def test_basic(self, dataloader_dummy, network_dummy):
metrics_table = generate_metrics_table({'bm_1': network_dummy},
dataloader_dummy,
{'rets': MeanReturns()})
assert isinstance(metrics_table, pd.DataFrame)
assert len(metrics_table) == len(dataloader_dummy.dataset)
assert {'metric', 'value', 'benchmark', 'timestamp'} == set(metrics_table.columns.to_list())
def test_errors(self, dataloader_dummy, network_dummy):
with pytest.raises(TypeError):
generate_metrics_table({'bm_1': 'WRONG'}, dataloader_dummy, {'metric': MeanReturns()})
with pytest.raises(TypeError):
generate_metrics_table({'bm_1': network_dummy}, 'FAKE', {'metric': MeanReturns()})
with pytest.raises(TypeError):
generate_metrics_table({'bm_1': network_dummy}, dataloader_dummy, {'metric': 'FAKE'})
# %%
# During training, the only mandatory metric/loss was the loss criterion that we tried to minimize.
# Naturally, one might be interested in many other metrics to evaluate the performance. See below
# an example.
metrics = {
'MaxDD': MaximumDrawdown(),
'Sharpe': SharpeRatio(),
'MeanReturn': MeanReturns()
}
# %%
# Let us now use the above created objects. We first generate a table with all metrics over all
# samples and for all benchmarks. This is done via :code:`generate_metrics_table`.
metrics_table = generate_metrics_table(benchmarks, dataloader_test, metrics)
# %%
# And then we plot it with :code:`plot_metrics`.
plot_metrics(metrics_table)
# %%
# Each plot represents a different metric. The x-axis represents the timestamps in our
# test set. The different colors are capturing different models. How is the value of a metric
# computed? We assume that the investor predicts the portfolio at time x and buys it. He then
# holds it for :code:`horizon` timesteps. The actual metric is then computed over this time horizon.
# %%
# Finally, we are also interested in how the allocation/prediction looks like at each time step.
# We can use the :code:`generate_weights_table` function to create a :code:`pd.DataFrame`.
weight_table = generate_weights_table(network, dataloader_test)