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:

1. Comment out or delete the line where qnbt.backtest_ml is used.
2. 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)

3. 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.