A Question for the fellow Quants
For all of the quants on here, what is the approximate proportion of strategies/factors that turn out to be useless to strategies that are successful? I'd also love to hear from anybody that has experience with FX algos (as i'm limited to only trading FX in my personal account since I'm interning at a hedge fund)
Even a lot of published asset pricing literature is questionable. Negative/inconclusive results are never published so there's an incentive play around with the data until it satisfies your original hypothesis.
It's hard question to answer. Unless your strategy is pretty high frequency, you won't know if your factor is useful for potentially years. If you're rebalancing the portfolio bimonthly, you're generating only 24 data points per year. To get a strong t-stat on whether long/short return on the factor is positive, you might need two or more years of data.
intra-day quants can get more immediate feedback on the efficacy of a factor or strategy change but most quants have to live with not really knowing whether what they did was useful out of sample for a long time. I don't know about higher frequency factors as that's not my space but most "useful" factors don't have that high of a sharpe in backtests. 0.4-0.6 is very common for simple single factor long/short sharpe. Most factors in backtests will go through multi year periods where the return is negative. But as long as the return profile is somewhat stable over time and the alpha hasn't decayed over time and is a diversifying source of alpha then it's okay to add.
Maybe higher freq quants can chime in on this as my experience is really mostly with monthly time series data.