Test copy-paste

Madjik Team

• 1 min read

💡

Hypothesis HY10019

Every trading model that looks great in backtests fails in live trading. Past performance doesn't predict future results - especially in crypto where market structure changes constantly.

Trading hypothesis

What traders get wrong

False assumption:

"My model has great backtested returns."

Truth:

Back-tested models don't work because they're optimized for past conditions, regimes change, and survivorship bias misleads.

Problem for trader:

That 500% backtested return means nothing. Models overfit. Edge decays.

Key takeaways

What you should consider as a trader

  1. Overfitting is the norm - Any model can fit historical data.
  2. Crypto regime changes are frequent - 2021 model fails in 2022.
  3. LLMs are backward-looking - They don't know current conditions.
  4. Alpha decays - Once discovered, edges disappear.
  5. Real-time adaptation is essential - Static models fail.

Data you need

"Better to be approximately right than precisely wrong"

Build adaptive models:

Data points:

  • Regime indicator
  • Model performance decay
  • Out-of-sample performance
  • Structural break detection

Comparison of data sources

Where to get crucial data feeds

ChatGPT/Claude

Data available: ❌ No

Knowledge cutoffs, no real-time data.

QuantConnect

Data available: ⚠️ Partial

Backtesting only.

Madjik

Data available: ✅ Yes

Get access to this data immediately.

Use yourself, or pass on to your software engineer. Or to your AI model - or to your boss.

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Science behind hypothesis

Research supports this hypothesis

Research shows ML models overfit to backtests and fail in production.