Is it possible to calculate the ratio of two securities ?

Hi Lionel,

The issue here is that sid(x) returns a security object. If you want to calculate the ratio of the security prices you'd want to do something like:

data[sid(x)].price / data[sid(y)].price

This will give you the latest price for each security. If you want historical prices you'll need to use history(), which is documented on our help page. Let me know if you have more questions.

Hope this helps,
Delaney

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Lionel

Hi Delaney ,

Thank you for the quick response. I will try this .

Cheers

Lionel

Hi Delaney ,

One last question. To pull the moving average of that ratio , what should I write ? i wrote the following but getting a **KeyError:1.0"

ratio = data[sid(x)].price / data[sid(y)].price

ratioMA = data[ratio].mavg(20)

thanks

Sure thing. Our mavg functions work for things already in data (securities), but in this case I think you'll need to do it a little differently. The easiest way seems to be using history like this

prices = history(bar_count=20, frequency='1d', field='price')  
ratioMA = (prices[sid(x)] / prices[sid(y)]).mean()

Calling history like this will give you a data structure containing the last 20 bars of price data for each security at a daily frequency. You can then index into prices like you did into data to get the 20 bars for that security. Dividing the two and taking the mean will give you the average for the last 20 days. This will be a moving average in your algorithm as the last 20 bars will be updated each time handle_data is called. Is this what you wanted? Another common use case would be computing the moving average for each of the last n days, which would be slightly different.

Disclaimer

Lionel

Hi Delaney,

Thank you for your help. I implemented your code the also, but the result are wrong. Here's the logic of the strategy :

RISK ON : SPY , RISK : OFF : TLT
calculate the SPY/TLT ratio : Ratio
calculate the 20 MA of SPY/TLT ratio : ratio MA
if Ratio > ratio MA, RISK ON, buy SPY
elif Ratio < ratio MA , RISK OFF , buy TLT

Simple enough , but the results are a bit out of whack .

In this case the algorithm is using a tremendous amount of leverage that would be unrealistic in the real world. It keeps ordering more and more securities, and never sells any. As a result the initial cash amount is exhausted and the algorithm goes into negative cash (borrows money) to make the following orders. I've recorded the cash in this backtest and it goes very negative almost immediately. Being able to borrow infinite amounts of money with no interest will certainly let you get the returns you see in your backtest.

In short your algorithm needs to keep track of the current leverage and not let it go above a certain amount. In the Quantopian Open contest our maximum is 3, but for personal trading you might want to restrict it to be lower or higher depending on your agreement with your broker. This can be accomplished by checking context.account.leverage before placing an order, and liquidating positions at some point in the algorithm.

Disclaimer

Another approach is to use order_target_percent(). This will adjust your portfolio to contain a certain percentage of each security. I'm attaching a backtest of the same algorithm modified to use this approach. As you can see it maintains a low leverage and does not borrow much cash.

Disclaimer

Market Tech

I had to wonder about the two obvious paradigms portrayed in the above P&L curve. What could be the cause?
It appears that there were two distinctive phases between treasuries and the S&P, one less negatively correlated, and one more. What's the next phase going to bring?

In the chart the lower green line is the correlation (weekly bars, 52 periods). The red line is the correlation of the period returns (close() - close(-5)).

SPY vs TLT correlation chart

Lionel

Delaney ,

Thank you so much for your help and latest code . I'm going to review it , and try to make it work. Will make sure to post the results here.

Market Tech , you raised a very good point. Based on Delany code, it seems that the strategy became more profitable when it when the inverse correlation was higher. Perhaps adding a rule such as * if correlation(SPY/TLT) < - X %* , would prevent the system to invest during less negatively correlated periods.

cheers

Lionel