@vyacheslav_b Thank you very much for your support.

I would like to ask, if I want to filter out the crypto codes with the highest sharpness, what should I do? Thank you. I tried using the get_best_instruments function but it didn't work

import qnt.stats as qnstats # data = qndata.stocks.load_ndx_data(tail = 17*365, dims = ("time", "field", "asset")) data = qndata.stocks.load_ndx_data(min_date="2005-01-01") def get_best_instruments(data, weights, top_size): # compute statistics: stats_per_asset = qnstats.calc_stat(data, weights, per_asset=True) # calculate ranks of assets by "sharpe_ratio": ranks = (-stats_per_asset.sel(field="sharpe_ratio")).rank("asset") # select top assets by rank "top_period" days ago: top_period = 1 rank = ranks.isel(time=-top_period) top = rank.where(rank <= top_size).dropna("asset").asset # select top stats: top_stats = stats_per_asset.sel(asset=top.values) # print results: print("SR tail of the top assets:") display(top_stats.sel(field="sharpe_ratio").to_pandas().tail()) print("avg SR = ", top_stats[-top_period:].sel(field="sharpe_ratio").mean("asset")[-1].item()) display(top_stats) return top_stats.coords["asset"].values get_best_instruments(data, weights, 10)

19ae499c-71f3-4702-bba3-81d20fb6c5ac-image.png

@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

@vyacheslav_b perfect. It wasnt obvious to me that single pass was meant by that. Thank you

@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

@captain-dog Hi,

This is the total number of different companies that have been constituents of Nasdaq-100 index at some point, from 2006-01-01 and so on. At any point, there are 100 companies in index (sometimes more, up to 108) and those are considered as liquid ("is_liquid"=1.0), as long as they are part of the index.

@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

@gjhernandezp Dear gjhernandezp,

No, models are trained from scratch taking the train_period of the data that is changing depending on the processing day. No, the retraining is done from scratch, taking the whole available data up until that point in time. That is not available at the moment, but take a note that when submitting a ML strategy it should be in a single-pass mode. For example instead of using backtest_ml function that is designed for testing purposes, one should use single-pass, for example: data_train = load_data(train_period) models = train_model(data_train) data_predict = load_data(lookback_period) weights_predict = predict(models, data_predict) print_stats(data_predict, weights_predict) qnout.write(weights_predict) # To participate in the competition, save this code in a separate cell.

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

Dear magenta.kabuto,

It appears to be a pandas version mismatch. You can try using different pandas version but it's not documented nor supported and our library could behave in an unexpected manner.

@vyacheslav_b Thank you so much

@vyacheslav_b Thank you so much

@illustrious-felice Hi, just insist and test other ideas, it is not easy but you will manage!

@magenta-kabuto The weights generated are simply the daily allocations to the various assets.

@vyacheslav_b Thank you so much