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


{{DISPLAYTITLE:Backtesting Portfolio Value}}

''This conversation was exported from ChatGPT''<ref>{{cite web|title=Backtesting Portfolio Value|url=https://chatgpt.com/share/695a4d49-630c-8000-8160-376e95b20d27|publisher=ChatGPT|access-date=2026-01-05}}</ref>

== 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:
# give you a tight, defensible estimate based on published 5Y performance + the latest close, and
# 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<ref>{{cite web|title=StockAnalysis|url=https://stockanalysis.com/quote/ams/VUSA/|publisher=stockanalysis.com|access-date=2026-01-05}}</ref>
* 5-year return shown for VUSA (EUR listing): ~+91.54% over 5Y Yahoo Finance<ref>{{cite web|title=Yahoo Finance|url=https://ca.finance.yahoo.com/quote/VUSA.AS/|publisher=Yahoo Finance|access-date=2026-01-05}}</ref>

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:

<syntaxhighlight lang="python">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}")

</syntaxhighlight>

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<ref>{{cite web|title=lightyear.com|url=https://lightyear.com/en-eu/tools/compare/VUSA%3AAEX-vs-VUSA%3ALSE|publisher=lightyear.com|access-date=2026-01-05}}</ref>

== References ==
<references />


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