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How to forecast many assets in one batch run with Kronos

Use predict_batch to forecast several equal-length price series at once and keep results in input order.

Who it is forA developer or analyst monitoring a watchlist of instruments who needs forecasts for all of them with the same lookback and horizon.

The problemRunning single forecasts one by one is slow and makes it harder to compare many assets in the same window.

The outcomeA list of forecast DataFrames, one per input series, returned in the same order as the inputs.

What you need first

  • Python 3.10 or newer
  • pip, and access to install requirements.txt
  • Internet access to load models from the Hugging Face Hub
  • Historical candlestick data in a pandas DataFrame with open, high, low, close columns
  • For fine-tuning only: a local Qlib data directory, a GPU setup that works with torchrun, and optionally a Comet.ml account

Steps

  1. Run pip install -r requirements.txt in the Kronos folder.
  2. Build a Python list of DataFrames df_list, plus matching x_timestamp_list and y_timestamp_list, making sure every series has the same lookback length and the same pred_len.
  3. Create the predictor with KronosPredictor(model, tokenizer, max_context=512) after loading the model and tokenizer from the Hugging Face Hub.
  4. Call predictor.predict_batch with df_list, x_timestamp_list, y_timestamp_list, pred_len, T=1.0, top_p=0.9, sample_count=1, and verbose=True.
  5. Loop over the returned pred_df_list in order and print pred_df.head() for each series.

Prompt for your agent

In the Kronos repo, prepare equal-length candlestick DataFrames for <list of instrument CSV paths>, call predict_batch with pred_len=<number of steps>, and write one forecast CSV per instrument. Verify that the number of returned DataFrames equals the number of input series and that each series has the same lookback length.

Caveat. The README says predict_batch leverages GPU parallelism, so a CPU-only machine may run it much more slowly.

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