@support Thanks for the detailed answer, that seems to be it, here is the final code:

import xarray as xr import qnt.stats as qns import qnt.output as qnout import qnt.data as qndata # single-stock trading data = qndata.futures.load_data(min_date="2005-01-01", assets=["F_ES"]) # attempting an optimal (unrealistic) long-only strategy # by looking at future prices, and investing only if there will be profit next_price_open = data.sel(field="open").shift(time=-1) next2_price_open = data.sel(field="open").shift(time=-2) weights = xr.where(next_price_open < next2_price_open, 1.0, 0.0) # sell short when optimal: # weights = xr.where(next_price_open > next2_price_open, -1.0, weights) weights = qnout.clean(weights, data) qnout.check(weights, data) qnout.write(weights) stats = qns.calc_stat( data, weights, # ignoring slippage for simplicity slippage_factor=0, roll_slippage_factor=0) stats.loc[:, "equity"].plot.step();

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

@eddiee

Hello.

This code looks to the future.
It is needed to train the model.
Pay attention to the name of the variable.

@support Thank you !

@support Got it, thanks a lot!

@support
Thanks.
You guys are the best!🏆

Dear @quani42,

Your submissions are in the queue and will be processed. Also, all submissions that are sent to the contest before the deadline will be eligible to take part in it.

Regards

@captain-nidoran hi, all your strategies which will take part to the contest should be under the "In Contest" tab in the "Competition" section.

The migration "Candidates" -> "In Contest" was not immediate as we released minor improvements to the front-end side once the submission phase was over.

@anshul96go Sorry for the late answer, we missed it somehow. Yes, all submissions sent before deadline will be processed and accepted.

@captain-nidoran Fixed, sorry for issue

@cross_platform-zebra Hi, there is no other limitation regarding local development. It is already configured to be exactly the same datasets for Nasdaq100 stocks, and returns the same statistics for trading system running locally or online.

@magenta-grimer There are 2 things you might want to change:

1: the lookback_period is 365 but you want a 400-day SMA. This will only produce NaNs, so the boolean array sma20 < sma20_crypto will be False everywhere resulting in -1 weights. 2*365 as lookback does the trick for these settings.

2: Bitcoin is trading 24/7, futures aren't. Better use crypto.time.values instead of futures.time.values for the output of load_data.

There might be something else that I didn't catch but the resulting sharpe is at least close to what would be expected (1.109 with 5 and 385)

@sheikh Hi, when it comes to stocks and historical simulations, the biggest issue is dealing with survivorship bias. The stock universe must include also stocks which have been delisted and we need to define trading rules which allow for trading instruments which make sense at each point in time. This week we are announing a new contest which is preparing the ground for stocks.

Thanks !

@support Ok, I see, thanks

@support I see, need to push them to submit then....

@support yes coarse heuristics work well as long as you are conservative. For shorter term models I have started using minute bars despite the computational hit, because it helps in a lot of other ways.

I may enter this contest, I am pretty rusty on predictive modelling and I am not sure I can do a good job using just daily prices, there is not a lot of data. I used to work at a CTA and I feel like we wasted a lot of man years using only prices, hoping better models would acheive more alpha. in the end the sharpe is similar to the S&P but uncorrelated, but you have gotten there with some simpler models and enjoyed life.

I have some other questions about the platform and the contest that I will post here.

Best
P.M.

@support Thank you so much. I have resolved this error

@support ok, thank you!

@newbiequant96 Hi, the template is a "working code" still to be finalized and published among the templates in the account area, however the logic behind is strictly multi-pass and a conversion to single pass is not really so straightforward.