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Current Target Portfolio
The latest policy-generated holdings for the open quarter, with target weights normalized to 100%.
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| Rank | Symbol | Target Weight | ELO Rank | Price Date | Price |
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Company Rankings by Fundamental Analysis
Filter the universe, save candidates to your shortlist, and open company-level fundamentals in one flow.
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Method: ELO
Historical Strategy Analysis (Sharpe Ratio)
The Magic Compounding Effect
Time in the market trumps timing the market. This analysis reveals that holding period matters far more than chasing the highest returns—a modest but consistent return held over decades will dramatically outperform higher returns over shorter periods. The mathematics of compounding show that patience and discipline are the most powerful wealth-building tools, not exceptional stock-picking ability.
Portfolio Performance
How to read this
What this shows
How long (days) and how deep (money) the portfolio stayed below its previous all-time high.
How to read
Touching the x-axis means a new peak; below it means the portfolio is still recovering.
Why it matters
Longer/deeper drawdowns usually feel worse and can force bad decisions (panic selling). It’s a practical way to compare “pain” across periods/strategies.
Caveats
A long drawdown doesn’t guarantee a rebound soon; markets can stay weak longer than expected.
How to read this
What this shows
The % return needed to get back to the previous all-time high from today’s level.
How to read
Bigger drawdowns need disproportionately bigger gains to recover (e.g., -50% needs +100%).
Why it matters
Good companies selling at a discount can provide a margin of safety.
Caveats
This is arithmetic, not a forecast. Prices can keep falling, and some companies never recover.
How to read this
What this shows
A 0–100 “how bad is the drawdown?” score vs. that same company’s own history. 100 = near its peak (no drawdown), 0 = near its worst historical drawdown.
How to read
Lower = more stretched vs. history. Use it to compare “stress level” across stocks on the same scale.
Why it matters
If fundamentals are intact, deep drawdowns can sometimes offer better entry points than buying near peaks.
Caveats
“Cheap” can get cheaper. A low score is not a buy signal by itself—bad businesses can stay down or go to zero.
How to read this
What this shows
A rule-based “oversold” indicator. It measures sustained downside pressure and highlights moments where past data suggests the odds of a rebound improved.
How to read
Treat highlighted points as “check this stock now”, not an automatic buy. Combine with fundamentals and position sizing.
Why it matters
Removes emotion: it gives consistent criteria for when to look for entries during sell-offs.
Caveats
Signals can be early and keep triggering in a downtrend. Always assume the next signal can fail.
How to read this
What this shows
One score that combines multiple signals (returns, drawdowns, and time effects). Higher = “better overall” across the metrics used.
How to read
Higher composite → stronger multi-metric profile.
Why it matters
A single metric can be misleading. This helps balance “high return” against “high risk” in one view.
Caveats
The score depends on how each metric is weighted. Treat it as a ranking tool, not a guarantee.
US Stock Market Index CAGR Statistics
How to read this
What this shows
A benchmark CAGR snapshot for an S&P 500 UCITS tracker (in EUR). Think of it as “what a simple index investment delivered”.
How to read
Higher CAGR is better, but also look for consistency across years (big swings usually mean higher uncertainty).
Why it matters
Use it to sanity-check your strategy: if a strategy can’t beat the index after costs, it’s often not worth the extra complexity.
Caveats
Past performance ≠ future returns. Also, results depend on currency (EUR vs USD) and the chosen time window.
ELO Rating Strategy Returns Backtest
How to read this
What this shows
Cumulative return across staggered start dates (offsets 1–8) by investing in companies with different rank threshold.
How to read
Look for curves that remain above baseline across offsets.
Why it matters
If ranking is effective, higher rated companies should beat the average and the index.
Caveats
Trading costs/slippage depend on your backtest setup.
