Hands-On-Machine-Learning-for-Algorithmic-Trading
Hands-On Machine Learning for Algorithmic Trading, published by Packt
파일 탐색기
- README.md
- 01_build_itch_order_book.ipynb
- 02_normalize_tick_data.ipynb
- create_message_spec.py
- message_types.xlsx
- README.md
- environment.yml
- zipline_example.ipynb
- 01_datareader.ipynb
- 02_lobster_itch_data.ipynb
- 03_quandl_demo.ipynb
- README.md
- edgar_xbrl.ipynb
- README.md
- README.md
- storage_benchmark.ipynb
- __init__.py
- README.md
- __init__.py
- table_spider.py
- __init__.py
- extensions.py
- items.py
- middlewares.py
- pipelines.py
- settings.py
- opentable_selenium.py
- selenium_setup.sh
- check_data.py
- README.md
- sa_selenium.py
- scrape_test.py
- __init__.py
- README.md
- feature_engineering.ipynb
- README.md
- 01_single_factor_zipline.ipynb
- 02_multiple_factors_quantopian_research.ipynb
- 03_performance_eval_alphalens.ipynb
- environment.yml
- README.md
- factor_library.ipynb
- README.md
- ta-lib
- __init__.py
- README.md
- alpha_factor_zipline_with_trades.ipynb
- alpha_factor_zipline_with_trades.py
- environment.yml
- README.md
- environment.yml
- pyfolio_demo.ipynb
- README.md
- deflated_sharpe_ratio.py
- README.md
- mean_variance_opt.py
- mean_variance_optimization.ipynb
- README.md
- kelly_rule.ipynb
- README.md
- __init__.py
- README.md
- 01_machine_learning_workflow.ipynb
- 02_mutual_information.ipynb
- 03_bias_variance.ipynb
- 04_cross_validation.py
- __init__.py
- README.md
- 01_linear_regression_intro.ipynb
- 02_fama_macbeth.ipynb
- 03_linear_regression.ipynb
- 04_logistic_regression.ipynb
- 05_logistic_regression_macro_data.ipynb
- 06_ml_for_trading.ipynb
- __init__.py
- README.md
- 01_stationarity_and_arima.ipynb
- 02_arch_garch_models.ipynb
- 03_vector_autoregressive_model.ipynb
- __init__.py
- README.md
- 01_updating_conjugate_priors.ipynb
- 02_bayesian_logistic_regression.ipynb
- 03_bayesian_sharpe_ratio.ipynb
- 04_linear_regression.ipynb
- 05_bayesian_time_series.ipynb
- 06_stochastic_volatility.ipynb
- __init__.py
- README.md
- 00_data_prep.ipynb
- 01_decision_trees.ipynb
- 02_random_forest.ipynb
- __init__.py
- README.md
- gbm_sklearn_tree.dot
- model_tuning.h5
- aws_gpu.sh
- 01_gbm_baseline.ipynb
- 02_sklearn_gbm_tuning.ipynb
- 03_sklearn_gbm_tuning_results.ipynb
- 04_xgboost_lightgbm_catboost_tuning.ipynb
- 05_xgboost_lightgbm_catboost_tuning_results.ipynb
- 06_model_interpretation.ipynb
- __init__.py
- gbm_params.py
- gbm_utils.py
- README.md
- 00_curse_of_dimensionality.ipynb
- 01_pca_key_ideas.ipynb
- 02_the_math_behind_pca.ipynb
- 03_pca_and_risk_factor_models.ipynb
- manifolds.h5
- 01_manifold_learning_intro.ipynb
- 02_manifold_learning_lle.ipynb
- 03_manifold_learning_tsne_umap.ipynb
- 04_manifold_learning_asset_prices.ipynb
- 01_clustering_algos.ipynb
- 02_kmeans_implementation.ipynb
- 03_kmeans_evaluation.ipynb
- 04_hierarchical_clustering.ipynb
- 05_density_based_clustering.ipynb
- 06_gaussian_mixture_models.ipynb
- HRP.py
- hrp.ipynb
- HRP.py
- hrp_demo.py
- HRP_MC.py
- README.md
- __init__.py
- README.md
- pipeline.svg
- spacy
- spaCy-architecture.svg
- spacy.jpg
- 01_nlp_pipeline with_spaCy.ipynb
- 02_nlp_with_textblob.ipynb
- 03_document_term_matrix.ipynb
- 04_news_text_classification.ipynb
- 05_sentiment_analysis_twitter.ipynb
- 06_sentiment_analysis_yelp.ipynb
- __init__.py
- README.md
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- collect_experiments.py
- lda_earnings_calls.ipynb
- run_experiments.py
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- timings.xlsx
- eval_experiments.py
- lda_yelp_reviews.ipynb
- run_experiments.py
- vis_experiments.py
- README.TXT
- 01_latent_semantic_indexing.ipynb
- 02_probabilistic_latent_analysis.ipynb
- 03_dirichlet_distribution.ipynb
- 04_lda_with_sklearn.ipynb
- 05_lda_with_gensim.ipynb
- __init__.py
- README.md
- README.md
- run.py
- word2vec-sentiments.py
- d2v_test.py
- yelp_sentiment.ipynb
- word2vec.py
- word2vec_wiki.py
- meta.tsv
- word2vec.py
- eval_vecs.py
- preprocessing.ipynb
- word2vec.ipynb
- compile-ops.sh
- run_tf.sh
- word2vec.py
- 01_preprocessing.ipynb
- 02_using_trained_vectors.ipynb
- 03_word2vec.ipynb
- 04_bonus_translation.ipynb
- __init__.py
- README.md
- README.md
- 01_build_and_train_feedforward_nn.ipynb
- 02_how_to_use_keras.ipynb
- 03_how_to_use_pytorch.ipynb
- 04_how_to_use_tensorflow.ipynb
- 05_how_to_optimize_a_NN_architecture.ipynb
- 05_how_to_optimize_a_NN_architecture.py
- __init__.py
- README.md
- timeseries_windowing.gif
- fred.lstm_10_5.weights.best.hdf5
- fred.lstm_12_3.weights.best.hdf5
- fred.lstm_12_6.weights.best.hdf5
- fred.lstm_20_10.weights.best.hdf5
- fred.lstm_25_10.weights.best.hdf5
- fred.lstm_5_5.weights.best.hdf5
- imdb.gru.weights.best.hdf5
- imdb.gru_pretrained.weights.best.hdf5
- quandl.lstm10.weights.best.hdf5
- quandl.lstm25_10.weights.best.hdf5
- quandl.lstm5.weights.best.hdf5
- quandl.lstm50_25.weights.best.hdf5
- quandl.lstm_months_25_10.weights.best.hdf5
- sp500.lstm.weights.best.hdf5
- sp500.lstm10.weights.best.hdf5
- sp500.lstm5.weights.best.hdf5
- 00_build_dataset.ipynb
- 01_univariate_time_series_regression.ipynb
- 02_stacked_lstm_with_feature_embeddings.ipynb
- 03_multivariate_timeseries.ipynb
- 04_sentiment_analysis.ipynb
- 05_sentiment_analysis_pretrained_embeddings.ipynb
- __init__.py
- README.md
- 01_conv_filter_viz.py
- 01_filter_example.ipynb
- 02_mnist_with_ffnn_and_lenet5.ipynb
- 03_cifar10_image_classification.ipynb
- 04_cnn_with_time_series.ipynb
- 05_bottleneck_features.ipynb
- 06_transfer_learning.ipynb
- 07_svhn_preprocessing.ipynb
- 08_svhn_object_detection.ipynb
- __init__.py
- README.md
- fashion_mnist.autencoder.32.weights.hdf5
- fashion_mnist.autencoder_conv.32.weights.hdf5
- fashion_mnist.autencoder_deep.32.weights.hdf5
- fashion_mnist.autencoder_denoise.32.weights
- fashion_mnist.autencoder_l1.32.weights.hdf5
- 01_deep_autoencoders.ipynb
- 02_convolutional_denoising_autoencoders.ipynb
- 03_variational_autoencoder.ipynb
- 04_deep_convolutional_generative_adversarial_network.ipynb
- __init__.py
- README.md
- tsne.h5
- 01_gridworld_dynamic_programming.ipynb
- 02_gridworld_q_learning.ipynb
- 03_lunar_lander_deep_q_learning.ipynb
- 04_q_learning_for_trading.ipynb
- README.md
- trading_env.py
- create_datasets.ipynb
- get_data.py
- README.md
- .gitattributes
- _config.yml
- environment.yml
- environment_linux.yml
- environment_mac_osx.yml
- installation.md
- LICENSE
- README.md
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