mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-29 05:21:48 +00:00
ba3baca54c
* ETS prediction * Update 'backtesting' and plots * Knn model * Keras/tf models refactored * ARIMA models and poetry/req updates * edit test * Regression condensed * Hugo server * Add tabulate to nn predictions and fix pylint * Add tabulate to prediction statistics * review comments Co-authored-by: James Maslek <james@macbook-pro.myfiosgateway.com>
71 lines
3.1 KiB
Python
71 lines
3.1 KiB
Python
# This configuration file allows to define the layers that constitute each
|
|
# of the neural network models used by our MLP, RNN and LSTM models.
|
|
# The first layer is the input layer, meaning that to it we add the input shape expected.
|
|
# Note that we can still add the number of neurons on the first layer.
|
|
# The last layer is the output layer, meaning that from it we have our prediction share price days
|
|
# therefore, we don't define units for this layer as this is defined from the terminal directly.
|
|
# There are several types of parameters available to configure your Neural Network model, and more on
|
|
# these can be found in https://www.tensorflow.org/api_docs/python/tf/keras/layers
|
|
# So far the following type of layers are allowed: Dense, SimpleRNN, LSTM, and Dropout
|
|
|
|
# This is an on-going work, hence, feel free to request support for more types of layers
|
|
|
|
# From the terminal, we are able to define:
|
|
# - The number of inputs used by the module
|
|
# - The number of training epochs
|
|
# - The frequency of data training (i.e. do we use all data, or every other day - may speed up training)
|
|
# - The preprocessing type: normalization, standardization, or none
|
|
# - The optimizer
|
|
# - The loss function
|
|
|
|
MultiLayer_Perceptron = [
|
|
{"Dense": {"units": 50, "activation": "relu"}},
|
|
{"Dense": {"units": 100, "activation": "relu"}},
|
|
{"Dense": {"units": 80, "activation": "relu"}},
|
|
{"Dense": {"units": 30, "activation": "relu"}},
|
|
{"Dense": {"activation": "relu"}},
|
|
]
|
|
|
|
Recurrent_Neural_Network = [
|
|
{"SimpleRNN": {"units": 20, "activation": "relu", "return_sequences": True}},
|
|
{"SimpleRNN": {"units": 10, "activation": "relu", "return_sequences": True}},
|
|
{"Dropout": {"rate": 0.1}},
|
|
{"SimpleRNN": {"units": 5, "activation": "relu", "return_sequences": False}},
|
|
{"Dense": {"activation": "linear"}},
|
|
]
|
|
|
|
Long_Short_Term_Memory = [
|
|
{"LSTM": {"units": 25, "activation": "tanh", "return_sequences": True}},
|
|
{"LSTM": {"units": 15, "activation": "tanh", "return_sequences": False}},
|
|
{"Dense": {"activation": "relu"}},
|
|
]
|
|
|
|
Convolutional = [
|
|
{"Conv1D": {"filters": 20, "kernel_size": 10, "activation": "relu"}},
|
|
{"MaxPool1D": {"pool_size": 2}},
|
|
{"Conv1D": {"filters": 10, "kernel_size": 5, "activation": "relu"}},
|
|
{"MaxPool1D": {"pool_size": 2}},
|
|
{"Flatten": {}},
|
|
{"Dense": {"activation": "relu"}},
|
|
]
|
|
|
|
# Optimizers : https://www.tensorflow.org/api_docs/python/tf/keras/optimizers
|
|
# Adam, Adagrad, Adadelta, Adamax,Ftrl, Nadam, Rmsprop, Sgd
|
|
Optimizer = "Adam"
|
|
|
|
# Add early stopping to save time training when validation is plateaued
|
|
# Can be set to large number or None to ignore
|
|
Early_Stop_Patience = 10
|
|
|
|
# Losses https://www.tensorflow.org/api_docs/python/tf/keras/losses
|
|
# mae, mape, mse, msle, poisson, logcosh, kld, hinge, squared_hinge, huber
|
|
Loss = "mae"
|
|
|
|
# Preprocessing technique to use : https://scikit-learn.org/stable/modules/preprocessing.html
|
|
# Options are:
|
|
# minmax - Adjusts data to be between 0 and 1 - Most Used for Stocks
|
|
# standardization - Standardizes data so that it has mean 0 and standard deviation = 1
|
|
# normalization - Normalizes data so that L2 norm (sum x_i^2) = 1
|
|
# none - No preprocessing
|
|
Preprocess = "minmax"
|