Its simple write an algorithm in python that :
Takes the cosine of the sum of day low and day close. So
something like this:
Cos(low+close) = ?
if sum > 0 =Buy signal
if sum < 0=Sell signal
Thanks. I wrote this program in R but i do not know python :p
HI Isaac,
The attached backtest does the bare minimum to develop the signal and trigger trades. Hope it helps.
thanks,
fawce
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Hello Isaac & Fawce,
I couldn't resist tinkering with Fawce's algorithm. I generalized the model:
signal = amplitude*math.cos(k*(close_p+low_p)+phase) For the attached backtest, I set amplitude = 1 and phase = 0. For k, I used:
price_scale = 150
k = 2*math.pi/price_scale I also added the standard context.max_notional and context.min_notional limits for buying and selling, respectively.
Grant
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.