Hello Jacob,

Do you have a specific algorithm you could share as an example?

Grant

Jacob, I assume by "belly up" you mean that the algo has a runtime error and fails when you change the backtest from daily mode to minutely mode.

The answer is that yes, it happens a lot, to newbies and experts both. We try really hard to make algo writing easy, and we work hard to make the product "smart" so that you can easily get past obstacles and complexities of algo writing. This is a problem that we didn't anticipate as well as others. (The ones we anticipate well are ones that you never noticed!) We're working on a few different changes to make this one go away, in particular making sample algorithms all minutely-compatible and by building some smart "run-once-daily" functions that work in minute mode algos. The new history() command should help, too, by making trailing windows of data in minute mode more easily accessible.

Some general strategies:

• Don't use brittle code like Booleans. It's tempting to say "if first run = True" but that fails in many ways. Find a more robust way to identify your current status.
  
• Along the same lines, it's easy to just say "order 50 shares" but that is brittle in the face of market and algorithm status. You should think instead of "target position X" and then have helper code that assesses your current portfolio position and issues the commands that gets you to the new, target portfolio.
  

Dan,

Sorry, I should have been more precise. What I mean is that algorithms that earn solid returns (north of 200% for example) on daily backtests seem to virtually shoot straight down in minute backtests.

Grant, for an example, see the attached backtest.

Hi Jacob,

The first thing I would check is your trading frequency and transaction costs. Unless you specify otherwise the Quantopian backtester applies (non-zero) defaults for transaction costs (slippage & commissions) to your strategy, if your strategy trades more frequently in minute mode than daily mode then you will incur more t-costs. This can very quickly overpower returns.

Best regards, Jess

Thanks Jacob,

Good example...no time now, but I'll try to have a look later. My sense is that it's gonna be non-trivial to replicate your algorithm exactly running on minute bars.

Grant

Jacob,

Here's an example you can tinker with. I tried to get TA-lib to work, with no success. Perhaps someone else can do a better job, and actually show the TA-lib implementation, with the history API.

Grant

Hello Grant,

Here is the same result using TA-Lib.

P.

Thanks Peter...Grant

Here's the result over a longer time frame, using:

stock_sma = prices[stock].mean(axis=1)  

Based on Peter's code above, I'd expect the result to be the same with TA-lib.

Grant

Hello Grant,
Could you please explain if there is any difference in performance, accuracy .. etc. by using talib to generate the SMA instead of python's "mean" or vice versa?

Best regards,
Omar

Hello Omar,

I can't comment on performance of the TA-Lib but it has the advantage of being a quite extensive library of functions including candle pattern recognition. Implementing a moving average in Python/pandas is trivial but implementing the whole library is not. That's not to say people don't waste their time trying - see https://code.google.com/p/ultra-finance/wiki/pyTaLib

P.

Wow, thanks for all the help everyone. Grant – thanks for putting that algo & backtest together, looks like only trading once a day made it work much better. Transaction costs must have been what was killing me, like Jessica said.

Thanks Peter,
I thought it was as you said, but had to check to see if I missed something.

Hello Jacob,
Maybe you should check that your chosen stocks are not biased. I have tried a different choice and got very different result. I also tried "set_universe" but that backtest hanged.