Simple Example Long Put Hedge

if you increase the percentage of portfolio for underlying asset to 2%, the 3 year return more than doubles.

Thanks for the comment S.G. But note that if you change the value of self.percentage to 2, the allocation towards the underlying is not 2%. Instead it is 200% which would mean that you are using margin to open a leveraged position. This comes with higher risk and fees. I'd recommend keeping self.percentage below 1 (which would be 100%).

Great video and thanks for sharing.

Swap out SPY for UPRO or TQQQ and you get some interesting numbers

Granted some of the Draw Downs are a whee bit harsher but interesting results

Hi Louis,

Nice algorithm! Thanks for sharing with the community.

Best,
Derek Melchin

I watched the order data, I couldn't get it to buy any puts though... tried from 2017 to 2020 (Nov) with TQQQ and SPY.  Maybe I configured something incorrectly. I will print out some log statements and see what is going on.

Hi Goldie,

Have you cloned the algorithm above or copied the code while watching the video? If you haven't cloned it, I recommend doing so to see if this fixes the problem.

I hit Clone Algorithm from this thread not the video (i didn't even know about it until later).

Oops, yes I guess after sifting thru the order data maybe I was originally thinking to see the Option symbol more noticeably but I guess those weird long numbers are the option symbols.

Fill: $235.00 USD

Hi Goldie,

I am glad it got sorted out.

Hi Louis,

could you pls add a HMA ( Hull ) as a decision criteria, since is very quick and it could exit position when reverses ( under defined condition - value, ...).

This would made your Strategy much more profitable.

Pls let us know about the code, since I am newbie in Py and QuantConnect too.

Wish you a happy and prosperous New Year 2021,

Andrei 

Hi Andrei,

Thanks for the suggestion. I won't add this indicator to this code for now, but I'll keep it in mind for future projects.

It would be a great exercise for you to try to add such an indicator yourself though.

Happy new year to you as well!

Hi Louis,

thanks for your reply...

Since I am completely new in Py, I would like a suggestion where I can find such video, so that I can make it alone...

Thank you very much again for your great projects...  They are awesome..

A very great buildblocks to add pieces together and make a very functional Bot 

Keep on doing it also in 2021 :)

Andrei

Hi Andrei,

If you are new to Python and QuantConnect, I'd recommend starting with the basics. Firstly, I'd recommend learning standard Python. If you feel somewhat confident with that, I'd recommend starting the QuantConnect bootcamp.

Without understanding the basics, you won't be able to truly understand more complex algorithms either.

I hope this helps.

Hello Louis,

this is great stuff. The strategy seems to cope very well with and profit from vola spikes, however I feel like extended bear markets (2007 onwards) weigh heavy on performance. I have been trying to come up with an improvement but still busy learning and understanding the basics. Maybe this is something for later this year :)

Best, Fabmei

Louis 

I think there might be a bug (not in your code) in the way the contracts get treated. The hedging value is higher than the value you pay for the option.(factor 100)

The assumtion is, one option contract for 100 shares of the underlying; looks like thats what the stratey equity curve does.

if you check how much you paid for one contract, its only around 1.6 USD when the underlying cost 251 USD.

Thats way too cheep.

I'm calculation how much I pay for all the options like:

self.Securities[self.contract].Close * number_of_options or

self.Portfolio[self.contract].Price * self.Portfolio[self.contract].Quantity

This is the same as what I see later in the orders.

But if the options start to spike self.Portfolio[self.contract].Price * self.Portfolio[self.contract].Quantity is not enought to offset the loss of the underlying asset. But the strategy curve looks like it was offseted.

Looks like the option chain gives you a price for one option one underlyiong and the stratety curve uses one option equals 100 underlying

Hello everyone, 

Note that somehow QuantConnect does not allow you to import the CBOE module anymore. So just comment out the following line and everything should work fine again:

# from QuantConnect.Data.Custom.CBOE import *

Hi Louis,

thank you for the code, very interesting. Can you just explain the “self.OTM = 0.01" line code. For example if selfOTM = 0.03, what does this mean concerning the Delta's of the Put option? Does it mean Delta of the Put = -0.03 (very OTM)?

self.OTM determines the percentage that the put option is out of the money. So a value of 0.03 would mean that the algorithm tries to trade a put option that is OTM by 3%. I hope this answers your question. Otherwise, let me know.