Moonshot looks for strategies inside the "moonshot" directory, so execute the following cell to "install" the strategy by moving the file to that location:
The
!sytax below lets us execute terminal commands from inside the notebook.
# make directory if doesn't exist
!mkdir -p /codeload/moonshot
!mv kitchensink_ml.py /codeload/moonshot/
Now we can run a walk-forward optimization, which iteratively trains and tests the machine learning model over successive periods.
In this example, we retrain the model annually (train="Y").
Because we do not specify a model parameter, the default model will be used, which is a regression model using Stochastic Gradient Descent. See the usage guide for more details about the default model.
The parameter progress=True causes the moonshot service to log the walk-forward progress during the analysis. To view the progress, open a separate Terminal window and stream the logs (using cut to format the logs to fit better in the window): quantrocket flightlog stream | cut -d ' ' -f 5-
As always, it is also a good idea to stream the detailed logs (
quantrocket flightlog stream -d) to see more granular logging during the walk-forward optimization.
from quantrocket.moonshot import ml_walkforward
ml_walkforward("kitchensink-ml",
start_date="2006-12-31",
end_date="2018-12-31",
train="Y",
progress=True,
filepath_or_buffer="kitchensink_ml*")
In the terminal window, you'll see output resembling the following:
[kitchensink-ml] Walk-forward analysis progress
train test progress
start end start end status Sharpe
iteration
0 1998-12-31 2001-12-30 2001-12-31 2002-12-30 ✓ -0.66
1 2001-12-31 2002-12-30 2002-12-31 2003-12-30 ✓ 4.13
2 2002-12-31 2003-12-30 2003-12-31 2004-12-30 ✓ 0.59
3 2003-12-31 2004-12-30 2004-12-31 2005-12-30 -
4 2004-12-31 2005-12-30 2005-12-31 2006-12-30
5 2005-12-31 2006-12-30 2006-12-31 2007-12-30
6 2006-12-31 2007-12-30 2007-12-31 2008-12-30
...from moonchart import Tearsheet
Tearsheet.from_moonshot_csv("kitchensink_ml_results.csv")
The trained model can be opened and inspected with joblib:
import joblib
trained_model = joblib.load("kitchensink_ml_trained_model.joblib")
print(type(trained_model))
<class 'sklearn.pipeline.Pipeline'>
estimator = trained_model.named_steps["estimator"]
print(estimator.coef_)
[-1.34829382e-03 1.59586293e-03 -5.37964974e-03 5.03634172e-03 1.76697327e-03 -4.81749326e-03 2.44881956e-03 -7.94251770e-03 4.54632030e-03 5.46585468e-03 1.19083565e-04 -1.70422498e-03 -1.37027375e-03 2.07582380e-03 1.44390384e-03 2.84971697e-03 -1.32943936e-05 3.70398584e-03 2.02261033e-03 -3.10249955e-03 -2.06972496e-03 -2.42205016e-03 -2.34435212e-03 -1.59885311e-03 -8.23607051e-04 -1.96146042e-03 -1.28053039e-03 -2.88410845e-03 9.27585927e-03 4.77671640e-03 -5.71552531e-03 -6.60115692e-03 -7.21790026e-03 -9.96407539e-03 -5.59588899e-04 6.60188312e-04 1.26066909e-03 1.18903662e-03 2.72005450e-03 -4.17630673e-03 -4.67664325e-03 -1.47470286e-03 1.19983041e-03 -1.56517068e-03 -3.28986787e-03 5.52887615e-05 2.00923245e-03 3.52792327e-04 -1.84249187e-03 -3.81018427e-03 1.21338737e-03 -1.02182224e-03 3.69924661e-03 -2.52483855e-03 9.86406649e-03 -5.58886283e-04 -1.68792396e-03 1.66169044e-04 1.34882574e-03 -5.24351352e-04 -4.97099554e-03 -1.04982881e-03 -2.02532429e-03 4.66637393e-02 5.15312353e-02 -2.26397439e-03 -6.34761166e-03 4.26696766e-04 -4.62305253e-04 7.07101469e-03]
To perform live trading, you would need to update your ML strategy to point to this trained model:
class TheKitchenSinkML(MoonshotML):
...
MODEL = "/codeload/kitchensink_ml/kitchensink_ml_trained_model.joblib"
See the usage guide for more details.