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How to forecast future candlesticks from one price history with Kronos

Load a pre-trained Kronos model and get a forecast of future OHLCV candles from your own historical data.

Reverse engineering. Forecasts from a sampled model are probabilistic and should be checked against your own out-of-sample data before anyone relies on them for trading decisions.

Who it is forA quant analyst or developer with a CSV of 5-minute or daily candles who needs a quick model-based forecast for one instrument.

The problemTurning raw candlestick history into a usable forecast normally means writing your own preprocessing, normalization, and inverse scaling around a model.

The outcomeA pandas DataFrame of forecasted open, high, low, close, volume and amount values, indexed by the future timestamps you supplied.

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 cloned Kronos folder, using Python 3.10 or newer.
  2. Load the tokenizer and model with KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base") and Kronos.from_pretrained("NeoQuasar/Kronos-small").
  3. Create the predictor with KronosPredictor(model, tokenizer, max_context=512).
  4. Split your data into a lookback window of 400 rows as x_df, x_timestamp, and a 120-step y_timestamp range, keeping the lookback at or below 512.
  5. Call predict with pred_len=120, T=1.0, top_p=0.9, and sample_count=1, then print pred_df.head().

Prompt for your agent

Using the Kronos repo, load NeoQuasar/Kronos-small with its tokenizer, forecast the next 120 candles for <CSV path with open, high, low, close columns and timestamps>, and return the pred_df table. Verify that the forecast index matches the y_timestamp values you passed in and that the lookback is no more than 512 rows.

Caveat. The README gives a demo forecast for BTC/USDT and a live demo page, but it does not report accuracy or backtest results for the default models.

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