Can Algorithms Really Adapt to Market Rhythm?

When you build and backtest algorithms, do you assume the same logic applies across all markets, crypto, stocks, or tokenized assets?

I have relied on algo trading for most of my crypto strategies, but recently I started experimenting on Bitget, trading stock-linked pairs like NVDAUSDT and TSLAUSDT to see how the systems perform outside pure crypto volatility.

What stood out to me was how event-driven volume on Bitget, especially during active stock futures phases, quietly shifted execution dynamics. Liquidity often deepened, yet fills became less predictable as bursts of short-term bidding emerged. It revealed how timing and volume patterns can throw off even the best backtests.

It makes me wonder, when we backtest, are we accounting for how live exchange conditions actually change? Markets aren’t static, they react to participation, incentives, and sentiment. On Bitget, that’s especially visible when activity spikes around specific stock futures events.

A solid algorithm doesn’t just run code, it adapts to market texture. And platforms like Bitget, with their blend of crypto and stock futures, are great testing grounds for seeing just how far that adaptability can go.