You can add location information to your Tweets, such as your city or precise location, from the web and via third-party applications. You always have the option to delete your Tweet location history. Learn more
With some additional robustness checks and careful implementation of a simulator for fill prices, commissions etc you are well on your way to launching your first quant strategy.
Only problem is that the price series is one I generated using 100% random noise, it is completely unpredictable by any signal. Congratulations, you now know how to overfit a backtest. Welcome to quant research.
few understand
This is awesome, I like the idea of plotting sharpe as a function of parameter setting to find the sweet spot... good to bet on multiple settings tho for robustness. Since this series randomm,Any xtra steps u do to scrutinize whether it works bc of luck? Or is that always a risk
1. Don’t do any of these steps
this way you assume no distributional properties of the time series, you don't have your overfitting and back testing issues...?
Even simpler would be too look for runs. And it would give you an nice indication if the time series is behaving as a random distribution.
fuckin beautiful
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.
