DeepDow
v0.1.0
Contents:
Introduction
Basics
Data Loading
Benchmarks
Layers
Networks
Losses
Experiments
Examples
API Reference:
deepdow package
DeepDow
Docs
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Index
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Index
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A
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B
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C
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D
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E
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F
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G
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H
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I
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K
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L
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M
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N
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O
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P
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Q
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R
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S
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T
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V
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W
_
__call__() (Alpha method)
(Benchmark method)
(Compose method)
(CumulativeReturn method)
(Dropout method)
(InverseVolatility method)
(KeynesNet method)
(LargestWeight method)
(Loss method)
(MaximumDrawdown method)
(MaximumReturn method)
(MeanReturns method)
(MinimumVariance method)
(Multiply method)
(Noise method)
(OneOverN method)
(Quantile method)
(Random method)
(SharpeRatio method)
(Singleton method)
(Softmax method)
(SortinoRatio method)
(SquaredWeights method)
(StandardDeviation method)
(TargetMeanReturn method)
(TargetStandardDeviation method)
(WorstReturn method)
_previous (ChangeWorkingDirectory attribute)
A
add_entry() (History method)
affine (AttentionCollapse attribute)
allocate_layer (LinearNet attribute)
alpha (BachelierNet attribute)
Alpha (class in deepdow.losses)
alpha (ThorpNet attribute)
AnalyticalMarkowitz (class in deepdow.layers.allocate)
AttentionCollapse (class in deepdow.layers.collapse)
AverageCollapse (class in deepdow.layers.collapse)
B
BachelierNet (class in deepdow.nn)
bar (ProgressBarCallback attribute)
Benchmark (class in deepdow.benchmarks)
BenchmarkCallback (class in deepdow.callbacks)
C
Callback (class in deepdow.callbacks)
callbacks (Run attribute)
ChangeWorkingDirectory (class in deepdow.utils)
channel_collapse_layer (BachelierNet attribute)
check_indices_agree() (PandasChecks static method)
check_no_gaps() (PandasChecks static method)
check_valid_entries() (PandasChecks static method)
collate_uniform() (in module deepdow.data)
Compose (class in deepdow.data)
compute_covariance() (CovarianceMatrix static method)
compute_distances() (KMeans static method)
compute_sqrt() (CovarianceMatrix static method)
context_vector (AttentionCollapse attribute)
Conv (class in deepdow.layers.transform)
Cov2Corr (class in deepdow.layers.misc)
covariance() (in module deepdow.losses)
covariance_layer (BachelierNet attribute)
CovarianceMatrix (class in deepdow.layers.misc)
create_custom_postfix_str() (ProgressBarCallback static method)
CumulativeReturn (class in deepdow.losses)
cvxpylayer (NumericalMarkowitz attribute)
D
database (History attribute)
deepdow.benchmarks
module
deepdow.callbacks
module
deepdow.data
module
deepdow.experiments
module
deepdow.explain
module
deepdow.layers.allocate
module
deepdow.layers.collapse
module
deepdow.layers.misc
module
deepdow.layers.transform
module
deepdow.losses
module
deepdow.nn
module
deepdow.utils
module
Dropout (class in deepdow.data)
dropout_layer (BachelierNet attribute)
(LinearNet attribute)
DummyNet (class in deepdow.nn)
E
EarlyStoppingCallback (class in deepdow.callbacks)
EarlyStoppingException
ElementCollapse (class in deepdow.layers.collapse)
exp_returns (ThorpNet attribute)
ExponentialCollapse (class in deepdow.layers.collapse)
F
FlexibleDataLoader (class in deepdow.data)
forward() (AnalyticalMarkowitz method)
(AttentionCollapse method)
(AverageCollapse method)
(BachelierNet method)
(Conv method)
(Cov2Corr method)
(CovarianceMatrix method)
(DummyNet method)
(ElementCollapse method)
(ExponentialCollapse method)
(KMeans method)
(LinearNet method)
(MaxCollapse method)
(MultiplyByConstant method)
(NCO method)
(NumericalMarkowitz method)
(Resample method)
(RNN method)
(SoftmaxAllocator method)
(SumCollapse method)
(ThorpNet method)
G
gamma (BachelierNet attribute)
gamma_sqrt (ThorpNet attribute)
gradient_wrt_input() (in module deepdow.explain)
H
History (class in deepdow.experiments)
hparams() (BachelierNet property)
(Benchmark property)
(DummyNet property)
(FlexibleDataLoader property)
(InverseVolatility property)
(KeynesNet property)
(LinearNet property)
(MaximumReturn property)
(MinimumVariance property)
(RigidDataLoader property)
(Run property)
(Singleton property)
(ThorpNet property)
I
initialize() (KMeans method)
InRAMDataset (class in deepdow.data)
InverseVolatility (class in deepdow.benchmarks)
K
KeynesNet (class in deepdow.nn)
KMeans (class in deepdow.layers.misc)
L
LargestWeight (class in deepdow.losses)
launch() (Run method)
linear (LinearNet attribute)
LinearNet (class in deepdow.nn)
log2simple() (in module deepdow.losses)
Loss (class in deepdow.losses)
M
matrix (ThorpNet attribute)
MaxCollapse (class in deepdow.layers.collapse)
MaximumDrawdown (class in deepdow.losses)
MaximumReturn (class in deepdow.benchmarks)
MeanReturns (class in deepdow.losses)
metrics (ProgressBarCallback attribute)
(Run attribute)
metrics() (History property)
metrics_per_epoch() (History method)
min (EarlyStoppingCallback attribute)
(ModelCheckpointCallback attribute)
MinimumVariance (class in deepdow.benchmarks)
MLFlowCallback (class in deepdow.callbacks)
ModelCheckpointCallback (class in deepdow.callbacks)
models (Run attribute)
module
deepdow.benchmarks
deepdow.callbacks
deepdow.data
deepdow.experiments
deepdow.explain
deepdow.layers.allocate
deepdow.layers.collapse
deepdow.layers.misc
deepdow.layers.transform
deepdow.losses
deepdow.nn
deepdow.utils
Multiply (class in deepdow.data)
MultiplyByConstant (class in deepdow.layers.misc)
N
n_epochs_no_improvement (EarlyStoppingCallback attribute)
NCO (class in deepdow.layers.allocate)
Noise (class in deepdow.data)
norm_layer (BachelierNet attribute)
(LinearNet attribute)
norm_layer_1 (KeynesNet attribute)
norm_layer_2 (KeynesNet attribute)
NumericalMarkowitz (class in deepdow.layers.allocate)
O
on_batch_begin() (Callback method)
(Run method)
(TensorBoardCallback method)
on_batch_end() (Callback method)
(ProgressBarCallback method)
(Run method)
(TensorBoardCallback method)
on_epoch_begin() (Callback method)
(ProgressBarCallback method)
(Run method)
on_epoch_end() (Callback method)
(EarlyStoppingCallback method)
(MLFlowCallback method)
(ModelCheckpointCallback method)
(ProgressBarCallback method)
(Run method)
(TensorBoardCallback method)
(ValidationCallback method)
on_train_begin() (BenchmarkCallback method)
(Callback method)
(EarlyStoppingCallback method)
(MLFlowCallback method)
(ModelCheckpointCallback method)
(Run method)
(TensorBoardCallback method)
on_train_end() (Callback method)
(Run method)
on_train_interrupt() (Callback method)
(EarlyStoppingCallback method)
(Run method)
OneOverN (class in deepdow.benchmarks)
optlayer (MaximumReturn attribute)
(MinimumVariance attribute)
output (ProgressBarCallback attribute)
P
PandasChecks (class in deepdow.utils)
portfolio_cumulative_returns() (in module deepdow.losses)
portfolio_opt_layer (BachelierNet attribute)
(KeynesNet attribute)
portfolio_returns() (in module deepdow.losses)
pretty_print() (History method)
prices_to_returns() (in module deepdow.utils)
ProgressBarCallback (class in deepdow.callbacks)
Q
Quantile (class in deepdow.losses)
R
Random (class in deepdow.benchmarks)
raw_to_Xy() (in module deepdow.utils)
Resample (class in deepdow.layers.allocate)
returns_to_Xy() (in module deepdow.utils)
RigidDataLoader (class in deepdow.data)
RNN (class in deepdow.layers.transform)
run (BenchmarkCallback attribute)
Run (class in deepdow.experiments)
run (MLFlowCallback attribute)
(ProgressBarCallback attribute)
(TensorBoardCallback attribute)
(ValidationCallback attribute)
S
scale_features() (in module deepdow.data)
SharpeRatio (class in deepdow.losses)
simple2log() (in module deepdow.losses)
Singleton (class in deepdow.benchmarks)
Softmax (class in deepdow.losses)
SoftmaxAllocator (class in deepdow.layers.allocate)
SortinoRatio (class in deepdow.losses)
SquaredWeights (class in deepdow.losses)
StandardDeviation (class in deepdow.losses)
SumCollapse (class in deepdow.layers.collapse)
T
TargetMeanReturn (class in deepdow.losses)
TargetStandardDeviation (class in deepdow.losses)
temperature (KeynesNet attribute)
(LinearNet attribute)
TensorBoardCallback (class in deepdow.callbacks)
ThorpNet (class in deepdow.nn)
time_collapse_layer (BachelierNet attribute)
transform_layer (BachelierNet attribute)
(KeynesNet attribute)
V
val_dataloaders (Run attribute)
ValidationCallback (class in deepdow.callbacks)
W
WorstReturn (class in deepdow.losses)
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v: v0.1.0
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