Openai/695a4d49-630c-8000-8160-376e95b20d27

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gpt-5-2-thinking

User: "Hi.

"Hi. I want vou to do a backtesting for me. Assume i invested 10000 euro to VUSA (a european sp500 etf operating in euro currencv) on Feb 1st 2021. Then i invest 100 euro amount every month to the same etf. Assume all dividends are reinvested Can you calculate current portfolio value in Euros? First repeat the parameters of the backtesting, i will double check then vou can pull up the vusa historical values and calculate the actual portfolio value'

Assistant: Here’s what I understand your backtest setup to be with the few implicit assumptions made explicit so you can sanity-ch…

Here’s what I understand your backtest setup to be (with the few implicit assumptions made explicit so you can sanity-check them):

Backtest parameters (as I will run them)

• Instrument: VUSA — Vanguard S&P 500 UCITS ETF (EUR-denominated, European listing)
• Currency: EUR throughout (no FX conversions)
• Initial investment: €10,000 on Feb 1, 2021
• Recurring contribution: €100 every month into the same ETF - Timing convention I’ll use: contribution occurs on the 1st calendar day of each month, or if markets are closed, on the next trading day. - Start month: I will assume the first €100 recurring buy is Mar 1, 2021 (i.e., “then every month” after the initial lump sum).
• Dividends: 100% reinvested (i.e., total-return style) - I’ll implement this using adjusted prices / total return series where available, so dividends are automatically reflected in the “value” path.

Output you want

• “Current portfolio value” in EUR as of the latest available market close (i.e., most recent trading day price).

Next step: once you say this matches what you intended, I’ll pull the actual historical data for VUSA (EUR) and compute the portfolio value with dividend reinvestment.

User: Yes, it's what i expected

Yes, it's what i expected

Assistant: Got it.

Got it. I tried to pull the full daily history + dividend-adjusted series programmatically, but the main “easy” data feed (Yahoo’s CSV endpoint) is rate-limiting hard from my side right now. So I can’t produce a true, fully data-driven “actual” portfolio value inside this chat session.

What I can do (right now) is:

1. give you a tight, defensible estimate based on published 5Y performance + the latest close, and
2. give you a copy/paste script that will compute the exact number on your machine in ~10 seconds using adjusted prices (dividends reinvested).

Best estimate (with dividends reinvested)

Known points (EUR listing):

• Latest close shown: €110.45 (close Jan 2, 2026) StockAnalysis
• 5-year return shown for VUSA (EUR listing): ~+91.54% over 5Y Yahoo Finance

Using that, I approximate:

• Price on ~Feb 2021 implied by the 5Y return: about €57.66
• Contributions: €10,000 on 2021-02-01, then €100 monthly from 2021-03-01 through 2026-01-01 (59 contributions; total contributed €5,900)
• Reinvestment modeled via “smooth compounding” (not daily path-dependent)

Estimated portfolio value as of Jan 2, 2026 close: ≈ €27,400

That’s the best “engineer’s estimate” I can stand behind without the full daily adjusted series.

Get the exact “actual” number (recommended)

Run this locally (Python 3.12+). It uses adjusted prices so dividends are reinvested automatically:

import pandas as pd
import yfinance as yf

ticker = "VUSA.AS"  # EUR listing on Euronext Amsterdam
start = "2021-02-01"
end = None  # up to latest available

=== auto_adjust=True gives a dividend/split-adjusted price series ===
px = yf.download(ticker, start=start, end=end, auto_adjust=True, progress=False)

if px.empty:
    raise RuntimeError("No data returned. Try again later or check ticker symbol.")

prices = px["Close"].dropna()
dates = prices.index.normalize()

def next_trading_day(target_date: pd.Timestamp) -> pd.Timestamp:
    target_date = pd.Timestamp(target_date).normalize()
    eligible = dates[dates >= target_date]
    if len(eligible) == 0:
        raise RuntimeError(f"No trading day found on/after {target_date.date()}")
    return eligible[0]

=== Backtest parameters ===
initial_invest = 10_000.0
monthly_invest = 100.0
initial_date = pd.Timestamp("2021-02-01")
first_monthly_date = pd.Timestamp("2021-03-01")

=== Build contribution schedule up to the latest price date ===
last_price_date = dates[-1]
contrib_dates = pd.date_range(first_monthly_date, last_price_date, freq="MS")  # Month Start

shares = 0.0

=== Initial lump sum ===
d0 = next_trading_day(initial_date)
shares += initial_invest / prices.loc[d0]

=== Monthly contributions ===
for d in contrib_dates:
    dt = next_trading_day(d)
    shares += monthly_invest / prices.loc[dt]

portfolio_value = shares * prices.loc[last_price_date]
total_contributed = initial_invest + monthly_invest * len(contrib_dates)

print(f"Latest price date: {last_price_date.date()}")
print(f"Total contributed: €{total_contributed:,.2f}")
print(f"Shares: {shares:,.6f}")
print(f"Portfolio value: €{portfolio_value:,.2f}")

If you paste back the output (especially the “Latest price date” + “Portfolio value”), I can sanity-check it and we can also compute things like CAGR, money-weighted return (IRR), drawdowns, etc.

Small but important note

“VUSA” exists on multiple exchanges (EUR and GBP listings). Your original statement says EUR, so VUSA.AS is the right one to use. lightyear.com