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    • O

      How long will the submission of a strategy take?
      Support • • omohyoid

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      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

    • A

      Futures contests and BTC??
      Support • • anthony_m

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

    • C

      Different dataset locally and in jupiterLab
      Support • • cross_platform.zebra

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      @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.

    • C

      Why Sharp ratios is not inverted ?
      Strategy help • • cyan.gloom

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      @support
      Thanks a lot !

    • S

      Q22 submission, strategies excluded
      Support • • Sun-73

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      Hi @support, everything is all right now. Thank you!

    • C

      How to load data to work with Multi-backtesting_ml
      Strategy help • • cyan.gloom

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      @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

    • J

      Fundamental Data: Periodic indicators & Instant indicators
      Strategy help • • johback

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      @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")
    • P

      Xarray Value Error
      Strategy help • • pink.seel

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

    • R

      Processing Time
      General Discussion • • rezhak21

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      @support ok, thank you!

    • O

      How to turn off "WARNING: some dates are missed in the portfolio_history"
      Support • • omohyoid

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      @omohyoid Hi, we do not have such calls, sorry

    • E

      Q17 Machine learning - RidgeRegression (Long/Short); there is an error in the code
      Strategy help • • EDDIEE

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      @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

    • A

      Submission Logic Questions
      Support • • auxiliary.snail

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      @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.

    • E

      Improving Quantiacs: Aligning Developer Objectives with the ones of Quantiacs
      General Discussion • developers improvement quantiacs rankings risk • • EDDIEE

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      @eddiee Hi, Mr. Eddie.

      I am new to building strategies using ML/DL on Quantiacs and am very impressed with the OS performance of your ML strategies. I hope you can give me your contact (mail, limkedin,...) so I can learn from your experience in building an ML/DL strategy.

      Sincerely thank.

    • cespadilla

      Question about the Q17 Machine Learning Example Algo
      Strategy help • • cespadilla

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      @cespadilla Hello.

      The reason is in "train_model" function.

      def train_model(data): asset_name_all = data.coords['asset'].values features_all = get_features(data) target_all = get_target_classes(data) models = dict() 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') target_for_learn_df, feature_for_learn_df = xr.align(target_cur, features_cur, join='inner') if len(features_cur.time) < 10: continue model = get_model() try: model.fit(feature_for_learn_df.values, target_for_learn_df) models[asset_name] = model except: logging.exception('model training failed') return models

      If there are less than 10 features for training the model, then the model is not created (if len(features_cur.time) < 10).

      This condition makes sense. I would not remove it.

      The second thing that can affect is the retraining interval of the model ("retrain_interval").

      weights = qnbt.backtest_ml( train=train_model, predict=predict_weights, train_period=2 *365, # the data length for training in calendar days retrain_interval=10 *365, # how often we have to retrain models (calendar days) retrain_interval_after_submit=1, # how often retrain models after submission during evaluation (calendar days) predict_each_day=False, # Is it necessary to call prediction for every day during backtesting? # Set it to true if you suspect that get_features is looking forward. competition_type='crypto_daily_long_short', # competition type lookback_period=365, # how many calendar days are needed by the predict function to generate the output start_date='2014-01-01', # backtest start date analyze = True, build_plots=True # do you need the chart? )
    • M

      Why we need to limit the time to process the strategy ?
      Support • • multi_byte.wildebeest

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      @multi_byte-wildebeest Hi, these limitations refer to the processing time per point in time, not for the full strategy.

      If it takes 10 minutes per historical day, and the simulation has to take into account 250 days for let us say 10 years, the multi-pass simulation would process 6 days per hour, 144 days per real day, that means 2 weeks of processing time for the full submission, it is a lot of time.

    • A

      Jupyter/Jupyter Lab are not working for code editing/running
      Support • • AlgoQuant

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      @captain-nidoran Fixed, sorry for issue

    • A

      Correlation fails although Sharpe ratio > 1
      Support • • agent.hitmonlee

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      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.

    • magenta.grimer

      Importing external data
      General Discussion • • magenta.grimer

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      support

      @penrose-moore Thank you for the idea. For the Bitcoin Futures contest we are indeed patching the Bitcoin Futures data with the BTC spot price to build a meaningful time series. For the other Futures contracts, for the moment we will keep the futures histories only, but add spot prices + patching with spot prices to increase the length of the time series to our to-do list.

    • S

      Stocks strategy
      Strategy help • • spancham

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      @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.

    • N

      SMA Example
      Support • • Nikos84

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      @support Thank you!

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