deepdow.losses module¶
Collection of losses.
All losses are designed for minimization.
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class
Alpha
(benchmark_weights=None, returns_channel=0, input_type='log')[source]¶ Bases:
deepdow.losses.Loss
Negative alpha with respect to a selected portfolio.
- Parameters
benchmark_weights (torch.tensor or None) – Weights of the benchmark portfolio of shape (n_assets,). Note that this loss assumes it will be always located under this index in the `y tensor. If None then equally weighted portfolio.
returns_channel (int) – Which channel of the y target represents returns.
input_type (str, {'log', 'simple'}) – What type of returns are we dealing with in y.
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__call__
(weights, y)[source]¶ Compute negative alpha with respect to the benchmark portfolio.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative alpha.
- Return type
torch.Tensor
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class
CumulativeReturn
(returns_channel=0, input_type='log')[source]¶ Bases:
deepdow.losses.Loss
Negative cumulative returns.
- Parameters
returns_channel (int) – Which channel of the y target represents returns.
input_type (str, {'log', 'simple'}) – What type of returns are we dealing with in y.
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__call__
(weights, y)[source]¶ Compute negative simple cumulative returns.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative simple cumulative returns.
- Return type
torch.Tensor
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class
LargestWeight
[source]¶ Bases:
deepdow.losses.Loss
Largest weight loss.
Loss function representing the largest weight among all the assets. It is supposed to encourage diversification since its minimal value is 1/n_asssets for the equally weighted portfolio (assuming full investment).
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__call__
(weights, *args)[source]¶ Compute largest weight.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
args (list) – Additional arguments. Just used for compatibility. Not used.
- Returns
Tensor of shape (n_samples,) representing the per sample largest weight.
- Return type
torch.Tensor
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class
Loss
[source]¶ Bases:
object
Parent class for all losses.
Additionally it implement +, -, * and / operation between losses.
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__call__
(weights, y)[source]¶ Compute loss.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_input_channels, horizon, n_assets) representing ground truth labels over the horizon of steps. The idea is that the channel dimensions can be given a specific meaning in the constructor.
- Returns
Tensor of shape (n_samples,) representing the per sample loss.
- Return type
torch.Tensor
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class
MaximumDrawdown
(returns_channel=0, input_type='log')[source]¶ Bases:
deepdow.losses.Loss
Negative of the maximum drawdown.
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__call__
(weights, y)[source]¶ Compute maximum drawdown.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample maximum drawdown.
- Return type
torch.Tensor
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class
MeanReturns
(returns_channel=0, input_type='log', output_type='simple')[source]¶ Bases:
deepdow.losses.Loss
Negative mean returns.
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__call__
(weights, y)[source]¶ Compute negative mean returns.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative mean returns.
- Return type
torch.Tensor
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class
Quantile
(returns_channel=0, q=0.1)[source]¶ Bases:
deepdow.losses.Loss
Compute negative percentile.
- Parameters
q (float) – Number from (0, 1) representing the quantile.
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__call__
(weights, y)[source]¶ Compute negative quantile.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative quantile.
- Return type
torch.Tensor
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class
SharpeRatio
(rf=0, returns_channel=0, input_type='log', output_type='simple', eps=0.0001)[source]¶ Bases:
deepdow.losses.Loss
Negative Sharpe ratio.
- Parameters
rf (float) – Risk-free rate.
returns_channel (int) – Which channel of the y target represents returns.
input_type (str, {'log', 'simple'}) – What type of returns are we dealing with in y.
output_type (str, {'log', 'simple'}) – What type of returns are we dealing with in the output.
eps (float) – Additional constant added to the denominator to avoid division by zero.
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__call__
(weights, y)[source]¶ Compute negative sharpe ratio.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative sharpe ratio.
- Return type
torch.Tensor
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class
Softmax
(returns_channel=0)[source]¶ Bases:
deepdow.losses.Loss
Softmax of per asset cumulative returns as the target.
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__call__
(weights, y)[source]¶ Compute softmax loss.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative worst return over the horizon.
- Return type
torch.Tensor
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class
SortinoRatio
(rf=0, returns_channel=0, input_type='log', output_type='simple', eps=0.0001)[source]¶ Bases:
deepdow.losses.Loss
Negative Sortino ratio.
- Parameters
rf (float) – Risk-free rate.
returns_channel (int) – Which channel of the y target represents returns.
input_type (str, {'log', 'simple'}) – What type of returns are we dealing with in y.
output_type (str, {'log', 'simple'}) – What type of returns are we dealing with in the output.
eps (float) – Additional constant added to the denominator to avoid division by zero.
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__call__
(weights, y)[source]¶ Compute negative Sortino ratio of portfolio return over the horizon.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative worst return over the horizon.
- Return type
torch.Tensor
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class
SquaredWeights
[source]¶ Bases:
deepdow.losses.Loss
Sum of squared weights.
Diversification loss. The equally weighted portfolio has a loss of 1 / n_assets, the lowest possible. The single asset portfolio has a loss of 1.
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__call__
(weights, *args)[source]¶ Compute sum of squared weights.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
args (list) – Additional arguments. Just used for compatibility. Not used.
- Returns
Tensor of shape (n_samples,) representing the per sample sum of squared weights.
- Return type
torch.Tensor
Notes
If single asset then equal to 1. If equally weighted portfolio then 1/N.
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class
StandardDeviation
(returns_channel=0, input_type='log', output_type='simple')[source]¶ Bases:
deepdow.losses.Loss
Standard deviation.
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__call__
(weights, y)[source]¶ Compute standard deviation.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample standard deviation.
- Return type
torch.Tensor
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class
TargetMeanReturn
(target=0.01, p=2, returns_channel=0, input_type='log', output_type='simple')[source]¶ Bases:
deepdow.losses.Loss
Target mean return.
Difference between some desired mean return and the realized one.
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__call__
(weights, y)[source]¶ Compute distance from the target return.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative mean returns.
- Return type
torch.Tensor
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class
TargetStandardDeviation
(target=0.01, p=2, returns_channel=0, input_type='log', output_type='simple')[source]¶ Bases:
deepdow.losses.Loss
Target standard deviation return.
Difference between some desired standard deviation and the realized one.
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__call__
(weights, y)[source]¶ Compute distance from the target return.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative mean returns.
- Return type
torch.Tensor
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class
WorstReturn
(returns_channel=0, input_type='log', output_type='simple')[source]¶ Bases:
deepdow.losses.Loss
Negative of the worst return.
This loss is designed to discourage outliers - extremely low returns.
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__call__
(weights, y)[source]¶ Compute negative of the worst return of the portfolio return over the horizon.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, n_channels, horizon, n_assets) representing the evolution over the next horizon timesteps.
- Returns
Tensor of shape (n_samples,) representing the per sample negative worst return over the horizon.
- Return type
torch.Tensor
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covariance
(x, y)[source]¶ Compute covariance between two 2D tensors.
- Parameters
x (torch.tensor) – Torch tensor of shape (n_samples, horizon)
y (torch.tensor) – Tensor of shape (n_samples, horizon)
- Returns
cov – Torch tensor of shape (n_samples,).
- Return type
torch.tensor
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log2simple
(x)[source]¶ Turn simple returns into log returns.
r_simple = exp(r_log) - 1.
- Parameters
x (torch.Tensor) – Tensor of any shape where each entry represents a simple return.
- Returns
Logarithmic returns.
- Return type
torch.Tensor
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portfolio_cumulative_returns
(weights, y, input_type='log', output_type='simple', rebalance=False)[source]¶ Compute cumulative portfolio returns.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the predicted weights by our portfolio optimizer.
y (torch.Tensor) – Tensor of shape (n_samples, horizon, n_assets) representing the log return evolution over the next horizon timesteps.
input_type (str, {'log', 'simple'}) – What type of returns are we dealing with in y.
output_type (str, {'log', 'simple'}) – What type of returns are we dealing with in the output.
rebalance (bool) – If True, each timestep the weights are adjusted to be equal to be equal to the original ones. Note that this assumes that we tinker with the portfolio. If False, the portfolio evolves untouched.
- Returns
Tensor of shape (n_samples, horizon).
- Return type
torch.Tensor
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portfolio_returns
(weights, y, input_type='log', output_type='simple', rebalance=False)[source]¶ Compute portfolio returns.
- Parameters
weights (torch.Tensor) – Tensor of shape (n_samples, n_assets) representing the simple buy and hold strategy over the horizon.
y (torch.Tensor) – Tensor of shape (n_samples, horizon, n_assets) representing single period non-cumulative returns.
input_type (str, {'log', 'simple'}) – What type of returns are we dealing with in y.
output_type (str, {'log', 'simple'}) – What type of returns are we dealing with in the output.
rebalance (bool) – If True, each timestep the weights are adjusted to be equal to be equal to the original ones. Note that this assumes that we tinker with the portfolio. If False, the portfolio evolves untouched.
- Returns
portfolio_returns – Of shape (n_samples, horizon) representing per timestep portfolio returns.
- Return type
torch.Tensor