\ubcf8 \ube14\ub85c\uadf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \uc774\uc6a9\ud558\uc5ec \uc790\ub3d9 \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95 \uc911 \ubcc0\ubd84 \uc624\ud1a0\uc778\ucf54\ub354(Variational Autoencoder, VAE) \uae30\ubc18\uc758 \uc0dd\uc131 \ubaa8\ub378\ub9c1\uc744 \uc911\uc810\uc801\uc73c\ub85c \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\ub294 \ud000\ud2b8 \ub9e4\ub9e4\ub97c \uc6d0\ud558\ub294 \ud22c\uc790\uc790, \uac1c\ubc1c\uc790 \ubc0f \ub370\uc774\ud130 \uacfc\ud559\uc790 \ubaa8\ub450\uc5d0\uac8c \uc720\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd(Machine Learning)\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uc5ec \ud328\ud134\uc744 \uc778\uc2dd\ud558\uace0 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud558\ub294 \ubd84\uc57c\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd(Deep Learning)\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd(Artificial Neural Networks)\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc774\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ubaa8\ub378\ub9c1\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc5d0\ub294 \ub2e4\uc591\ud55c \uae30\ubc95\uc774 \uc874\uc7ac\ud558\uba70, \uc8fc\ub85c \uc9c0\ub3c4\ud559\uc2b5(Supervised Learning), \ube44\uc9c0\ub3c4\ud559\uc2b5(Unsupervised Learning), \uac15\ud654\ud559\uc2b5(Reinforcement Learning)\uc73c\ub85c \ubd84\ub958\ub429\ub2c8\ub2e4.<\/p>\n
\ub525\ub7ec\ub2dd \uae30\ubc95\uc5d0\uc11c \uc8fc\ub85c \uc0ac\uc6a9\ub418\ub294 \uc2e0\uacbd\ub9dd\uc73c\ub85c\ub294 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN), \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN), \uadf8\ub9ac\uace0 \ucd5c\uadfc \uac01\uad11\ubc1b\uace0 \uc788\ub294 \ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer) \ubaa8\ub378\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ubcc0\ubd84 \uc624\ud1a0\uc778\ucf54\ub354(Variational Autoencoder, VAE)\ub294 \uc0dd\uc131 \ubaa8\ub378\ub9c1\uc758 \uc77c\uc885\uc73c\ub85c, \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\ub85c\ubd80\ud130 \uc0c8\ub85c\uc6b4 \uc0d8\ud50c\uc744 \uc0dd\uc131\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub2a5\ub825\uc744 \uc9c0\ub2c8\uace0 \uc788\uc2b5\ub2c8\ub2e4. VAE\ub294 \uc778\ucf54\ub354\uc640 \ub514\ucf54\ub354\ub85c \uad6c\uc131\ub418\uba70, \ub370\uc774\ud130\uc758 \uc7a0\uc7ac\uc801\uc778 \ud45c\ud604(latent representation)\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n
VAE\uc758 \uae30\ubcf8 \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\ubcc0\ubd84 \uc624\ud1a0\uc778\ucf54\ub354\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \uae08\uc735 \ub370\uc774\ud130\ub97c \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9, \uac70\ub798\ub7c9, \uae30\uc220\uc801 \uc9c0\ud45c \ub4f1\uc758 \ub370\uc774\ud130\ub97c \ud3ec\ud568\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc758 \ud615\uc0c1(shape)\uacfc \ud2b9\uc131\uc744 \uc774\ud574\ud558\uace0, \ud544\uc694\ud55c \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
\ub370\uc774\ud130 \uc218\uc9d1 \ubc29\ubc95\uc740 API, \uc6f9 \uc2a4\ud06c\ub798\ud551 \ub610\ub294 \uacf5\uac1c\ub41c \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, Yahoo Finance API\ub97c \ud65c\uc6a9\ud558\uc5ec \uc6d0\ud558\ub294 \uc885\ubaa9\uc758 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \uacb0\uce21\uce58\ub97c \ucc98\ub9ac\ud558\uace0, \ud544\uc694\uc2dc \uc815\uaddc\ud654(Normalization)\ub97c \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\ub294 \ubaa8\ub378 \ud559\uc2b5\uc758 \uc131\ub2a5\uc744 \ub192\uc774\ub294 \uc911\uc694\ud55c \uacfc\uc815\uc785\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c VAE \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uc5ec \uae08\uc735 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \uc0dd\uc131 \ubaa8\ub378\ub9c1\uc744 \uc2dc\ub3c4\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 VAE\uc758 \uae30\ubcf8 \uad6c\uc870\ub97c \uad6c\ud604\ud558\uae30 \uc704\ud55c Python \ucf54\ub4dc \uc608\uc2dc\uc785\ub2c8\ub2e4.<\/p>\n
\n import numpy as np\n import tensorflow as tf\n from tensorflow.keras import layers, losses\n\n latent_dim = 2 # \uc7a0\uc7ac \uacf5\uac04\uc758 \ucc28\uc6d0\n\n # \uc778\ucf54\ub354\n encoder_inputs = layers.Input(shape=(original_dim,))\n x = layers.Dense(64, activation='relu')(encoder_inputs)\n x = layers.Dense(32, activation='relu')(x)\n z_mean = layers.Dense(latent_dim)(x)\n z_log_var = layers.Dense(latent_dim)(x)\n encoder = tf.keras.Model(encoder_inputs, [z_mean, z_log_var])\n\n # \uc0d8\ud50c\ub9c1\n def sampling(args):\n z_mean, z_log_var = args\n epsilon = tf.random.normal(shape=(tf.shape(z_mean)[0], latent_dim))\n return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n\n z = layers.Lambda(sampling)([z_mean, z_log_var])\n\n # \ub514\ucf54\ub354\n decoder_inputs = layers.Input(shape=(latent_dim,))\n x = layers.Dense(32, activation='relu')(decoder_inputs)\n x = layers.Dense(64, activation='relu')(x)\n decoder_outputs = layers.Dense(original_dim, activation='sigmoid')(x)\n decoder = tf.keras.Model(decoder_inputs, decoder_outputs)\n\n # VAE \ubaa8\ub378\n vae_outputs = decoder(z)\n vae = tf.keras.Model(encoder_inputs, vae_outputs)\n\n # \uc190\uc2e4 \ud568\uc218 \uc815\uc758\n reconstruction_loss = losses.binary_crossentropy(encoder_inputs, vae_outputs)\n reconstruction_loss *= original_dim\n kl_loss = -0.5 * tf.reduce_sum(1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var), axis=-1)\n vae_loss = tf.reduce_mean(reconstruction_loss + kl_loss)\n vae.add_loss(vae_loss)\n vae.compile(optimizer='adam')\n <\/code>\n <\/pre>\n5. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\ubaa8\ub378\uc774 \uc900\ube44\ub418\uc5c8\uc73c\uba74, \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec VAE \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4. \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c \uc190\uc2e4(loss) \ud568\uc218\ub97c \ubaa8\ub2c8\ud130\ub9c1\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
\n # \ubaa8\ub378 \ud559\uc2b5\n vae.fit(x_train, epochs=50, batch_size=128)\n <\/code>\n <\/pre>\n6. \uc0dd\uc131 \ubaa8\ub378\ub9c1<\/h2>\n
\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc0d8\ud50c\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc7a0\uc7ac \uacf5\uac04\uc5d0\uc11c \uc0d8\ud50c\uc744 \uc0dd\uc131\ud558\uace0, \uc774\ub97c \ub514\ucf54\ub354\uc5d0 \ud1b5\uacfc\uc2dc\ucf1c \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130 \uc608\uc81c\ub97c \ub9cc\ub4e4\uc5b4 \ub0c5\ub2c8\ub2e4.<\/p>\n
6.1 \uc0d8\ud50c \uc0dd\uc131 \ucf54\ub4dc<\/h3>\n\n # \uc0d8\ud50c \uc0dd\uc131\n z_samples = np.random.normal(size=(1000, latent_dim))\n generated_samples = decoder.predict(z_samples)\n <\/code>\n <\/pre>\n7. \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uc218\ub9bd<\/h2>\n
\uc0dd\uc131\ub41c \ub370\uc774\ud130\ub97c \ubc14\ud0d5\uc73c\ub85c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc138\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \uc608\uce21\ud558\uc5ec \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \ubd84\uc11d\ud558\uc5ec \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7.1 \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998 \uc608\uc2dc<\/h3>\n
\uac04\ub2e8\ud55c \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc758 \uc608\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\n def trading_strategy(generated_data):\n buy_signals = []\n sell_signals = []\n for i in range(1, len(generated_data)):\n if generated_data[i] > generated_data[i - 1]: # \uac00\uaca9\uc774 \uc0c1\uc2b9\ud558\uba74 \ub9e4\uc218\n buy_signals.append(generated_data[i])\n else: # \uac00\uaca9\uc774 \ud558\ub77d\ud558\uba74 \ub9e4\ub3c4\n sell_signals.append(generated_data[i])\n return buy_signals, sell_signals\n <\/code>\n <\/pre>\n8. \uc131\ub2a5 \ud3c9\uac00\uc640 \ud29c\ub2dd<\/h2>\n
\uad6c\ucd95\ud55c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574, \uc218\uc775\ub960, \ucd5c\ub300 \ud558\ub77d\ud3ed, \uc0e4\ud504 \ube44\uc728\uacfc \uac19\uc740 \ub2e4\uc591\ud55c \uc9c0\ud45c\ub97c \ud65c\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4. \ubaa8\ub378\uc758 \uacfc\uc801\ud569(overfitting)\uc744 \ud53c\ud558\uae30 \uc704\ud574 \uad50\ucc28 \uac80\uc99d(Cross Validation)\uacfc \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd(Hyperparameter Tuning) \ubc29\ubc95\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
9. \uacb0\ub860<\/h2>\n
\ubcc0\ubd84 \uc624\ud1a0\uc778\ucf54\ub354\ub97c \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uad6c\ucd95 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. VAE\ub294 \uc0dd\uc131 \ubaa8\ub378\ub9c1\uc5d0 \uc720\uc6a9\ud55c \uac15\ub825\ud55c \ub3c4\uad6c\ub85c, \uae08\uc735 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uae4a\uc740 \ud1b5\ucc30\uc744 \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud000\ud2b8 \ub9e4\ub9e4\uc758 \uae30\ucd08\ub97c \uc774\ud574\ud558\uace0, \uc2e4\uc9c8\uc801\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c\ub3c4 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uacfc \uad00\ub828\ub41c \ub2e4\uc591\ud55c \uc8fc\uc81c\ub97c \ub2e4\ub8f0 \uc608\uc815\uc774\ub2c8, \ub9ce\uc740 \uad00\uc2ec \ubd80\ud0c1\ub4dc\ub9bd\ub2c8\ub2e4!<\/p>\n