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Hi,
I am a trader first before I started learning programming (not too long ago tbh). I was satisfied to know that my usual strategy in day trading could be replicated in regular algorithm coding (i.e manually selecting stocks to backtest) with decent positive performance. However, this allows for human selection bias as I have to manually choose a stock to trade and was convinced using the algorithm framework can improve the performance.
I have since attempted to replicate my strategy within the algorithm framework using a dynamic universe and alpha model. The main issue now is that the performance yields a horrendously negative performance. I'm not sure where to pick this apart.
Feedback, thoughts, suggestions, and questions are all welcome.
Kristofferson V Tandoc
Here is an example of a backtest of the same strategy but for a personally/manually selected stock “AMPY”:
Kristofferson V Tandoc
Update:
The alpha model triggers too many unfavourable buy/sell orders. Is there a way to reduce trading frequency using the algorithm framework while executing buy/sell orders at specific conditions?
Jared Broad
Hey Kris,
That would be the job of the portfolio construction model - he should aggregate and weigh the signals from the alpha model and decide on the final allocations. It doesn't have to be a 1-1 pass-through, and some signals could indeed be completely ignored if e.g. allocating to them would lead to a high concentration of risky or correlated assets.
If the above paragraph sounds like nonsense / over-complication, I would not recommend using the framework. The classic style of algorithm design is suitable for 90% of retail applications. The framework is designed for managing larger portfolios.
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 QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect 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. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
Kristofferson V Tandoc
Hi Jared Broad,
Thanks for the response.
It makes sense to me. Actually the documentation on key concepts of the algorithm framework is quite easy to digest. The implementation (into code) is the hardest part. Not a lot of tutorials (for beginners) on other modules/models of the framework exist after the universe selection and alpha model selection tutorials in the bootcamp. That's basically where I'm at.
I'll keep digging into more documentation. All other suggestions and opinions are still welcome.
Kristofferson V Tandoc
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 QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect 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. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
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