@theflyingdutchman Hello, before the end of the week the update will be ready, sorry for the delay

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

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

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

@buyers_are_back Hello.
Here is a new example of stock prediction using index data.
I recommend using the single-pass version.
https://quantiacs.com/documentation/en/data/indexes.html

@dark-pidgeot Yes, of course. Please note that we do not implement leverage, and the sum of the absolute values of the weights has to be equal or smaller than 1. If it is larger, they will be rescaled down.

@magenta-grimer Hello, we updated the documentation.

Now there are 78 futures contracts. Yes, we allow allocating to only 1 asset. If you trade more assets, then you can go long on some of them and short others.

Using more assets helps in increasing the Sharpe ratio, as the mean return grows linearly with the number of assets, and the volatility in the denominator with the square root of the number of assets if there are no correlation terms.

Using uncorrelated assets would then lead to a scaling of the Sharpe ratio with the square root of the number of assets. In practice, however, correlation terms are decreasing this growth.

Stated more simply, it is a good idea to avoid putting all your eggs in the same basket...

@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.

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

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

@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)

@xiaolan Yes, allocations are translate to orders internally, it is enough to check the variation in the allocations and transform it into number of contracts bought/sold. When we designed the toolbox the goal was to simplify development as much as possible for the users.

@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();

@support
Thanks.
You guys are the best!🏆

@support Got it, thanks a lot!

@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.

@anthony_m we patched with spot BTC data see answer: https://quantiacs.com/community/topic/6/btc-contest-start-date

@rezhak21 Rules are defined at: https://quantiacs.com/contest and more details for the current contests (submission time till end of May) can be found at: https://quantiacs.com/contest/15

For Futures the in sample period starts on January 1st 2006, for the BTC Futures on January 1st, 2014

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