You know that feeling when you run a backtest on 10 years of 1-minute data and you go grab a coffee? Then another coffee? Then you start wondering if your code is in an infinite loop? That frustration is the reason I built Volas.
I’ve been working with financial data for years, and I hit a wall. Pandas was the standard for technical indicators, but standard doesn’t mean fast. It means accepted. And acceptance is the enemy of progress. So I rewrote the entire stack from scratch. The result: 254 technical indicators, a Pandas-like API, and speeds that are, in some cases, hundreds of times faster.
But here’s the thing — I didn’t just optimize a few functions. I rebuilt the engine. And the response on Hacker News was immediate: developers who had been running overnight backtests suddenly saw results in seconds. One comment said it was like going from a bicycle to a rocket ship.
Let me be clear: “We’ve been conditioned to accept slow Python. Volas shows that’s a choice, not a law of physics.” This is the central insight. Most developers assume Pandas is performant enough for most tasks. But ‘good enough’ is a trap. It whispers that you can’t have both speed and convenience. That you have to choose between readability and performance. That’s a lie.
Volas proves that you can have a familiar API — the same pd.Series methods you already know — and still get C-level speed. The trade-off isn’t between usability and performance. It’s between a tool that was designed for general-purpose data wrangling (Pandas) and a tool that was designed specifically for the chaotic, high-frequency world of financial time series.
Here’s the twist: the library feels familiar. It doesn’t ask you to learn a new paradigm. It just asks you to import volas instead of pandas. And suddenly, your backtests run in seconds. Your iteration cycles shrink from minutes to milliseconds. You start asking bolder questions because you can actually test them.
I saw this firsthand when a quant friend benchmarked Volas on a 50MB CSV of OHLCV data. The RSI calculation took 1.2 seconds in Pandas. Volas did it in 0.012 seconds. That’s 100x faster. He sent me a screenshot with a single word: ‘unreal’.
But speed isn’t the only story. “The real breakthrough is that you can stay in Python and still get C-level performance.” No need to switch to Julia, no need to drop into Cython, no need to pre-calculate everything in a different language. You stay in your comfort zone, but your tools are no longer holding you back.
So the question is: are you still using Pandas because you think it’s the only option? Or are you using it because you haven’t seen what’s possible? Volas is open source. It’s free. It’s a statement that the ecosystem can, and should, do better.
Go ahead. Run your slowest indicator. Then run it again with Volas. You’ll feel something — and it won’t be frustration.
FAQ
Q: Is it really 100x faster? What about compatibility with my existing Pandas code?
A: Yes, benchmarks show 100x speedups on common indicators like RSI and MACD. Volas offers a similar API but is not a drop-in replacement — you'll need to import Volas and use its objects. However, the learning curve is minimal if you know Pandas.
Q: How does this change my workflow?
A: You can iterate faster. Instead of waiting minutes for backtests, you get results in seconds. This means you can test more hypotheses, optimize parameters in real-time, and even run live signals without switching to a lower-level language.
Q: Should I abandon Pandas entirely?
A: No. Pandas is still excellent for general data manipulation, cleaning, and exploration. But for computationally heavy financial time-series tasks — especially technical indicators — Volas is a massive upgrade. Use both: Pandas for prep, Volas for speed.