We love IPython here at Quantopian - it's one of our regular tools. And we agree it's good for data exploration.
We think of algo writing as happening in roughly three steps (roughly!):
- Data exploration. You've got a pile of data and maybe an idea. You slice and dice the data, trying to suss the idea out of the data, trying to find the way to cleave the data in a way that you can say "ah ha! there it is!" Many ideas die in this stage - they just don't hold up to the data exploration. This open-ended data exploration is great for tools like iPython.
- Code and test. You've got your idea, you know how to articulate it, so you do so in Python. You test it in the backtester and make sure the data insight you had holds up in market simulation. You do out-of-sample testing and walk-forward testing to be sure. Many ideas die in this stage, too - maybe the underlying situation no longer exists, or maybe there is some real-world reason that keeps you from making it run.
- Trade it. You've tested it as well as you've could, so you put your money behind the algo and let it trade.
Quantopian's first release was #2. We got the data, the backtester, etc. We're in the process of releasing #3, so that people can trade. When we've got that going, we plan on going back to #1 and solve that problem, too.
The short answer is that we totally agree with you, but it's unfortunately not in the immediate road map.
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