@magenta-grimer

Hello.

Remove .isel(time=-1).

ma_slow = close.rolling(time=parameter1).mean() #.isel(time=-1) ma_fast = close.rolling(time=parameter2).mean()#.isel(time=-1)

It selects the last day, you need an entire series.

Regards.

@stefanm said in Collections has no attribute Iterable:

pip install progressbar2==3.55.0

Thank you so much @stefanm for support. Yes, it did work. Although on installation I did get these complaints:
qnt 0.0.306 requires tabulate>=0.9.0, but you have tabulate 0.8.10 which is incompatible.
qnt 0.0.306 requires xarray==0.20.2, but you have xarray 0.20.1 which is incompatible.

Also, my apologies for the late response. I had to put things aside for a day or so. Once again, thank you.

@magenta-grimer

Hello.

I confirm this bug.
It is fixed now.
If you clone this template again, it will work ok.

Thank you very much for your report.

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.

@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

@support
Actually, I've write the weights to the output function.
螢幕擷取畫面 2024-04-24 235034.png
I think the reason might be that the data was out-of-date when the strategy received at the weekend. After the data update in the next day, it failed to pass the test.

@vg2001 Dear vg2001,
There were some problems with the servers, thank you for your patience.
Regards

@antinomy wow, thank you so much, this is awesome!

@support thanks, yes....

@support

Thank you, I noticed some changes on the rules so I have to reimplement the strategy anyway.

@support Thank you, yes, this is what I was looking for

@support great, thank you, yes, this is what I was looking for

@maxime Yes, of course, you can use our optimizer:

https://quantiacs.com/community/topic/29/optimizing-and-monitoring-a-trading-system-with-quantiacs

Be careful with optimization as it will lead to overfitting....

@anshul96go Hi Anshul, the IS period starts on January 1st 2014. You can use a strategy with the following logic:

if Volume/OI is zero, then take this decision:

Otherwise, take this decision:

But yous algorithm should produce results also in the period when Volume/OI data were not available.

@eddiee It is fixed, sorry for the problem.

@gjhernandezp Hello, you can use them for local development. Unfortunately, we do not support yet external datafeeds after submission...it is on our to-do list.

@mwalimudan Sorry for the issue, but do not worry, we are extending the submission deadline to end of October as there were several issues. Please do not hesitate to report problems.

@support
Thanks
I got it !

@support. Thank you. So as I understand it, I will give higher weight to strategies with lower correlation and vice versa, right? According to your answer, I understand that I can also give high weight to low-volatility strategies and vice versa. So what about equal risk portfolio? In your opinion, is this an effective way to optimize your portfolio?

@magenta-kabuto Thank you very much for your advice. I will research to apply your suggestions to the algorithm