@sun-73 @RoyPalo, Hi,

Q23 Leaderboard was updated several days ago, all eligible submissions are there now, sorry for late notice. Please let us know if you find any submission that is missing.

@green-flareon Thanks. The live phase of the Q23 is running. Quants can join any contest during the submission phase. Q24 is on.

@support Great help! Thank you so much!

@sheikh It is fine, please just submit, check the result and let us know if you see any issue. It should work fine.

@support Ok, I see, thanks

@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.

@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.

@support ok, thank you!

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

@support
Thanks a lot !

@cyan-gloom

Hello. The provided code is insufficient to understand the problem.

I assume that a certain function might not be returning the required value (for instance, the function where your model is being created).

I recommend that you check all return values of functions, using tools like display or print. Then, compare them with what is returned in properly working examples.

The state allows you to use data from previous iterations. You can find an example here:
https://github.com/quantiacs/toolbox/blob/2f4c42e33c7ce789dfad5d170444fd542e28c8ae/qnt/examples/004-strategy-futures-multipass-stateful.py

@vg2001 Hello, the Q19 is a replica of the Q18, you ccan use the same examples.

@johback

Hello

More examples are here https://github.com/quantiacs/toolbox/blob/main/qnt/tests/test_fundamental_data.py

This is a simple example.

import qnt.data as qndata import datetime as dt import qnt.data.secgov_indicators import qnt.data as qndata import qnt.stats as qns assets = qndata.stocks.load_ndx_list(tail=dt.timedelta(days=5 * 365)) assets_names = [i["id"] for i in assets] data = qndata.stocks.load_ndx_data(tail=dt.timedelta(days=5 * 365), dims=("time", "field", "asset"), assets=assets_names, forward_order=True) facts_names = ['operating_expense'] # 'assets', 'liabilities', 'ivestment_short_term' and other fundamental_data = qnt.data.secgov_load_indicators(assets, time_coord=data.time, standard_indicators=facts_names) # Operating expenses include marketing, noncapitalized R&D, # travel and entertainment, office supply, rent, salary, cogs... weights = fundamental_data.sel(field='operating_expense') is_liquid = data.sel(field="is_liquid") weights = weights * is_liquid # calc stats stats = qns.calc_stat(data, weights.sel(time=slice("2006-01-01", None))) 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")

@pink-seel Super that you found it, please do not hesitate to ask for support!

@auxiliary-snail Hi,

unfortunately, this is not allowed and in accordance with the rules. Using hard-coded time periods in which trading algorithm will work differently, is not a quantitative method (just like manual asset selection, e.g. "trade only Apple or Microsoft"). We still haven't implemented a mechanism for automatic recognition of such behaviors in trading strategies, and even though a strategy could be successfully submitted, it will not be eligible for prize winning.
What we are searching for, is well performing strategy over entire in_sample period (SR>0.7), robust to all market movements 2006-2025, so we can expect it will perform well in future, too.

@nosaai Hello

Spyder should be run under conda environment

conda activate qntdev conda install spyder spyder

an alternative way is to clone the library from https://github.com/quantiacs/toolbox
and develop strategies inside qnt. But I recommend using the approach from the documentation.

@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

This is a possible fix, but no gurantee. You have to adjust also the prediction function.

def train_model(data):
"""Create and train the models working on an asset-by-asset basis."""

models = dict()

asset_name_all = data.coords['asset'].values

data = data.sel(time=slice('2013-05-01',None)) # cut the noisy data head before 2013-05-01

features_all = get_features(data)
target_all = get_target_classes(data)

model = create_model()

for asset_name in asset_name_all:

# drop missing values: target_cur = target_all.sel(asset=asset_name).dropna('time', 'any') features_cur = features_all.sel(asset=asset_name).dropna('time', 'any') # align features and targets: target_for_learn_df, feature_for_learn_df = xr.align(target_cur, features_cur, join='inner') if len(features_cur.time) < 10: # not enough points for training continue try: model.fit(feature_for_learn_df.values, target_for_learn_df) models[asset_name] = model except KeyboardInterrupt as e: raise e except: logging.exception('model training failed')

return models

@buyers_are_back Hi,

Regarding your first question, yes, that is correct. As we look more into the past, it is more difficult to get data for companies which have been index members but don't exist anymore, for example. This is also related to your second question - symbols with '~1' are in almost all cases, the same companies with the same ticker symbol, but with different ISIN (International Securities Identification Number). For instance, SanDisk company ("NAS:SNDK") was standalone public company until 2016, when Western Digital acquired SanDisk. In 2025 company spinoff, SanDisk re-emerged on the Nasdaq as an independent public company, with the same ticker as it was ('SNDK'), but with different ISIN (considered as different company).
Those symbol pairs, should not have an intersection in membership ("is_liquid" field should not be 1.0 for both at the same time), otherwise it could be mistake by provider.

Thanks for the answer!

I still think something is wrong with this correlation checker. I even used this function to randomize the weights a few times, and I got the same correlation error:

def add_random_noise(weights, noise_level=0.01): noise = np.random.uniform(-noise_level, noise_level, size=weights.shape) return weights + noise

I am pretty sure it's impossible to have 90% correlation in this case.