diff --git a/services/experimentAnalysis.ts b/services/experimentAnalysis.ts
index a43f38c..774c575 100644
--- a/services/experimentAnalysis.ts
+++ b/services/experimentAnalysis.ts
@@ -264,21 +264,21 @@ export function computeDiD(
   salesData: CombinedKPIs[]
 ): DifferenceInDifferencesResult {
   const startDate = parseLocalDate(experiment.start_date);
-  const endDate = experiment.end_date ? parseLocalDate(experiment.end_date) : new Date();
+  const endDate = experiment.end_date ? parseLocalDate(experiment.end_date) : new Date(new Date().setHours(0, 0, 0, 0));
 
-  // Seasonal Baseline override logic
-  let beforeStart: Date;
-  let beforeEnd = startDate; // By default, before period goes up to the exact start date
+  const startTs = startDate.getTime();
+  const endTs = endDate.getTime() + 86400000; // Add 24h so end bound is midnight of next day
+
+  let beforeStartTs: number;
+  let beforeEndTs: number;
 
   if (experiment.baseline_start_date && experiment.baseline_end_date) {
-    beforeStart = parseLocalDate(experiment.baseline_start_date);
-    beforeEnd = parseLocalDate(experiment.baseline_end_date);
-    // Explicit end + 1 day so `< beforeEnd` logic includes the last day
-    beforeEnd = new Date(beforeEnd.getTime() + 86400000);
+    beforeStartTs = parseLocalDate(experiment.baseline_start_date).getTime();
+    beforeEndTs = parseLocalDate(experiment.baseline_end_date).getTime() + 86400000;
   } else {
-    // Legacy auto-deduction (shifts the exact timeframe to the left)
-    const durationMs = endDate.getTime() - startDate.getTime();
-    beforeStart = new Date(startDate.getTime() - durationMs);
+    const durationMs = endTs - startTs;
+    beforeEndTs = startTs; // Adjacent: baseline ends right at the moment experiment starts
+    beforeStartTs = beforeEndTs - durationMs;
   }
 
   const treatmentAsinSet = getExperimentAsins(experiment.asins || [], salesData);
@@ -291,11 +291,6 @@ export function computeDiD(
     controlWeekly = aggregateWeeklyMetrics(controlAsinSet, salesData, experiment.marketplace);
   }
 
-  const startTs = startDate.getTime();
-  const endTs = endDate.getTime();
-  const beforeStartTs = beforeStart.getTime();
-  const beforeEndTs = beforeEnd.getTime();
-
   const tSplit = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
   const cSplit = hasControlGroup
     ? splitPeriods(controlWeekly, startTs, endTs, beforeStartTs, beforeEndTs)
@@ -347,19 +342,21 @@ export function buildCounterfactualSeries(
   metric: string
 ): TrendDataPoint[] {
   const startDate = parseLocalDate(experiment.start_date);
-  const endDate = experiment.end_date ? parseLocalDate(experiment.end_date) : new Date();
+  const endDate = experiment.end_date ? parseLocalDate(experiment.end_date) : new Date(new Date().setHours(0, 0, 0, 0));
 
-  // Seasonal Baseline override logic
-  let beforeStart: Date;
-  let beforeEnd = startDate;
+  const startTs = startDate.getTime();
+  const endTs = endDate.getTime() + 86400000; // Add 24h
+
+  let beforeStartTs: number;
+  let beforeEndTs: number;
 
   if (experiment.baseline_start_date && experiment.baseline_end_date) {
-    beforeStart = parseLocalDate(experiment.baseline_start_date);
-    beforeEnd = parseLocalDate(experiment.baseline_end_date);
-    beforeEnd = new Date(beforeEnd.getTime() + 86400000);
+    beforeStartTs = parseLocalDate(experiment.baseline_start_date).getTime();
+    beforeEndTs = parseLocalDate(experiment.baseline_end_date).getTime() + 86400000;
   } else {
-    const durationMs = endDate.getTime() - startDate.getTime();
-    beforeStart = new Date(startDate.getTime() - durationMs);
+    const durationMs = endTs - startTs;
+    beforeEndTs = startTs;
+    beforeStartTs = beforeEndTs - durationMs;
   }
 
   const treatmentAsinSet = getExperimentAsins(experiment.asins || [], salesData);
@@ -368,15 +365,7 @@ export function buildCounterfactualSeries(
   const hasControlGroup = (experiment.control_asins || []).length > 0;
 
   if (!hasControlGroup) {
-    // Without control group, counterfactual = flat line at pre-treatment average
-    const startTs = startDate.getTime();
-    const endTs = endDate.getTime();
-    const beforeStartTs = beforeStart.getTime();
-    const beforeEndTs = beforeEnd.getTime();
-
-    // To calculate counterfactual, we need the "before" average.
-    // Treatment before period:
-    const { before: beforeData } = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs); // Use splitPeriods to match exactly
+    const { before: beforeData } = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
     const preAvg = avgMetric(beforeData, metric);
 
     return treatmentWeekly.map(w => ({
@@ -390,11 +379,6 @@ export function buildCounterfactualSeries(
   const controlAsinSet = getExperimentAsins(experiment.control_asins, salesData);
   const controlWeekly = aggregateWeeklyMetrics(controlAsinSet, salesData, experiment.marketplace);
 
-  const startTs = startDate.getTime();
-  const endTs = endDate.getTime();
-  const beforeStartTs = beforeStart.getTime();
-  const beforeEndTs = beforeEnd.getTime();
-
   const { before: tBeforeData } = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
   const { before: cBeforeData } = splitPeriods(controlWeekly, startTs, endTs, beforeStartTs, beforeEndTs);