
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report.signal_quality = "B"
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report.action_recommendation = f"{report.final_signal.name.replace('_', ' ')} — Trend forming, watch closely"
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@@ -1743,36 +1876,14 @@ class SignalAggregator:
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)
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confidence = 0.0
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# Final signal
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if score >= 35 and confidence >= 55:
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report.final_signal = SignalStrength.STRONG_BUY
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elif score >= 25 and confidence >= 50:
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report.final_signal = SignalStrength.BUY
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elif score <= -35 and confidence >= 55:
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report.final_signal = SignalStrength.STRONG_SELL
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elif score <= -25 and confidence >= 50:
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report.final_signal = SignalStrength.SELL
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else:
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report.final_signal = SignalStrength.NEUTRAL
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# Final signal (grading_mode: "pair" = the shipped rule; the research
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# modes grade on one conviction number — helpers at module top)
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report.final_signal, report.signal_quality = _final_signal_and_grade(
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score, confidence, report.mtf_agreement, grading_mode_of(self.config.signals)
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)
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report.final_score = score
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report.final_confidence = confidence
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# Quality grade
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abs_score = abs(score)
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if abs_score >= 40 and confidence >= 65 and report.mtf_agreement:
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report.signal_quality = "A+"
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elif abs_score >= 33 and confidence >= 55 and report.mtf_agreement:
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report.signal_quality = "A"
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elif abs_score >= 28 and confidence >= 50:
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report.signal_quality = "B"
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elif abs_score >= 25 and confidence >= 45:
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report.signal_quality = "C"
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elif abs_score >= 15:
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report.signal_quality = "D"
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else:
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report.signal_quality = "F"
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if not report.action_recommendation:
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sig_name = report.final_signal.name.replace("_", " ")
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if report.signal_quality in ("A+", "A"):
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