This portfolio is dominated by a single crypto holding, with iShares Bitcoin ETP making up 57% of the weight. Around a third of the portfolio then sits in higher-octane strategies: a 2x leveraged global equity ETF and a managed futures ETF. The remaining slice is in two equity funds focused on small-cap value and high dividends. This structure leans hard into growth and return-seeking assets, with very little in lower-volatility instruments. With only about 11 months of history, any patterns seen so far may reflect short-term market moves rather than stable long-term behaviour, so the composition tells more about risk appetite than proven results.
Over this roughly 11‑month period, a hypothetical €1,000 invested in the portfolio fell to about €889, giving a Compound Annual Growth Rate (CAGR) of -12.5%. CAGR is like an “average yearly speed” over the journey. Over the same window, both the US and global equity benchmarks showed strong double‑digit positive CAGRs, so the portfolio lagged meaningfully. The max drawdown, or worst peak‑to‑trough drop, was about -24%, and that fall has not yet been fully recovered. With such a short track record and dominant exposure to bitcoin and leverage, it’s hard to treat this as a reliable guide to long‑term behaviour; it mainly shows how sharp short-term swings can be.
The Monte Carlo projection uses past monthly returns to simulate many possible future paths, like rolling dice 1,000 times to see a range of outcomes. Here, the median 15‑year outcome for €1,000 lands around €2,030, but the spread is huge: about €245 at the low end (5th percentile) and nearly €17,951 at the high end (95th percentile). That wide fan reflects the high volatility of bitcoin and leverage in such a short input sample. The model also assumes that return patterns from this brief period repeat, which is a big stretch. So, the projection is more a stress‑testing tool for “what could happen” than a dependable forecast of “what will happen.”
By asset class, crypto clearly dominates at 57%, with smaller portions in stocks (16%) and bonds (10%), and 17% tagged as “No data.” That large crypto slice means a big chunk of risk and return is driven by a single, very volatile asset class rather than a mix of traditional holdings. In many broad equity benchmarks, crypto is effectively 0%, so this is a major tilt away from typical market structures. The presence of bonds is relatively modest and likely doesn’t offset the swings from bitcoin and leveraged equity. Given the limited history, it’s especially important to see the asset mix as setting up potential volatility, not as something proven to work over multiple cycles.
This breakdown covers the equity portion of your portfolio only.
Sector data shows 57% of the portfolio classified as “Crypto,” with small slices across several traditional sectors such as financials, consumer discretionary, energy, and others, each only a few percent or less. This is very different from a typical diversified equity index, where sectors are more evenly spread and none dominates to this extent. When a single category, like crypto here, dominates the sector view, portfolio returns will often move more with that category’s specific news and sentiment than with broad economic trends. Over just 11 months, it’s difficult to judge how this blend behaves across different environments, but the sector breakdown clearly signals that crypto is the main driver.
This breakdown covers the equity portion of your portfolio only.
The geographic data captures only a small slice of the portfolio: around 7% in North America, 3% in developed Europe, and 1% in Japan. The majority of the portfolio’s exposure, especially the bitcoin component, doesn’t slot neatly into a country bucket in this framework. Compared to common global equity benchmarks, which usually spread across many regions, this recorded geographic diversification is relatively limited. In practice, crypto and some of the funds may still be influenced by global economic conditions, but the look‑through data suggests the portfolio isn’t closely anchored to a classic regional mix. With less than a year of history, it’s hard to see how geographic shocks would feed through.
This breakdown covers the equity portion of your portfolio only.
By market capitalization, the mapped equity slice leans toward the smaller end of the spectrum: about 4% in small caps and 3% in micro caps, with only a few percent combined in large and mega caps, plus a small mid‑cap portion. Smaller companies often have higher volatility and more idiosyncratic risk than large, established firms. On top of that, the majority crypto exposure sits outside this framework but tends to be at least as volatile as smaller stocks. Compared with broad equity indices, which are heavily large‑ and mega‑cap, this structure suggests a tilt toward more “lively” parts of the market. Given the short dataset, long‑term behaviour of this tilt can’t yet be observed robustly.
This breakdown covers the equity portion of your portfolio only.
Look‑through data for the ETFs is extremely limited: only about 0.6% of the portfolio is covered via top‑10 holdings, leaving over 99% of underlying positions unobserved. The names that do show up are mainly individual small or mid‑size companies, each representing roughly 0.05–0.07% of the portfolio. Because coverage is so low and only top‑10 holdings are used, any overlap between ETFs is almost certainly understated. This means hidden concentration in particular stocks or themes could exist without appearing in the data. With such incomplete visibility and only 11 months of performance, conclusions about underlying company‑level diversification need to be treated as tentative, not definitive.
Risk contribution shows how much each holding drives total portfolio ups and downs, which can be very different from its weight. Here, the bitcoin ETP is 57% of the portfolio but contributes about 88% of overall risk, a risk‑to‑weight ratio of 1.55. The next two holdings together add around 10% of risk, and the top three positions contribute over 98% of total risk. That means the last two ETFs barely move the needle in terms of volatility. When one position dominates risk like this, the portfolio’s experience will mostly track that single asset’s swings. With less than a year of data, the exact percentages may shift over time, but the current dominance of bitcoin is clear.
This chart shows the Efficient Frontier, calculated using your current assets with different allocation combinations. It highlights the best balance between risk and return based on historical data. "Efficient" portfolios maximize returns for a given risk or minimize risk for a given return. Portfolios below the curve are less efficient. This is informational and not a recommendation to buy or sell any assets.
Click on the colored dots to explore allocations.
The risk vs. return chart compares this portfolio to an “efficient frontier,” which shows the best possible return for each risk level using the same holdings with different weights. The current portfolio sits well below that curve, with a negative Sharpe ratio of -0.64, meaning it delivered less return than cash after adjusting for volatility over the sample period. In contrast, both the maximum‑Sharpe and minimum‑variance mixes show much higher Sharpe ratios and lower risk, at least within this dataset. This suggests that, historically, different weightings of the existing assets could have produced a better balance of risk and return. Because the history is only 11 months, these optimization results are informative but far from definitive.
The portfolio’s average ongoing fee, or Total Expense Ratio (TER), is about 0.24% per year, which is relatively low given the mix of specialized funds. TER is like a small annual toll taken from fund assets to cover management and operating costs. The cheapest holding is the bitcoin ETP at 0.15%, while the leveraged ETF is the priciest at 0.60%, but it’s a smaller portion of the portfolio. Over long periods, even small fee differences can compound, so keeping the overall TER modest supports better net returns. With only 11 months of live performance, it’s too early to see the full compounding effect, but the cost structure is a positive starting point.
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