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>
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3 changed files with 56 additions and 3 deletions
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@ -100,14 +100,15 @@ def run_monte_carlo(
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for sim in range(n_sims):
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asset_values = current_values.copy()
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for t in range(n_months):
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# Add monthly contribution before GBM step so it compounds
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if monthly_contribution > 0:
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asset_values = asset_values + weights * monthly_contribution
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Z = rng.standard_normal(n_assets)
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corr_Z = L @ Z
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# GBM step for each asset
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asset_values = asset_values * np.exp(
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(mus - 0.5 * sigmas ** 2) * DT + sigmas * np.sqrt(DT) * corr_Z
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)
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port_val = float(asset_values.sum()) + monthly_contribution * (t + 1)
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portfolio_paths[sim, t] = max(0.0, port_val)
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portfolio_paths[sim, t] = max(0.0, float(asset_values.sum()))
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# Compute percentile paths
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pcts = {
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@ -122,6 +123,18 @@ def run_monte_carlo(
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prob_gain = float(np.mean(final_values > total_value))
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expected_value = float(np.median(final_values))
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# Year-by-year milestones (at each 12-month boundary)
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milestones = []
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for yr in range(1, years + 1):
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idx = min(yr * 12 - 1, n_months - 1)
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milestones.append({
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"year": yr,
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"date": future_dates[idx],
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"p10": round(float(np.percentile(portfolio_paths[:, idx], 10)), 2),
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"p50": round(float(np.percentile(portfolio_paths[:, idx], 50)), 2),
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"p90": round(float(np.percentile(portfolio_paths[:, idx], 90)), 2),
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})
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return {
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"dates": future_dates,
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"percentiles": {
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@ -131,5 +144,6 @@ def run_monte_carlo(
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"current_value": round(total_value, 2),
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"expected_value": round(expected_value, 2),
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"probability_of_gain": round(prob_gain, 3),
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"milestones": milestones,
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"insufficient_data": False,
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}
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@ -28,6 +28,14 @@ export interface PercentilePath {
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value: number;
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}
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export interface MonteCarloMilestone {
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year: number;
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date: string;
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p10: number;
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p50: number;
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p90: number;
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}
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export interface MonteCarloResponse {
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dates: string[];
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percentiles: {
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@ -40,6 +48,7 @@ export interface MonteCarloResponse {
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current_value: number;
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expected_value: number;
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probability_of_gain: number;
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milestones: MonteCarloMilestone[];
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insufficient_data: boolean;
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}
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@ -525,6 +525,36 @@ function MonteCarloTab() {
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style={{ width: "100%", height: "360px" }}
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/>
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</div>
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{/* Year-by-year milestone table */}
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{data.milestones?.length > 0 && (
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<div className="bg-card border border-border rounded-xl overflow-hidden">
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<div className="px-5 py-3 border-b border-border">
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<p className="text-sm font-semibold">Year-by-Year Projections</p>
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<p className="text-xs text-muted-foreground mt-0.5">P10 = pessimistic · P50 = median · P90 = optimistic</p>
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</div>
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<table className="w-full text-sm">
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<thead>
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<tr className="border-b border-border bg-secondary/30">
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<th className="text-left px-5 py-2 text-xs text-muted-foreground font-medium">Year</th>
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<th className="text-right px-5 py-2 text-xs text-destructive font-medium">P10</th>
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<th className="text-right px-5 py-2 text-xs text-foreground font-medium">P50</th>
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<th className="text-right px-5 py-2 text-xs text-success font-medium">P90</th>
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</tr>
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</thead>
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<tbody>
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{data.milestones.map((m, i) => (
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<tr key={m.year} className={cn("border-b border-border last:border-0", i % 2 === 0 ? "" : "bg-secondary/10")}>
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<td className="px-5 py-2.5 font-medium">Year {m.year}</td>
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<td className="px-5 py-2.5 text-right tabular-nums text-destructive">{formatCurrency(m.p10, "GBP")}</td>
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<td className="px-5 py-2.5 text-right tabular-nums font-semibold">{formatCurrency(m.p50, "GBP")}</td>
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<td className="px-5 py-2.5 text-right tabular-nums text-success">{formatCurrency(m.p90, "GBP")}</td>
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</tr>
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))}
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</tbody>
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</table>
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</div>
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)}
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</>
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)}
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