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feat(Data): added various data loading config options, walk forward method draft (#9)

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* feat(Eval): added format_data_for_backtest()

* feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO

* feat(Core): added walk forward method of training/testing

* fix(Model): remove the unnecessary softmax activation from the keras models

* feat(Core): added walk_forward_train_test()
This commit is contained in:
Mark Aron Szulyovszky
2021-12-01 09:28:24 +01:00
committed by GitHub
parent d4676e099b
commit 7aedb91069
8 changed files with 678 additions and 75 deletions
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model_classification_keras.py
+18 -10
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@@ -1,6 +1,6 @@
#%% Import all the stuff, load data, define constants
from sklearn.utils import shuffle
from load_data import load_files, create_target_pos_neg_classes
from load_data import load_files, create_target_classes
import pandas as pd
from tensorflow import keras
from utils.normalize import normalize
@@ -13,13 +13,20 @@ from utils.rolling import rolling_window
from keras_models.classification import create_basic_cnn_model, create_basic_lstm_model, create_resnet_cnn_model
from keras_models.classification_transformer import create_basic_transformer_model
data = load_files('data/', add_features=True, log_returns=False)
data.reset_index(drop=True, inplace=True)
data = data[[column for column in data.columns if not column.endswith('volume')]]
# data = data[["BTC_returns", "BTC_mom_10", "BTC_mom_20", "BTC_mom_30", "BTC_mom_60", "BTC_vol_10", "BTC_vol_20", "BTC_vol_60", "day_month", "day_week", "month"]]
data = load_files(path='data/',
own_asset='BTC_ETH',
load_other_assets=True,
log_returns=False,
add_date_features=True,
own_technical_features='level2',
other_technical_features='level2',
exogenous_features='none',
index_column='int'
)
target_col = 'target'
data = create_target_pos_neg_classes(data, 'BTC_ETH_returns', 1)
data = create_target_classes(data, 'BTC_ETH_returns', 1, 'two')
num_classes = 2
learning_rate = 0.002
@@ -71,7 +78,7 @@ dataset_val = keras.utils.timeseries_dataset_from_array(
#%%
for batch in dataset_train.take(10):
for batch in dataset_train.take(1):
batch_inputs, batch_targets = batch
print("Input shape:", batch_inputs.shape)
@@ -83,9 +90,9 @@ n_features = batch_inputs.shape[2]
# print(batch_targets)
# %%
model = create_basic_lstm_model(input_shape=(n_timestamps, n_features), num_classes=num_classes)
# model = create_basic_lstm_model(input_shape=(n_timestamps, n_features), num_classes=num_classes)
# model = create_basic_cnn_model(input_shape=(n_timestamps, n_features), num_classes=num_classes)
# model = create_resnet_cnn_model(input_shape=(n_timestamps, n_features), num_classes=num_classes)
model = create_resnet_cnn_model(input_shape=(n_timestamps, n_features), num_classes=num_classes)
# model = create_basic_transformer_model(
# input_shape=(n_timestamps, n_features),
# n_classes=num_classes,
@@ -99,7 +106,8 @@ model = create_basic_lstm_model(input_shape=(n_timestamps, n_features), num_clas
# )
optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=['accuracy'])
loss = keras.losses.CategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
model.summary()
# %%