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Crypto Trading-Strategie-Guides
Ausführliche Guides zu Backtesting, Walk-Forward-Optimierung, Smart-Order-Laddern, Position Sizing und dem Rest des Quant-Werkzeugkastens — geschrieben für Retail-Trader, die die langweilige Wahrheit statt Marketing-Sprech wollen.
Empfehlung der Redaktion
Den kompletten Guide zu Krypto-Trading-Strategien lesen
The HalfTrend indicator, explained — and what its latch is actually worth, in numbers
HalfTrend's whole idea is refusing to flip until price proves it. We explain the latch mechanic, then measure what it buys you against Parabolic SAR and SuperTrend on 50 coins of real history — including the part none of them were fitted to.
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Guide
Praktische Anleitungen — Sizing, Ausstiege, Timeframes wählen, Strategien Schritt für Schritt bauen.
- Guide·9 Min. Lesezeit
Volatility targeting: how professional desks keep risk constant
Fixed dollar size ignores the obvious: a $10,000 position in BTC has 5× the dollar volatility of a $10,000 position in gold. Vol-targeting scales the position so the dollar risk stays constant — and it's how every serious systematic shop sizes trades.
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- Guide·8 Min. Lesezeit
How to backtest a strategy on Binance (fees, data, and pairs)
Backtesting for Binance means testing against Binance's real fees, real pairs, and real data — not a generic simulation. Here's how to make the test match the venue.
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- Guide·7 Min. Lesezeit
Trendlines, drawn properly: two-touch rule, slope, and why most are wrong
A trendline is the simplest tool on the chart and the easiest to lie with. Here is how to draw one that means something — and the discipline that keeps it from becoming a Rorschach test.
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- Guide·8 Min. Lesezeit
What timeframe should you trade? The honest answer
There is no best timeframe — but there is a best timeframe for you. The choice is a trade-off between noise, costs, and how much of your life trading will consume.
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- Guide·8 Min. Lesezeit
Diversification for traders: running many strategies, not many coins
Holding ten crypto assets that all crash together is not diversification. For an algo trader, real diversification comes from uncorrelated strategies — here's why.
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- Guide·10 Min. Lesezeit
Algorithmische Trading-Strategien: die wichtigsten Typen, ehrlich bewertet
Eine Tour durch die Strategiefamilien, die Retail-Algo-Trader tatsächlich nutzen — worauf jede setzt, welchen Markt sie braucht und wie schwer es wirklich ist, sie zum Laufen zu bringen.
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Konzept
Eine Idee pro Artikel — ein Indikator, eine Kennzahl oder ein Stück Theorie, klar erklärt.
- Konzept·7 Min. Lesezeit
Does the best backtest predict the future? We measured it 200 times
When you optimize a strategy and ship the best-Sharpe settings, you're betting the in-sample ranking of your parameters holds up out-of-sample. Across 200 strategy-coin grids, that ranking correlated just 0.37 with reality — and a quarter of the time it was no better than random.
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- Konzept·8 Min. Lesezeit
Buy-and-hold vs trading crypto: what the data actually says
HODL maximalists quote the returns. Traders quote the drawdowns. Both are cherry-picking halves of the same dataset — so let's put the whole thing on the table.
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- Konzept·7 Min. Lesezeit
How many trades does a backtest need before you can trust it?
Fifteen trades tell you almost nothing, no matter how many years they span. Here is the honest arithmetic of backtest sample sizes — and why trade count beats calendar length every time.
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- Konzept·7 Min. Lesezeit
R-multiples: the only way to compare trades that actually scales
Was that +$80 trade good? You cannot tell from dollars alone. Expressed as R-multiples — units of initial risk — every trade gets the same currency, and your strategy's quality becomes measurable.
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- Konzept·6 Min. Lesezeit
Heikin-Ashi-Kerzen: glattere Charts, mit einem entscheidenden Haken
Heikin-Ashi-Kerzen glätten das Chart-Rauschen, sodass Trends förmlich vom Bildschirm springen. Sie machen Trendfolge intuitiv — und führen jeden still in die Irre, der den angezeigten Preis für real hält.
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- Konzept·7 Min. Lesezeit
Donchian channels: the breakout indicator the Turtles got rich on
Two lines — the N-bar high and the N-bar low — and a rule to enter on breakouts. That's a Donchian channel. The system trained the most famous prop-trading class in history. Here is how it works and where it breaks.
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Deep-Dive
Lange, pointierte Artikel darüber, was Retail-Strategien wirklich killt — und wie man überlebt.
- Deep-Dive·6 Min. Lesezeit
Parabolic SAR vs SuperTrend: 50 coins, five and a half years, one honest scoreboard
Both flip between long and short with a trailing line — but one of them trades four times as often. We ran both through the same engine on identical data, with fees, and scored the part of history neither was fitted to.
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- Deep-Dive·8 Min. Lesezeit
Which crypto strategy families survive out-of-sample? All 10, ranked
We tuned ten classic strategy families on twenty coins, then ran each tuned winner once on data it had never seen. Only two families kept a positive median edge. Here is the full ranking — including the three that were negative before fees even bit.
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- Deep-Dive·6 Min. Lesezeit
SuperTrend vs EMA crossover: we tested both on 50 coins so you don't have to argue about it
Two of the most popular trend-following systems, identical conditions, fifty crypto pairs, an out-of-sample honesty check — and a scoreboard that isn't close this month.
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- Deep-Dive·10 Min. Lesezeit
Wir haben 11,440 Backtests laufen lassen. Die durchschnittliche 'Gewinner'-Strategie verlor die Hälfte ihres Edge auf Daten, die sie nie gesehen hatte
Zehn klassische Strategiefamilien, 5,720 Parameterkonfigurationen, zwanzig Krypto-Paare, eine ehrliche Regel: nur auf der Vergangenheit tunen, dann genau einmal auf Daten testen, die die Strategie nie gesehen hat. Hier steht, wie viel von der 'gebacktesteten Profitabilität' übrig blieb.
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- Deep-Dive·11 Min. Lesezeit
CPCV: the cross-validation method that catches overfitting walk-forward misses
Walk-forward optimisation gives you one out-of-sample equity curve. Combinatorial purged cross-validation gives you hundreds — and shows you the distribution of your strategy's true performance, not the lucky-path artefact.
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- Deep-Dive·8 Min. Lesezeit
Backtest data quality: the bad candles that fake an edge
Your backtest is only as honest as the candles you feed it. Bad ticks, gaps, and survivorship bias quietly fabricate edges — or hide real ones. Here's how to spot them.
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Walkthrough
Schritt-für-Schritt-Touren durch die Plattform — Features, Screens und die dahinterliegenden Workflows.
- Walkthrough·8 Min. Lesezeit
How to backtest an ATR strategy on crypto (stops and sizing that adapt)
ATR isn't an entry signal — it's a volatility measure. The right way to backtest it is as the engine behind your stops and position sizing. Here's how, step by step.
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- Walkthrough·8 Min. Lesezeit
How to backtest a Donchian breakout strategy on crypto
The channel breakout that powered the original Turtle traders is simple to state and easy to overfit. Here's how to backtest it honestly, from rules to walk-forward.
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- Walkthrough·8 Min. Lesezeit
How to backtest a VWAP strategy on crypto (filter, not trigger)
VWAP is a fair-value benchmark, so the strategies worth backtesting use it as a filter or a reversion reference — not a crossover signal. Here's how to test one honestly.
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- Walkthrough·8 Min. Lesezeit
Momentum trading strategy: how to backtest one on crypto
Momentum is the simplest edge in markets — buy what's going up — and one of the easiest to ruin by buying the exact top. Here's how to backtest it honestly.
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- Walkthrough·8 Min. Lesezeit
How to backtest an ADX trend-strength strategy on crypto
ADX measures how strong a trend is, not its direction. Learn to turn that into testable rules, run it on real crypto history, and validate the edge with walk-forward.
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- Walkthrough·8 Min. Lesezeit
How to backtest a Bollinger Bands strategy on crypto
Turn a Bollinger Bands idea — mean-reversion fade or volatility breakout — into testable rules, run it on real crypto history, and tell whether the edge holds up.
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