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Block audited losing signal cohorts

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Theodore Song
2026-08-18 12:36:12 -04:00
parent 846d1f1482
commit 25622d2ee5
6 changed files with 208 additions and 52 deletions
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scripts/evaluate-signals.mjs
+21 -4
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@@ -179,7 +179,16 @@ const RULES = [
{ name: "follow_reversal", field: "netReturn", test: (row) => row.type === "reversal" },
{ name: "fade_trend", field: "fadeNetReturn", test: (row) => row.type === "trend" },
{ name: "fade_trend_yes_move", field: "fadeNetReturn", test: (row) => row.type === "trend" && row.side === "YES" },
{ name: "fade_trend_no_move", field: "fadeNetReturn", test: (row) => row.type === "trend" && row.side === "NO" },
{ name: "fade_trend_mid", field: "fadeNetReturn", test: (row) => row.type === "trend" && row.band === "mid" },
{ name: "fade_trend_favorites", field: "fadeNetReturn", test: (row) => row.type === "trend" && ["favorite", "heavy-favorite"].includes(row.band) },
{ name: "fade_trend_longshots", field: "fadeNetReturn", test: (row) => row.type === "trend" && row.band === "longshot" },
{ name: "fade_strong_trend", field: "fadeNetReturn", test: (row) => row.type === "trend" && Math.abs(row.dayMove) >= 0.015 && Math.abs(row.weekMove) >= 0.03 },
{ name: "fade_moderate_trend", field: "fadeNetReturn", test: (row) => row.type === "trend" && Math.abs(row.dayMove) <= 0.03 && Math.abs(row.weekMove) <= 0.10 },
...["Politics", "Sports", "Crypto", "Economy", "Pop Culture", "Other"].map((category) => ({
name: `fade_trend_${category.toLowerCase().replace(/\s+/g, "_")}`, field: "fadeNetReturn",
test: (row) => row.type === "trend" && row.category === category,
})),
];
function evaluateRules(rows) {
@@ -196,17 +205,25 @@ function chronologicalEvaluation(rows) {
ordered.filter((row) => row.observedAt >= cut1 && row.observedAt < cut2),
ordered.filter((row) => row.observedAt >= cut2)];
const thirdRules = thirds.map(evaluateRules), pooled = evaluateRules(ordered);
const trainRules = evaluateRules(train), testRules = evaluateRules(test);
const robustRules = Object.fromEntries(RULES.map((rule) => {
const segments = thirdRules.map((result) => result[rule.name]);
const trainStats = trainRules[rule.name], testStats = testRules[rule.name], pooledStats = pooled[rule.name];
const enoughData = segments.every((segment) => segment.count >= 20 && segment.markets >= 5);
const allPositive = enoughData && segments.every((segment) => segment.mean > 0 && segment.marketMean > 0);
const allNegative = enoughData && segments.every((segment) => segment.mean < 0 && segment.marketMean < 0);
const trainTestPositive = trainStats.count >= 40 && testStats.count >= 20
&& trainStats.mean > 0 && trainStats.marketMean > 0 && testStats.mean > 0 && testStats.marketMean > 0;
const trainTestNegative = trainStats.count >= 40 && testStats.count >= 20
&& trainStats.mean < 0 && trainStats.marketMean < 0 && testStats.mean < 0 && testStats.marketMean < 0;
const allPositive = enoughData && trainTestPositive && pooledStats.lower90 > 0
&& segments.every((segment) => segment.mean > 0 && segment.marketMean > 0);
const allNegative = enoughData && trainTestNegative && pooledStats.upper90 < 0
&& segments.every((segment) => segment.mean < 0 && segment.marketMean < 0);
return [rule.name, { enoughData, allPositive, allNegative,
minimumSegmentMean: Math.min(...segments.map((segment) => segment.mean)),
maximumSegmentMean: Math.max(...segments.map((segment) => segment.mean)), pooled: pooled[rule.name] }];
maximumSegmentMean: Math.max(...segments.map((segment) => segment.mean)), pooled: pooledStats }];
}));
return { splitTime: splitTime ? new Date(splitTime * 1000).toISOString() : null,
trainCount: train.length, testCount: test.length, train: evaluateRules(train), test: evaluateRules(test),
trainCount: train.length, testCount: test.length, train: trainRules, test: testRules,
thirds: thirdRules, robustRules };
}