From 2940b120e0da5b52f5bfd5c6f87426fda8f075a0 Mon Sep 17 00:00:00 2001
From: megaproxy <megaproxy@gmail.com>
Date: Tue, 28 Apr 2026 11:02:41 +0000
Subject: [PATCH] ML predictions Phase 5: Monte Carlo contribution fix and
 milestone table

Fix contribution compounding: monthly contributions are now added to
asset values before each GBM step so they grow with market returns,
rather than being summed as a static lump at each period.

Add year-by-year milestone table below the fan chart showing P10/P50/P90
portfolio values at each annual checkpoint up to the selected horizon.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 backend/app/ml/monte_carlo.py                 | 20 +++++++++++--
 frontend/src/api/predictions.ts               |  9 ++++++
 .../src/pages/predictions/PredictionsPage.tsx | 30 +++++++++++++++++++
 3 files changed, 56 insertions(+), 3 deletions(-)

diff --git a/backend/app/ml/monte_carlo.py b/backend/app/ml/monte_carlo.py
index 166b6c5..dc0a50c 100644
--- a/backend/app/ml/monte_carlo.py
+++ b/backend/app/ml/monte_carlo.py
@@ -100,14 +100,15 @@ def run_monte_carlo(
     for sim in range(n_sims):
         asset_values = current_values.copy()
         for t in range(n_months):
+            # Add monthly contribution before GBM step so it compounds
+            if monthly_contribution > 0:
+                asset_values = asset_values + weights * monthly_contribution
             Z = rng.standard_normal(n_assets)
             corr_Z = L @ Z
-            # GBM step for each asset
             asset_values = asset_values * np.exp(
                 (mus - 0.5 * sigmas ** 2) * DT + sigmas * np.sqrt(DT) * corr_Z
             )
-            port_val = float(asset_values.sum()) + monthly_contribution * (t + 1)
-            portfolio_paths[sim, t] = max(0.0, port_val)
+            portfolio_paths[sim, t] = max(0.0, float(asset_values.sum()))
 
     # Compute percentile paths
     pcts = {
@@ -122,6 +123,18 @@ def run_monte_carlo(
     prob_gain = float(np.mean(final_values > total_value))
     expected_value = float(np.median(final_values))
 
+    # Year-by-year milestones (at each 12-month boundary)
+    milestones = []
+    for yr in range(1, years + 1):
+        idx = min(yr * 12 - 1, n_months - 1)
+        milestones.append({
+            "year": yr,
+            "date": future_dates[idx],
+            "p10": round(float(np.percentile(portfolio_paths[:, idx], 10)), 2),
+            "p50": round(float(np.percentile(portfolio_paths[:, idx], 50)), 2),
+            "p90": round(float(np.percentile(portfolio_paths[:, idx], 90)), 2),
+        })
+
     return {
         "dates": future_dates,
         "percentiles": {
@@ -131,5 +144,6 @@ def run_monte_carlo(
         "current_value": round(total_value, 2),
         "expected_value": round(expected_value, 2),
         "probability_of_gain": round(prob_gain, 3),
+        "milestones": milestones,
         "insufficient_data": False,
     }
diff --git a/frontend/src/api/predictions.ts b/frontend/src/api/predictions.ts
index e1966de..6acc802 100644
--- a/frontend/src/api/predictions.ts
+++ b/frontend/src/api/predictions.ts
@@ -28,6 +28,14 @@ export interface PercentilePath {
   value: number;
 }
 
+export interface MonteCarloMilestone {
+  year: number;
+  date: string;
+  p10: number;
+  p50: number;
+  p90: number;
+}
+
 export interface MonteCarloResponse {
   dates: string[];
   percentiles: {
@@ -40,6 +48,7 @@ export interface MonteCarloResponse {
   current_value: number;
   expected_value: number;
   probability_of_gain: number;
+  milestones: MonteCarloMilestone[];
   insufficient_data: boolean;
 }
 
diff --git a/frontend/src/pages/predictions/PredictionsPage.tsx b/frontend/src/pages/predictions/PredictionsPage.tsx
index ceb6c3b..54f2a68 100644
--- a/frontend/src/pages/predictions/PredictionsPage.tsx
+++ b/frontend/src/pages/predictions/PredictionsPage.tsx
@@ -525,6 +525,36 @@ function MonteCarloTab() {
               style={{ width: "100%", height: "360px" }}
             />
           </div>
+
+          {/* Year-by-year milestone table */}
+          {data.milestones?.length > 0 && (
+            <div className="bg-card border border-border rounded-xl overflow-hidden">
+              <div className="px-5 py-3 border-b border-border">
+                <p className="text-sm font-semibold">Year-by-Year Projections</p>
+                <p className="text-xs text-muted-foreground mt-0.5">P10 = pessimistic · P50 = median · P90 = optimistic</p>
+              </div>
+              <table className="w-full text-sm">
+                <thead>
+                  <tr className="border-b border-border bg-secondary/30">
+                    <th className="text-left px-5 py-2 text-xs text-muted-foreground font-medium">Year</th>
+                    <th className="text-right px-5 py-2 text-xs text-destructive font-medium">P10</th>
+                    <th className="text-right px-5 py-2 text-xs text-foreground font-medium">P50</th>
+                    <th className="text-right px-5 py-2 text-xs text-success font-medium">P90</th>
+                  </tr>
+                </thead>
+                <tbody>
+                  {data.milestones.map((m, i) => (
+                    <tr key={m.year} className={cn("border-b border-border last:border-0", i % 2 === 0 ? "" : "bg-secondary/10")}>
+                      <td className="px-5 py-2.5 font-medium">Year {m.year}</td>
+                      <td className="px-5 py-2.5 text-right tabular-nums text-destructive">{formatCurrency(m.p10, "GBP")}</td>
+                      <td className="px-5 py-2.5 text-right tabular-nums font-semibold">{formatCurrency(m.p50, "GBP")}</td>
+                      <td className="px-5 py-2.5 text-right tabular-nums text-success">{formatCurrency(m.p90, "GBP")}</td>
+                    </tr>
+                  ))}
+                </tbody>
+              </table>
+            </div>
+          )}
         </>
       )}