Topics created by multi_byte.wildebeest

@multi_byte-wildebeest Hi, these limitations refer to the processing time per point in time, not for the full strategy.

If it takes 10 minutes per historical day, and the simulation has to take into account 250 days for let us say 10 years, the multi-pass simulation would process 6 days per hour, 144 days per real day, that means 2 weeks of processing time for the full submission, it is a lot of time.

@multi_byte-wildebeest Hi, there are always limitations in a real-world scenario. Here we are running a contest on US stocks only, however imagine to trade assets worldwide. Then you have to take care of different timezones, and once US markets close, you have some hours to generate trading positions for (let us say) Australian markets.

If a ML model takes days to take a decision, that is unusable.

The limitations in place are per point-in-time, it means that for each "pass" the system should take no more than 5 or 10 minutes.

In practice we have also other limitations, because when many systems are in our queue, it can take very long to process all of them.

@multi_byte-wildebeest Hi. Without an example, it's unclear what the problem might be.

If you use a state and a function that returns the prediction for one day, you will not get correct results with precheck.

This was discussed here: https://quantiacs.com/community/topic/555/access-previous-weights/18

@support Thank you !

@multi_byte-wildebeest Hello.

How to get the Sharpe Ratio is in the Quick Start template.
https://github.com/quantiacs/strategy-q20-nasdaq100-quick-start/blob/master/strategy.ipynb

import qnt.stats as qnstats def get_sharpe(market_data, weights): rr = qnstats.calc_relative_return(market_data, weights) sharpe = qnstats.calc_sharpe_ratio_annualized(rr).values[-1] return sharpe sharpe = get_sharpe(data, weights) # weights.sel(time=slice("2006-01-01",None))

or

import qnt.output as qnout qnout.check(weights, data, "stocks_nasdaq100")

or

stat = qnstats.calc_stat(data, weights) display(stat.to_pandas().tail())

or

import qnt.graph as qngraph statistics = qnstats.calc_stat(data, weights) display(statistics.to_pandas().tail()) performance = statistics.to_pandas()["equity"] qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log") display(statistics[-1:].sel(field=["sharpe_ratio"]).transpose().to_pandas()) qnstats.print_correlation(weights, data)

Please look at this post
https://quantiacs.com/community/topic/515/what-is-forward-looking-and-why-it-s-effective-badly-to-strategy/6?_=1711712434795

@illustrious-felice Hello. please look at this post
https://quantiacs.com/community/topic/515/what-is-forward-looking-and-why-it-s-effective-badly-to-strategy/6?_=1711712434795

@multi_byte-wildebeest Dear multi_byte-wildebeest,
You can check logs for your submission in the Filtered panel, so you can get more information why it might have failed. Please note that installing libraries takes time and should be placed in init.ipynb file and not in strategy.ipynb. Also check for potential bugs, errors and tricky loops.

Regards

@multi_byte-wildebeest is it taking 2-3 minutes for each point in time?

@multi_byte-wildebeest Hello. I don't use machine learning models in trading.