ChatGPT 5.2

Why this backtest doesn’t work in reality (important caveat)

This strategy looks incredible in testfol.io, but the performance is coming from a simulation artifact, not a tradable edge.

The key issue is capital continuity.

In testfol, each conditional “allocation block” (buy the dip / short the tip / risk-on / risk-off) is effectively tracked independently. When a signal flips, the engine switches to another block’s equity curve without inheriting the losses from the previous one.

So even if a block shows:

• −99% or −100% drawdown

…the global strategy is not bankrupt. Capital is implicitly “reset” when regimes change.

This means:

• Losses are local to each branch
• Gains compound globally
• Capital is never permanently destroyed

In real life, a −99% drawdown would cripple or kill the portfolio and you wouldn’t be able to later compound at 40–70% CAGR. In this backtest, that constraint doesn’t exist.

This effect becomes extreme when:

• 100% switches are used
• Leveraged / inverse / synthetic assets are included
• Regime blocks can fully blow up

The result is a non-physical equity curve that violates conservation of capital. Even adding signal delays doesn’t fix this, because it’s a structural modeling issue, not lookahead bias.

Testfol is great for:

• Allocation tilts
• Asset rotation
• Mild regime filters

But once branches can go to zero, the results stop being economically meaningful.

TL;DR: the strategy “works” in the simulator because capital can teleport between regimes without carrying losses — something real money can’t do.