mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
1d6eed3a94
* feat(Core): added the first classification model & the feature necessary * feat(Models): added basic transformers model
116 lines
3.4 KiB
Python
116 lines
3.4 KiB
Python
#%% Import all the stuff, load data, define constants
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from sklearn.utils import shuffle
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from load_data import load_files, create_target_cum_forward_returns
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import pandas as pd
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from tensorflow import keras
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from utils.normalize import normalize
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import tensorflow as tf
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from utils.visualize import visualize_loss
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from sklearn.preprocessing import MinMaxScaler
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from utils.evaluate import print_regression_metrics
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import numpy as np
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from utils.rolling import rolling_window
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data = load_files('data/', add_features=True, log_returns=False)
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data.reset_index(drop=True, inplace=True)
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data = data[[column for column in data.columns if not column.endswith('volume')]]
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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"]]
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target_col = 'target'
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data = create_target_cum_forward_returns(data, 'BTC_returns', 10)
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learning_rate = 0.002
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batch_size = 64
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epochs = 100
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split_fraction = 0.715
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train_split = int(split_fraction * int(data.shape[0]))
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past = 10
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future = 1
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start = past + future
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end = start + train_split
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#%% split data into training - validation sets
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train_data = data.loc[0 : train_split - 1]
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val_data = data.loc[train_split:]
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#%% create features and target for training set & keras dataset
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feature_scaler = MinMaxScaler(feature_range= (-1, 1))
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target_scaler = MinMaxScaler(feature_range= (-1, 1))
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x_train = feature_scaler.fit_transform(train_data.drop(target_col, axis=1).values) # you get the mean and std
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y_train = target_scaler.fit_transform(data.iloc[start:end][target_col].values.reshape(-1, 1))
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dataset_train = keras.preprocessing.timeseries_dataset_from_array(
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x_train,
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y_train,
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sequence_length=past,
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batch_size=batch_size,
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)
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#%% create features and target for validation set & keras dataset
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x_end = len(val_data) - past - future
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label_start = train_split + past + future
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x_val = feature_scaler.transform(val_data.drop(target_col, axis=1).iloc[:x_end].values) # you use the training data's mean and std
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y_val = target_scaler.transform(data.iloc[label_start:][target_col].values.reshape(-1, 1))
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dataset_val = keras.utils.timeseries_dataset_from_array(
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x_val,
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y_val,
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sequence_length=past,
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batch_size=batch_size,
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shuffle=False,
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)
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#%%
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for batch in dataset_train.take(10):
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batch_inputs, batch_targets = batch
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print("Input shape:", batch_inputs.shape)
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print("Target shape:", batch_targets.shape)
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# print(batch_inputs)
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# print(batch_targets)
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# %%
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model = keras.Sequential()
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model.add(keras.layers.LSTM(units = 10, return_sequences = True, activation = 'sigmoid', input_shape=(batch_inputs.shape[1], batch_inputs.shape[2])))
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model.add(keras.layers.Dropout(0.4))
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model.add(keras.layers.Dense(units = 64, activation = 'sigmoid'))
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model.add(keras.layers.Dropout(0.4))
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model.add(keras.layers.Dense(units = 32, activation = 'sigmoid'))
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model.add(keras.layers.Dropout(0.4))
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model.add(keras.layers.Dense(units = 1))
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optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
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model.compile(optimizer=optimizer, loss="mean_squared_error")
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model.summary()
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# %%
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path_checkpoint = "model_checkpoint.h5"
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history = model.fit(
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dataset_train,
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epochs=epochs,
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validation_data=dataset_val,
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)
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#%%
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pred = model.predict(rolling_window(x_val, 11))
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pred = pred.reshape(pred.shape[0], 1)
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pred = target_scaler.inverse_transform(pred)
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print_regression_metrics(y_val, pred)
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#%%
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visualize_loss(history, "Training and Validation Loss") |