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Synonyme/Zusammenhängende Konzepte: Handel mit künstlicher Intelligenz, algorithmischer Handel, automatisierter Handel, Robo-Advisor für Finanzen, quantitativer Handel #Quant

I Built a Trading Signal Using “Quantum Physics” Math — Here’s What Actually $BTC
A few weeks ago, I went down a rabbit hole trying to apply Bloch sphere math (used for quantum bits) to crypto price action. Sounds crazy — and it mostly is — but there was a useful idea hidden inside. Fixing what was broken taught me more about signal design than anything else this year.
Here’s the honest breakdown:
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The Idea
In quantum mechanics, a qubit isn’t just 0 or 1 — it’s a probability mix, represented on a sphere. The angle (θ) determines the probability:
p = cos²(θ/2)
I thought: what if “p” = probability price goes up?
Then:
- θ shrinking → bullish (probability rising)
- θ growing → bearish
So instead of raw price, I track how this “probability state” rotates.
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What Was Broken (and Fixes)
1) Bad normalization
The first version assumed fixed % moves (like +6%) meant “strong signal.” That doesn’t work across different coins or volatility conditions.
Fix: normalize using volatility (z-score), then map with a sigmoid:
z = momentum / rolling_std
p = 1 / (1 + exp(-z))
Now signals are consistent across assets.
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2) Signal combination blew up
I tried “quantum interference” by adding amplitudes:
sqrt(p1) + sqrt(p2) → squared
Problem: values > 1 → clipped → signal becomes flat when strongest.
Fix: use geometric mean instead:
p = sqrt(p1 * p2)
Now:
- stays between 0 and 1
- only rises when both signals agree
- behaves cleanly
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3) No regime filter
Momentum signals fail in sideways markets → constant false entries.
Fix:
- Only trade if higher timeframe is trending
- Example: price above 200 EMA + ADX > 20
This single filter removed most bad trades.
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What This Signal Really Is
Strip away the “quantum” branding and it’s:
→ A dual-timeframe momentum acceleration signal
→ With adaptive normalization
→ And proper regime filtering
In simple terms:
It detects when price is not just rising — but accelerating upward.
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Key Lessons
1) Normalize adaptively, not with fixed numbers
(Use volatility, not arbitrary thresholds)
2) When combining signals, test edge cases
(What happens when both are strong?)
3) The real edge is filtering the market regime
(Not the fancy entry signal)
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Does It Work?
- Works okay in trending markets
- Average in choppy ones (as expected)
- Too early to judge live performance
Honest answer: I don’t know yet.
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Final Thought
Most “alpha” isn’t magic — it’s just cleaning up bad math:
- proper normalization
- no overflow errors
- correct signal combination
- filtering noise
Once you fix those, you find out if there was ever a real edge.
Sometimes there is.
Sometimes it’s just RSI wearing a Halloween costume.
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Trade carefully. Not financial advice.
$BTC
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