# ML for Trading - 2<sup>nd</sup> Edition
This [book](https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d) aims to show how ML can add value to algorithmic trading strategies in a practical yet comprehensive way. It covers a broad range of ML techniques from linear regression to deep reinforcement learning and demonstrates how to build, backtest, and evaluate a trading strategy driven by model predictions.
In four parts with **23 chapters plus an appendix**, it covers on **over 800 pages**:
- important aspects of data sourcing, **financial feature engineering**, and portfolio management,
- the design and evaluation of long-short **strategies based on supervised and unsupervised ML algorithms**,
- how to extract tradeable signals from **financial text data** like SEC filings, earnings call transcripts or financial news,
- using **deep learning** models like CNN and RNN with market and alternative data, how to generate synthetic data with generative adversarial networks, and training a trading agent using deep reinforcement learning
<p align="center">
<a href="https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d">
<img src="https://ml4t.s3.amazonaws.com/assets/cover_toc_gh.png" width="75%">
</a>
</p>
This repo contains **over 150 notebooks** that put the concepts, algorithms, and use cases discussed in the book into action. They provide numerous examples that show
- how to work with and extract signals from market, fundamental and alternative text and image data,
- how to train and tune models that predict returns for different asset classes and investment horizons, including how to replicate recently published research, and
- how to design, backtest, and evaluate trading strategies.
> We **highly recommend** to review the notebooks while reading the book; they are usually in executed state and often contain additional information that the space constraints of the book did not permit to include.
## What's new in the 2<sup>nd</sup> Edition?
First and foremost, this [book](https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=VMKJPZC4N36TTZZCWATP&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=8f331266-0d21-4c76-a3eb-d2e61d23bb31&pd_rd_w=kVGNF&pd_rd_wg=LYLKH&ref_=pd_gw_ci_mcx_mr_hp_d) demonstrates how you can extract signals from a diverse set of data sources and design trading strategies for different asset classes using a broad range of supervised, unsupervised, and reinforcement learning algorithms. It also provides relevant mathematical and statistical knowledge to facilitate the tuning of an algorithm or the interpretation of the results. Furthermore, it covers the financial background that will help you work with market and fundamental data, extract informative features, and manage the performance of a trading strategy.
From a practical standpoint, the 2nd edition aims to equip you with the conceptual understanding and tools to develop your own ML-based trading strategies. To this end, it frames ML as a critical element in a process rather than a standalone exercise, introducing the end-to-end ML for trading workflow from data sourcing, feature engineering, and model optimization to strategy design and backtesting.
More specifically, the ML4T workflow starts with generating ideas for a well-defined investment universe, collecting relevant data, and extracting informative features. It also involves designing, tuning, and evaluating ML models suited to the predictive task. Finally, it requires developing trading strategies to act on the models' predictive signals, as well as simulating and evaluating their performance on historical data using a backtesting engine. Once you decide to execute an algorithmic strategy in a real market, you will find yourself iterating over this workflow repeatedly to incorporate new information and a changing environment.
<p align="center">
<img src="https://i.imgur.com/kcgItgp.png" width="75%">
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The [second edition](https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d)'s emphasis on the ML4t workflow translates into a new chapter on [strategy backtesting](08_ml4t_workflow), a new [appendix](24_alpha_factor_library) describing over 100 different alpha factors, and many new practical applications. We have also rewritten most of the existing content for clarity and readability.
The trading applications now use a broader range of data sources beyond daily US equity prices, including international stocks and ETFs. It also demonstrates how to use ML for an intraday strategy with minute-frequency equity data. Furthermore, it extends the coverage of alternative data sources to include SEC filings for sentiment analysis and return forecasts, as well as satellite images to classify land use.
Another innovation of the second edition is to replicate several trading applications recently published in top journals:
- [Chapter 18](18_convolutional_neural_nets) demonstrates how to apply convolutional neural networks to time series converted to image format for return predictions based on [Sezer and Ozbahoglu](https://www.researchgate.net/publication/324802031_Algorithmic_Financial_Trading_with_Deep_Convolutional_Neural_Networks_Time_Series_to_Image_Conversion_Approach) (2018).
- [Chapter 20](20_autoencoders_for_conditional_risk_factors) shows how to extract risk factors conditioned on stock characteristics for asset pricing using autoencoders based on [Autoencoder Asset Pricing Models](https://www.aqr.com/Insights/Research/Working-Paper/Autoencoder-Asset-Pricing-Models) by Shihao Gu, Bryan T. Kelly, and Dacheng Xiu (2019), and
- [Chapter 21](21_gans_for_synthetic_time_series) shows how to create synthetic training data using generative adversarial networks based on [Time-series Generative Adversarial Networks](https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks) by Jinsung Yoon, Daniel Jarrett, and Mihaela van der Schaar (2019).
All applications now use the latest available (at the time of writing) software versions such as pandas 1.0 and TensorFlow 2.2. There is also a customized version of Zipline that makes it easy to include machine learning model predictions when designing a trading strategy.
## Installation and Data Sources
- For instructions on using a Docker image or setting up various `conda` environments to install the packages used in the notebooks, see [here](installation/README.md).
- To download and preprocess many of the data sources used in this book see [create_datasets](data/create_datasets.ipynb).
# Chapter Summary
The [book](https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d) has four parts that address different challenges that arise when sourcing and working with market, fundamental and alternative data sourcing, developing ML solutions to various predictive tasks in the trading context, and designing and evaluating a trading strategy that relies on predictive signals generated by an ML model.
> The directory for each chapter contains a README with additional information on content, code examples and additional resources.
[Part 1: From Data to Strategy Development](#part-1-from-data-to-strate
Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original:机器学习用于算法交易，第二版-Packt出版 （360个子文件） 
scrapy.cfg 231B kc_house_data.csv 2.4MB us_equities_meta_data.csv 541KB wiki_stocks.csv 81KB lda_multicore_test_results.csv 318B TED2013_sample.en 2KB TED2013_sample.es 2KB
timeseries_windowing.gif 384KB 01_deep_convolutional_generative_adversarial_network.ipynb 18.43MB 03_manifold_learning_tsne_umap.ipynb 12.67MB 02_manifold_learning_lle.ipynb 9.57MB 06_evaluate_trading_signals.ipynb 7.03MB 02_common_alpha_factors.ipynb 3.88MB 02_pymc3_workflow.ipynb 3.62MB 07_model_interpretation.ipynb 3.05MB 03_101_formulaic_alphas.ipynb 2.82MB 01_boosting_baseline.ipynb 2.59MB 07_backtesting_with_zipline.ipynb 2.51MB 01_manifold_learning_intro.ipynb 2.46MB 05_backtesting_with_zipline.ipynb 2.39MB 08_backtesting_with_zipline.ipynb 2.36MB 09_backtesting_with_zipline.ipynb 2.22MB 03_ml4t_with_zipline.ipynb 2.13MB 02_backtesting_with_zipline.ipynb 1.99MB 02_arima_models.ipynb 1.88MB 07_alphalens_analysis.ipynb 1.74MB 01_hierarchical_risk_parity.ipynb 1.62MB 03_backtesting_with_backtrader.ipynb 1.61MB 04_lda_with_sklearn.ipynb 1.51MB 03_bayesian_sharpe_ratio.ipynb 1.51MB 04_factor_evaluation.ipynb 1.46MB 01_deep_autoencoders.ipynb 1.45MB 03_image_classification_with_alexnet.ipynb 1.3MB 06_performance_eval_alphalens.ipynb 1.28MB 03_kalman_filter_and_wavelets.ipynb 1.23MB 07_pairs_trading_backtest.ipynb 1.22MB 04_vector_autoregressive_model.ipynb 1.03MB 05_density_based_clustering.ipynb 943KB 01_machine_learning_workflow.ipynb 937KB 03_variational_autoencoder.ipynb 866KB 07_sec_filings_return_prediction.ipynb 831KB 06_lda_earnings_calls.ipynb 809KB 04_rolling_regression.ipynb 760KB 01_clustering_algos.ipynb 757KB 02_rebuild_nasdaq_order_book.ipynb 747KB 03_kmeans_evaluation.ipynb 639KB 05_predicting_stock_returns_with_linear_regression.ipynb 634KB 04_mean_variance_optimization.ipynb 593KB 04_optimizing_a_NN_architecture_for_trading.ipynb 558KB 01_linear_regression_intro.ipynb 532KB 03_arch_garch_models.ipynb 509KB 01_decision_trees.ipynb 494KB 03_pyfolio_demo.ipynb 494KB 01_tsa_and_stationarity.ipynb 485KB 07_lda_financial_news.ipynb 474KB 01_filter_example.ipynb 457KB 02_TimeGAN_TF2.ipynb 455KB 02_kmeans_implementation.ipynb 425KB 02_the_math_behind_pca.ipynb 425KB 06_conditional_autoencoder_for_asset_pricing_model.ipynb 424KB 11_satellite_images.ipynb 420KB 03_sklearn_gbm_tuning_results.ipynb 408KB 10_transfer_learning.ipynb 399KB 05_random_forest_return_signals.ipynb 386KB 04_hierarchical_clustering.ipynb 376KB edgar_xbrl.ipynb 358KB 02_how_to_use_talib.ipynb 358KB 03_evaluating_synthetic_data.ipynb 351KB 06_gaussian_mixture_models.ipynb 349KB 05_cointegration_tests.ipynb 343KB 02_bagged_decision_trees.ipynb 337KB 03_lobster_itch_data.ipynb 334KB 05_stochastic_volatility.ipynb 326KB 05_zipline_data_demo.ipynb 320KB 03_normalize_tick_data.ipynb 295KB 01_build_and_train_feedforward_nn.ipynb 294KB 00_data_prep.ipynb 293KB 01_univariate_time_series_regression.ipynb 290KB 01_pandas_datareader_demo.ipynb 289KB 06_statistical_arbitrage_with_cointegrated_pairs.ipynb 282KB 03_bias_variance.ipynb 277KB 03_pca_and_risk_factor_models.ipynb 276KB 04_japanese_equity_features.ipynb 267KB 01_pca_key_ideas.ipynb 260KB 04_multivariate_timeseries.ipynb 260KB 09_bottleneck_features.ipynb 243KB 03_preparing_the_model_data.ipynb 233KB 02_fama_macbeth.ipynb 226KB 04_manifold_learning_asset_prices.ipynb 224KB 01_backtest_with_trades.ipynb 221KB 05_kelly_rule.ipynb 207KB 02_digit_classification_with_lenet5.ipynb 206KB 08_doc2vec_yelp_sentiment.ipynb 201KB 07_factor_library_quantopian.ipynb 198KB 02_backtest_with_pf_optimization.ipynb 195KB 04_statistical_inference_of_stock_returns_with_statsmodels.ipynb 193KB 02_convolutional_denoising_autoencoders.ipynb 189KB 01_using_pretrained_vectors.ipynb 185KB 04_build_us_stock_dataset.ipynb 177KB 05_alphalens_analysis.ipynb 171KB共 360 条
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