Be ultra careful about look ahead bias when using these sorts of data sets. It's easy to use the VIX close in the morning by mistake. One needs to shift the data.

Yes, I did. I develop the algos in Excel and have been doing that for ever. So I am always careful of lookahead bias.

But, no way to get these using using the fetch function just yet, eh?

VIX and some other volatility indicators provided by Quandl are provided natively. Check them out here: https://www.quantopian.com/data?searchq=CBOE

The last one listed has an example algorithm plus each page has it's own "How to" code block.

These data sets are designed to avoid look-ahead bias in terms of how we surface the data through to the backtester but being careful of lookahead bias is still a good idea :)

Thanks
Josh

Honestly, I couldn't figure out the code as I am a novice in Python. What would really help me out if someone writes a snippet code that just return the values of the closing vix, vix future 1, and vix future 2 for the previous day. I would really appreciate it if someone writes and shares the code. I am sure a lot of people are doing this already. Thanks much in advance!

Hi quantopian team, please is there any update?
More importantly, is there any chance to have intraday VIX data supported in QP without having to use Pipeline or other more complex solutions?
Thanks,
Angel

Not sure about vix futures 1 and 2 months, but you can extract via pipeline VIX and VXV as proxies for futures 1 and 2 month
Here is example of the code taken from this thread https://www.quantopian.com/posts/vix-trading-algorithm-return-150-percent-a-year-over-past-5-years-but-has-50-percent-drawdown-from-2015-meltdown#584637541379940012000213
My understanding, no other simple solutions at the moment, only pipeline ans fetcher... until Q implements futures support

class GetVIX(CustomFactor):  
    window_length = 1  
    def compute(self, today, assets, out, vix):  
        out[:] = vix[-1]

def initialize(context):  
    pipe = Pipeline()  
    attach_pipeline(pipe, 'my_pipeline')  
    pipe.add(GetVIX(inputs=[cboe_vix.vix_close]), 'VixClose')  
    pipe.add(GetVIX(inputs=[cboe_vxv.close]), 'VxvClose')  
    pipe.add(GetVIX(inputs=[cboe_vxd.close]), 'VxdClose')  
    pipe.add(GetVIX(inputs=[cboe_vvix.vvix]), 'VvixClose')

def before_trading_start(context,data):  
    context.output = pipeline_output('my_pipeline')  
    context.vix = context.output["VixClose"].iloc[0]  
    context.vxv = context.output["VxvClose"].iloc[0]  
    context.vxd = context.output["VxdClose"].iloc[0]  
    context.vvix = context.output["VvixClose"].iloc[0]

Many thanks Maxim, much appreciated. I will debug over the next days the vix data we get from pipe with data I get from another source.
If you backtest this algo since end of 2007 the return is quite astronomical. Even if there's a too high DD, still the return seems too nice to be true.
I'll review it and see if we can find a way to smooth it and have less swings. Many thanks for sharing it,
Angel