@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.

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

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

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

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

@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-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)

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

@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.
You guys are the best!🏆

@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();

@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.

@vyacheslav_b Apologies for the late response. Thanks for the assistance, all is now well. Cheers

@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

@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

@support Got it, thanks a lot!

@raider512 because they use a manual selection of assets

@theflyingdutchman Hello,

Another option is to rewrite your strategy for a single-pass version before submitting it. This approach will significantly speed up the calculations. However, it's important to note that the actual statistical values can only be tracked after submitting the strategy to the competition.

For example:
https://github.com/quantiacs/strategy-ml-crypto-long-short/blob/master/strategy.ipynb

To adapt this strategy for a single-pass version, follow these steps:

Comment out or delete the line where qnbt.backtest_ml is used. Insert the following code: import xarray as xr import qnt.ta as qnta import qnt.data as qndata import qnt.output as qnout import qnt.stats as qnstats retrain_interval = 3*365 + 1 data = qndata.stocks.load_ndx_data(tail=retrain_interval) models = train_model(data) weights = predict(models, data) In a new cell, insert code to save the weights: qnout.write(weights)

To view the strategy's statistics, use the following code in a new cell:

# Calculate stats stats = qnstats.calc_stat(data, weights) display(stats.to_pandas().tail()) # Graph performance = stats.to_pandas()["equity"] import qnt.graph as qngraph qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log")

The qnbt.backtest_ml function is a unique tool for evaluating machine learning strategies, which stands out from what is offered on other platforms. It allows users to set retraining intervals and analyze statistical metrics of the strategy, as opposed to the traditional evaluation of the machine learning model. This provides a deeper understanding of the strategy's effectiveness under various market conditions.