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Hi all,
How important is performance recency in backtests? I tried googling this (in both a quant and statistical perspective) and was not able to find anything.
One backtest iteration shows significantly higher performance in the most recent years however
Another backtest shows a better performance overall
I'm leaning towards overall performance because it is unbiased, but I keep thinking that the strategy could be more fitting to the current times in this exponentially evolving trading world.
What do you guys think?
Shile Wen
Hi Pcnpj,
This is a great example of trade offs that Quants need to make. In my opinion, we should look at the performance of the algorithm through different market conditions (e.g. Bull and Bear) to see if it is able to perform in various market conditions. Then, we need to see that recent performance isn't too bad or else it could be a sign of Alpha Decay.
Best,
Shile Wen
Pcnpj
Thanks for the response Shile WenÂ
Is there any easier way to know which markets are bullish and bearish besides manually and looking at indexes vs. my algorithm?
Much appreciated
Jared Broad
That is the trillion-dollar question =)
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Pcnpj
Haha, I knew there wasn't ever a "set" or "right" answer but wanted peoples thoughts :P
Pcnpj
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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