color version of all figures

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diff --git a/README.md b/README.md
@@ -14,12 +14,12 @@ In four parts with **23 chapters plus an appendix**, it covers on **over 800 pag
 </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
+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. 
+> We **highly recommend** reviewing the notebooks while reading the book; they are usually in an executed state and often contain additional information that we could not include due to the book's space constraints.  
 
 ## What's new in the 2<sup>nd</sup> Edition?
 
@@ -44,13 +44,18 @@ Another innovation of the second edition is to replicate several trading applica
 
 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, Data Sources and Bug Reports
+## Installation instructions, Data Sources and Bug Reports
+
+The code examples rely on a wide range of Python libraries from the data science and finance domains. To facilitate installation, we use [Docker](https://www.docker.com/get-started) to provide containerized [conda](https://docs.conda.io/en/latest/) environments.
+
+- The [installation](installation/README.md) directory contains detailed instructions on setting up and using a Docker image to run the notebooks. It also contains configuration files for setting up various `conda` environments and install the packages used in the notebooks directly on your machine if you prefer (and, dpending on your system, are prepared to go the extra mile).
+- To download and preprocess many of the data sources used in this book, see the instructions in the [README](data/README.md) file alongside various notebooks in the [data](data) directory.
 
-- 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).
 > If you have any difficulties installing the environments, downloading the data or running the code, please raise a **GitHub issue** in the repo ([here](https://github.com/stefan-jansen/machine-learning-for-trading/issues)). Working with GitHub issues has been described [here](https://guides.github.com/features/issues/).
 
-> Update: You can download the **algoseek** data used in the book [here](https://www.algoseek.com/products.html). I will add the preprocessing instructions shortly.
+> **Update**: You can download the **[algoseek](https://www.algoseek.com)** data used in the book [here](https://www.algoseek.com/ml4t-book-data.html). I will add the preprocessing instructions shortly.
+
+> **Update**: The [figures](figures) directory contains color versions of the charts used in the book. 
 
 # Chapter Summary
 

diff --git a/figures/Appendix/Figure A.1 - Comparison of MAs for AAPL closing price.png b/figures/Appendix/Figure A.1 - Comparison of MAs for AAPL closing price.png
diff --git a/...ure A.10 - Smoothed and unsmoothed StochRSI series for the AAPL stock price.png b/...ure A.10 - Smoothed and unsmoothed StochRSI series for the AAPL stock price.png
diff --git a/figures/Appendix/Figure A.11 - STOCH series for the AAPL stock price.png b/figures/Appendix/Figure A.11 - STOCH series for the AAPL stock price.png
diff --git a/figures/Appendix/Figure A.12 - ULTOSC series for the AAPL stock price.png b/figures/Appendix/Figure A.12 - ULTOSC series for the AAPL stock price.png
diff --git a/figures/Appendix/Figure A.13 - WILLR series for the AAPL stock price.png b/figures/Appendix/Figure A.13 - WILLR series for the AAPL stock price.png
diff --git a/figures/Appendix/Figure A.14 - ADOSC series for the AAPL stock price.png b/figures/Appendix/Figure A.14 - ADOSC series for the AAPL stock price.png
diff --git a/figures/Appendix/Figure A.15 - ATR series for the AAPL stock price.png b/figures/Appendix/Figure A.15 - ATR series for the AAPL stock price.png
diff --git a/figures/Appendix/Figure A.16 - Alpha 001 histogram and scatter plot.png b/figures/Appendix/Figure A.16 - Alpha 001 histogram and scatter plot.png
diff --git a/figures/Appendix/Figure A.17 - SHAP values for common and formulaic alphas.png b/figures/Appendix/Figure A.17 - SHAP values for common and formulaic alphas.png
diff --git a/figures/Appendix/Figure A.18 - Rank correlation of performance metrics.png b/figures/Appendix/Figure A.18 - Rank correlation of performance metrics.png
diff --git a/figures/Appendix/Figure A.19 - Top 25 features for each performance metric.png b/figures/Appendix/Figure A.19 - Top 25 features for each performance metric.png
diff --git a/figures/Appendix/Figure A.2 - Bollinger Bands for AAPL close price in 2012.png b/figures/Appendix/Figure A.2 - Bollinger Bands for AAPL close price in 2012.png
diff --git a/figures/Appendix/Figure A.20 - Alphalens performance metric for Alpha 54.png b/figures/Appendix/Figure A.20 - Alphalens performance metric for Alpha 54.png
diff --git a/figures/Appendix/Figure A.3 - Normalized Bollinger Band indicators.png b/figures/Appendix/Figure A.3 - Normalized Bollinger Band indicators.png
diff --git a/.../Appendix/Figure A.4 - Distribution of normalized Bollinger Band indicators.png b/.../Appendix/Figure A.4 - Distribution of normalized Bollinger Band indicators.png
diff --git a/figures/Appendix/Figure A.5 - Parabolic SAR for AAPL stock price.png b/figures/Appendix/Figure A.5 - Parabolic SAR for AAPL stock price.png
diff --git a/figures/Appendix/Figure A.6 - PLUS_DMMINUS_DM for AAPL stock price.png b/figures/Appendix/Figure A.6 - PLUS_DMMINUS_DM for AAPL stock price.png
diff --git a/figures/Appendix/Figure A.7 - ADX for the AAPL stock price series.png b/figures/Appendix/Figure A.7 - ADX for the AAPL stock price series.png
diff --git a/figures/Appendix/Figure A.8 - Aroon Oscillator for the AAPL stock price series.png b/figures/Appendix/Figure A.8 - Aroon Oscillator for the AAPL stock price series.png
diff --git a/...Appendix/Figure A.9 - The three MACD series for the AAPL stock price series.png b/...Appendix/Figure A.9 - The three MACD series for the AAPL stock price series.png
diff --git a/figures/Chapter_1/Figure 1.1 - The ML4T workflow.png b/figures/Chapter_1/Figure 1.1 - The ML4T workflow.png
diff --git a/figures/Chapter_1/Figure 1.2 - The alpha factor research process.png b/figures/Chapter_1/Figure 1.2 - The alpha factor research process.png
diff --git a/... - How evidence updates the prior to the posterior probability distribution.png b/... - How evidence updates the prior to the posterior probability distribution.png
diff --git a/figures/Chapter_10/Figure 10.10 - Forest and energy plot.png b/figures/Chapter_10/Figure 10.10 - Forest and energy plot.png
diff --git a/figures/Chapter_10/Figure 10.11 - Single-variable model predictions.png b/figures/Chapter_10/Figure 10.11 - Single-variable model predictions.png
diff --git a/figures/Chapter_10/Figure 10.12 - The Bayesian SR in plate notation.png b/figures/Chapter_10/Figure 10.12 - The Bayesian SR in plate notation.png
diff --git a/...apter_10/Figure 10.13 - The posterior distribution for the model parameters.png b/...apter_10/Figure 10.13 - The posterior distribution for the model parameters.png
diff --git a/...10/Figure 10.14 - The difference between two Bayesian SRs in plate notation.png b/...10/Figure 10.14 - The difference between two Bayesian SRs in plate notation.png
diff --git a/... - The posterior distributions for the differences between two Bayesian SRs.png b/... - The posterior distributions for the differences between two Bayesian SRs.png
diff --git a/...- Price series and correlation over time of two pairs of trading candidates.png b/...- Price series and correlation over time of two pairs of trading candidates.png
diff --git a/...ter_10/Figure 10.17 - Changes in intercept and slope coefficients over time.png b/...ter_10/Figure 10.17 - Changes in intercept and slope coefficients over time.png
diff --git a/figures/Chapter_10/Figure 10.18 - Rolling regression lines and price series.png b/figures/Chapter_10/Figure 10.18 - Rolling regression lines and price series.png
diff --git a/figures/Chapter_10/Figure 10.19 - Daily S&P 500 log returns.png b/figures/Chapter_10/Figure 10.19 - Daily S&P 500 log returns.png
diff --git a/...e probability that the S&P 500 goes up the next day after up to 500 updates.png b/...e probability that the S&P 500 goes up the next day after up to 500 updates.png
diff --git a/...es/Chapter_10/Figure 10.20 - Trace plot for the stochastic volatility model.png b/...es/Chapter_10/Figure 10.20 - Trace plot for the stochastic volatility model.png
diff --git a/figures/Chapter_10/Figure 10.21 - Model.png b/figures/Chapter_10/Figure 10.21 - Model.png
diff --git a/...tion between recession and leading indicators for horizons from 1-24 months.png b/...tion between recession and leading indicators for horizons from 1-24 months.png
diff --git a/...hapter_10/Figure 10.4 - Leading indicator distributions by recession status.png b/...hapter_10/Figure 10.4 - Leading indicator distributions by recession status.png
diff --git a/figures/Chapter_10/Figure 10.5 - Bayesian logistic regression.png b/figures/Chapter_10/Figure 10.5 - Bayesian logistic regression.png
diff --git a/figures/Chapter_10/Figure 10.6 - Traces after 100 samples.png b/figures/Chapter_10/Figure 10.6 - Traces after 100 samples.png
diff --git a/figures/Chapter_10/Figure 10.7 - Traces after an additional 50,000 samples.png b/figures/Chapter_10/Figure 10.7 - Traces after an additional 50,000 samples.png
diff --git a/...es/Chapter_10/Figure 10.8 - Credible intervals for yield curve and leverage.png b/...es/Chapter_10/Figure 10.8 - Credible intervals for yield curve and leverage.png
diff --git a/...es/Chapter_10/Figure 10.9 - Traces after 400 and after over 200,000 samples.png b/...es/Chapter_10/Figure 10.9 - Traces after 400 and after over 200,000 samples.png
diff --git a/figures/Chapter_11/Figure 11.1 - How a decision tree learns rules from data.png b/figures/Chapter_11/Figure 11.1 - How a decision tree learns rules from data.png
diff --git a/...1.10 - Feature importance for the best regression and classification models.png b/...1.10 - Feature importance for the best regression and classification models.png
diff --git a/...re 11.11 - Bias-variance breakdown for individual and bagged decision trees.png b/...re 11.11 - Bias-variance breakdown for individual and bagged decision trees.png
diff --git a/figures/Chapter_11/Figure 11.12 - How a random forest grows individual trees.png b/figures/Chapter_11/Figure 11.12 - How a random forest grows individual trees.png
diff --git a/figures/Chapter_11/Figure 11.13 - Random forest feature importance.png b/figures/Chapter_11/Figure 11.13 - Random forest feature importance.png
diff --git a/... of the daily mean information coefficient for various model configurations.png b/... of the daily mean information coefficient for various model configurations.png
diff --git a/...confidence intervals for the various random forest configuration parameters.png b/...confidence intervals for the various random forest configuration parameters.png
diff --git a/figures/Chapter_11/Figure 11.16 - Alphalens factor signal evaluation.png b/figures/Chapter_11/Figure 11.16 - Alphalens factor signal evaluation.png
diff --git a/figures/Chapter_11/Figure 11.17 - Pyfolio strategy evaluation.png b/figures/Chapter_11/Figure 11.17 - Pyfolio strategy evaluation.png
diff --git a/... 11.2 - Mutual information for features and returns or price move direction.png b/... 11.2 - Mutual information for features and returns or price move direction.png
diff --git a/figures/Chapter_11/Figure 11.3 - OLS results and regression tree.png b/figures/Chapter_11/Figure 11.3 - OLS results and regression tree.png
diff --git a/...gure 11.4 - Decision surfaces for linear regression and the regression tree.png b/...gure 11.4 - Decision surfaces for linear regression and the regression tree.png
diff --git a/figures/Chapter_11/Figure 11.5 - Classification loss functions.png b/figures/Chapter_11/Figure 11.5 - Classification loss functions.png
diff --git a/figures/Chapter_11/Figure 11.6 - Visualization of a classification tree.png b/figures/Chapter_11/Figure 11.6 - Visualization of a classification tree.png
diff --git a/figures/Chapter_11/Figure 11.7 - Visualization of a classification tree.png b/figures/Chapter_11/Figure 11.7 - Visualization of a classification tree.png
diff --git a/figures/Chapter_11/Figure 11.8 - Train and validation scores for both models.png b/figures/Chapter_11/Figure 11.8 - Train and validation scores for both models.png
diff --git a/...Chapter_11/Figure 11.9 - Learning curves for the best version of each model.png b/...Chapter_11/Figure 11.9 - Learning curves for the best version of each model.png
diff --git a/figures/Chapter_12/Figure 12.1 - AdaBoost cross-validation performance.png b/figures/Chapter_12/Figure 12.1 - AdaBoost cross-validation performance.png
diff --git a/...Chapter_12/Figure 12.10 - Average and cumulative returns by factor quantile.png b/...Chapter_12/Figure 12.10 - Average and cumulative returns by factor quantile.png
diff --git a/.../Chapter_12/Figure 12.11 - Rolling IC for 1-day and 21-day return forecasts.png b/.../Chapter_12/Figure 12.11 - Rolling IC for 1-day and 21-day return forecasts.png
diff --git a/figures/Chapter_12/Figure 12.12 - LightGBM feature importance.png b/figures/Chapter_12/Figure 12.12 - LightGBM feature importance.png
diff --git a/...2.13 - Partial dependence plots for scikit-learn GradientBoostingClassifier.png b/...2.13 - Partial dependence plots for scikit-learn GradientBoostingClassifier.png
diff --git a/figures/Chapter_12/Figure 12.14 - Partial dependence as a 3D plot.png b/figures/Chapter_12/Figure 12.14 - Partial dependence as a 3D plot.png
diff --git a/figures/Chapter_12/Figure 12.15 - SHAP summary plots.png b/figures/Chapter_12/Figure 12.15 - SHAP summary plots.png
diff --git a/figures/Chapter_12/Figure 12.16 - SHAP force plot.png b/figures/Chapter_12/Figure 12.16 - SHAP force plot.png
diff --git a/figures/Chapter_12/Figure 12.17 - SHAP clustered force plot.png b/figures/Chapter_12/Figure 12.17 - SHAP clustered force plot.png
diff --git a/figures/Chapter_12/Figure 12.18 - SHAP interaction plot.png b/figures/Chapter_12/Figure 12.18 - SHAP interaction plot.png
diff --git a/...re 12.19 - Strategy performance—cumulative returns and rolling Sharpe ratio.png b/...re 12.19 - Strategy performance—cumulative returns and rolling Sharpe ratio.png
diff --git a/figures/Chapter_12/Figure 12.2 - The gradient boosting algorithm.png b/figures/Chapter_12/Figure 12.2 - The gradient boosting algorithm.png
diff --git a/...apter_12/Figure 12.20 - Information coefficient for high-frequency features.png b/...apter_12/Figure 12.20 - Information coefficient for high-frequency features.png
diff --git a/...er_12/Figure 12.21 - Average 1-min returns and cumulative returns by decile.png b/...er_12/Figure 12.21 - Average 1-min returns and cumulative returns by decile.png
diff --git a/...oss-validation performance of the scikit-learn gradient boosting classifier.png b/...oss-validation performance of the scikit-learn gradient boosting classifier.png
diff --git a/...e 12.4 - Hyperparameter impact for the scikit-learn gradient boosting model.png b/...e 12.4 - Hyperparameter impact for the scikit-learn gradient boosting model.png
diff --git a/... of the gradient boosting model hyperparameter settings on test performance.png b/... of the gradient boosting model hyperparameter settings on test performance.png
diff --git a/figures/Chapter_12/Figure 12.6 - Depth-wise vs leaf-wise growth.png b/figures/Chapter_12/Figure 12.6 - Depth-wise vs leaf-wise growth.png
diff --git a/...Predictive performance and runtimes of the various gradient boosting models.png b/...Predictive performance and runtimes of the various gradient boosting models.png
diff --git a/...aily IC for the LightGBM and CatBoost models over three prediction horizons.png b/...aily IC for the LightGBM and CatBoost models over three prediction horizons.png
diff --git a/...nt estimates and their confidence intervals for different forecast horizons.png b/...nt estimates and their confidence intervals for different forecast horizons.png
diff --git a/figures/Chapter_13/Figure 13.1 - Nonlinear dimensionality re duction.png b/figures/Chapter_13/Figure 13.1 - Nonlinear dimensionality re duction.png
diff --git a/...- Return volatility patterns captured by the first two principal components.png b/...- Return volatility patterns captured by the first two principal components.png
diff --git a/figures/Chapter_13/Figure 13.11 - Eigenportfolio weights.png b/figures/Chapter_13/Figure 13.11 - Eigenportfolio weights.png
diff --git a/figures/Chapter_13/Figure 13.12 - Cumulative eigenportfolio returns.png b/figures/Chapter_13/Figure 13.12 - Cumulative eigenportfolio returns.png
diff --git a/figures/Chapter_13/Figure 13.13 - Silhouette plots for three and four clusters.png b/figures/Chapter_13/Figure 13.13 - Silhouette plots for three and four clusters.png
diff --git a/...Dendrograms and cophenetic correlation for different dissimilarity measures.png b/...Dendrograms and cophenetic correlation for different dissimilarity measures.png
diff --git a/...er_13/Figure 13.15 - Comparing the DBSCAN and HDBSCAN clustering algorithms.png b/...er_13/Figure 13.15 - Comparing the DBSCAN and HDBSCAN clustering algorithms.png
diff --git a/figures/Chapter_13/Figure 13.16 - Original and clustered correlation matrix.png b/figures/Chapter_13/Figure 13.16 - Original and clustered correlation matrix.png
diff --git a/...s/Chapter_13/Figure 13.17 - Cumulative returns for the different portfolios.png b/...s/Chapter_13/Figure 13.17 - Cumulative returns for the different portfolios.png
diff --git a/...average distance constant grows exponentially with the number of dimensions.png b/...average distance constant grows exponentially with the number of dimensions.png
diff --git a/..._13/Figure 13.3 - Average distance of 1,000 data points in a unit hypercube.png b/..._13/Figure 13.3 - Average distance of 1,000 data points in a unit hypercube.png
diff --git a/figures/Chapter_13/Figure 13.4 - PCA in 2D from various perspectives.png b/figures/Chapter_13/Figure 13.4 - PCA in 2D from various perspectives.png
diff --git a/...gure 13.5 - Visual representation of dimensionality reduction from 3D to 2D.png b/...gure 13.5 - Visual representation of dimensionality reduction from 3D to 2D.png
diff --git a/figures/Chapter_13/Figure 13.6 - The SVD decomposed.png b/figures/Chapter_13/Figure 13.6 - The SVD decomposed.png
diff --git a/...MAP visualization of Fashion MNIST image data for different hyperparameters.png b/...MAP visualization of Fashion MNIST image data for different hyperparameters.png
diff --git a/...ure 13.8 - (Cumulative) explained return variance by PCA-based risk factors.png b/...ure 13.8 - (Cumulative) explained return variance by PCA-based risk factors.png
diff --git a/...ure 13.9 - Explained variance of the top 10 principal components—100 trials.png b/...ure 13.9 - Explained variance of the top 10 principal components—100 trials.png
diff --git a/figures/Chapter_14/Figure 14.1 - The NLP workflow.png b/figures/Chapter_14/Figure 14.1 - The NLP workflow.png
diff --git a/figures/Chapter_14/Figure 14.2 - spaCy dependency tree.png b/figures/Chapter_14/Figure 14.2 - spaCy dependency tree.png
diff --git a/figures/Chapter_14/Figure 14.3 - Document-term matrix and cosine similarity.png b/figures/Chapter_14/Figure 14.3 - Document-term matrix and cosine similarity.png
diff --git a/...14.4 - The distributions of unique tokens and number of tokens per document.png b/...14.4 - The distributions of unique tokens and number of tokens per document.png
diff --git a/...Chapter_14/Figure 14.5 - Accuracy of custom versus generic sentiment scores.png b/...Chapter_14/Figure 14.5 - Accuracy of custom versus generic sentiment scores.png
diff --git a/figures/Chapter_14/Figure 14.6 - Basic exploratory analysis of Yelp reviews.png b/figures/Chapter_14/Figure 14.6 - Basic exploratory analysis of Yelp reviews.png
diff --git a/... (all models, left) and for logistic regression with varying regularization.png b/... (all models, left) and for logistic regression with varying regularization.png
diff --git a/figures/Chapter_15/Figure 15.1 - LSI and the SVD.png b/figures/Chapter_15/Figure 15.1 - LSI and the SVD.png
diff --git a/figures/Chapter_15/Figure 15.10 - Most common earnings call toke ns.png b/figures/Chapter_15/Figure 15.10 - Most common earnings call toke ns.png
diff --git a/...res/Chapter_15/Figure 15.11 - Most important words for earnings call topics.png b/...res/Chapter_15/Figure 15.11 - Most important words for earnings call topics.png
diff --git a/figures/Chapter_15/Figure 15.12 - pyLDAVis' interactive topic explorer.png b/figures/Chapter_15/Figure 15.12 - pyLDAVis' interactive topic explorer.png
diff --git a/...er_15/Figure 15.13 - Impact of LDA hyperparameter settings on topic quality.png b/...er_15/Figure 15.13 - Impact of LDA hyperparameter settings on topic quality.png
diff --git a/figures/Chapter_15/Figure 15.14 - Corpus statistics for financial news data.png b/figures/Chapter_15/Figure 15.14 - Corpus statistics for financial news data.png
diff --git a/figures/Chapter_15/Figure 15.15 - Top 15 words for financial news topics.png b/figures/Chapter_15/Figure 15.15 - Top 15 words for financial news topics.png
diff --git a/figures/Chapter_15/Figure 15.2 - LSI topic weights for train and test data.png b/figures/Chapter_15/Figure 15.2 - LSI topic weights for train and test data.png
diff --git a/figures/Chapter_15/Figure 15.3 - Top 10 words per LSI topic.png b/figures/Chapter_15/Figure 15.3 - Top 10 words per LSI topic.png
diff --git a/...igure 15.4 - The statistical dependencies modeled by pLSA in plate notation.png b/...igure 15.4 - The statistical dependencies modeled by pLSA in plate notation.png
diff --git a/figures/Chapter_15/Figure 15.5 - pLSA weights by topic for train and test data.png b/figures/Chapter_15/Figure 15.5 - pLSA weights by topic for train and test data.png
diff --git a/figures/Chapter_15/Figure 15.6 - Top words per topic for p LSA.png b/figures/Chapter_15/Figure 15.6 - Top words per topic for p LSA.png
diff --git a/figures/Chapter_15/Figure 15.7 - Three draws from the Dirichlet distribution.png b/figures/Chapter_15/Figure 15.7 - Three draws from the Dirichlet distribution.png
diff --git a/...gure 15.8 - The statistical dependencies of the LDA model in plate notation.png b/...gure 15.8 - The statistical dependencies of the LDA model in plate notation.png
diff --git a/figures/Chapter_15/Figure 15.9 - Topic coherence and test set assignments.png b/figures/Chapter_15/Figure 15.9 - Topic coherence and test set assignments.png
diff --git a/..._16/Figure 16.1 - Continuous-bag-of-words versus skip-gram processing logic.png b/..._16/Figure 16.1 - Continuous-bag-of-words versus skip-gram processing logic.png
diff --git a/figures/Chapter_16/Figure 16.2 - Embedding vector arithmetic.png b/figures/Chapter_16/Figure 16.2 - Embedding vector arithmetic.png
diff --git a/figures/Chapter_16/Figure 16.3 - GloVe accuracy on word2vec analogies.png b/figures/Chapter_16/Figure 16.3 - GloVe accuracy on word2vec analogies.png
diff --git a/...es/Chapter_16/Figure 16.4 - 2D visualization of selected analogy embeddings.png b/...es/Chapter_16/Figure 16.4 - 2D visualization of selected analogy embeddings.png
diff --git a/figures/Chapter_16/Figure 16.5 - 3D embeddings and metadata visualization.png b/figures/Chapter_16/Figure 16.5 - 3D embeddings and metadata visualization.png
diff --git a/...er_16/Figure 16.6 - Analogy accuracy by category and for a specific example.png b/...er_16/Figure 16.6 - Analogy accuracy by category and for a specific example.png
diff --git a/...mber of tokens per review (left) and terms most similar to the token 'good'.png b/...mber of tokens per review (left) and terms most similar to the token 'good'.png
diff --git a/..._16/Figure 16.8 - Confusion matrix and test accuracy for alternative models.png b/..._16/Figure 16.8 - Confusion matrix and test accuracy for alternative models.png
diff --git a/figures/Chapter_17/Figure 17.1 - AI timeline and subfields.png b/figures/Chapter_17/Figure 17.1 - AI timeline and subfields.png
diff --git a/figures/Chapter_17/Figure 17.10 - In- and out-of-sample backtest performance.png b/figures/Chapter_17/Figure 17.10 - In- and out-of-sample backtest performance.png
diff --git a/...s/Chapter_17/Figure 17.2 - A feedforward architecture with one hidden layer.png b/...s/Chapter_17/Figure 17.2 - A feedforward architecture with one hidden layer.png
diff --git a/figures/Chapter_17/Figure 17.3 - Synthetic data for binary classification.png b/figures/Chapter_17/Figure 17.3 - Synthetic data for binary classification.png
diff --git a/figures/Chapter_17/Figure 17.4 - Training loss per iteration.png b/figures/Chapter_17/Figure 17.4 - Training loss per iteration.png
diff --git a/...er_17/Figure 17.5 - V isualizing the function learned by the neural network.png b/...er_17/Figure 17.5 - V isualizing the function learned by the neural network.png
diff --git a/figures/Chapter_17/Figure 17.6 - TensorBoard learning process visualization.png b/figures/Chapter_17/Figure 17.6 - TensorBoard learning process visualization.png
diff --git a/..._17/Figure 17.7 - Information coefficients for various model configurations.png b/..._17/Figure 17.7 - Information coefficients for various model configurations.png
diff --git a/figures/Chapter_17/Figure 17.8 - OLS coefficients and confidence intervals.png b/figures/Chapter_17/Figure 17.8 - OLS coefficients and confidence intervals.png
diff --git a/figures/Chapter_17/Figure 17.9 - Signal quality evaluation.png b/figures/Chapter_17/Figure 17.9 - Signal quality evaluation.png
diff --git a/...apter_18/Figure 18.1 - The result of basic edge filters applied to an image.png b/...apter_18/Figure 18.1 - The result of basic edge filters applied to an image.png
diff --git a/...es/Chapter_18/Figure 18.10 - Ten types of land use contained in the dataset.png b/...es/Chapter_18/Figure 18.10 - Ten types of land use contained in the dataset.png
diff --git a/figures/Chapter_18/Figure 18.11 - Cross-validation performance.png b/figures/Chapter_18/Figure 18.11 - Cross-validation performance.png
diff --git a/figures/Chapter_18/Figure 18.12 - Cropped sample images of the SVHN dataset.png b/figures/Chapter_18/Figure 18.12 - Cropped sample images of the SVHN dataset.png
diff --git a/figures/Chapter_18/Figure 18.13 - Cross-validation performance.png b/figures/Chapter_18/Figure 18.13 - Cross-validation performance.png
diff --git a/...igure 18.14 - Information coefficient with respect to forward return by lag.png b/...igure 18.14 - Information coefficient with respect to forward return by lag.png
diff --git a/...ure 18.15 - (Biased) out-of-sample information coefficients for best epochs.png b/...ure 18.15 - (Biased) out-of-sample information coefficients for best epochs.png
diff --git a/figures/Chapter_18/Figure 18.16 - Technical indicators.png b/figures/Chapter_18/Figure 18.16 - Technical indicators.png
diff --git a/... 18.17 - Mutual information and two-dimensional grid layout for time series.png b/... 18.17 - Mutual information and two-dimensional grid layout for time series.png
diff --git a/figures/Chapter_18/Figure 18.18 - Dendrograms for row and column features.png b/figures/Chapter_18/Figure 18.18 - Dendrograms for row and column features.png
diff --git a/figures/Chapter_18/Figure 18.19 - Alphalens signal quality evaluation.png b/figures/Chapter_18/Figure 18.19 - Alphalens signal quality evaluation.png
diff --git a/...8/Figure 18.2 - Typical operations in a two-dimensional convolutional layer.png b/...8/Figure 18.2 - Typical operations in a two-dimensional convolutional layer.png
diff --git a/figures/Chapter_18/Figure 18.20 - Backtest performance in- and out-of-sample.png b/figures/Chapter_18/Figure 18.20 - Backtest performance in- and out-of-sample.png
diff --git a/figures/Chapter_18/Figure 18.3 - From convolutions to a feature map.png b/figures/Chapter_18/Figure 18.3 - From convolutions to a feature map.png
diff --git a/...hapter_18/Figure 18.4 - Predictive performance and computational complexity.png b/...hapter_18/Figure 18.4 - Predictive performance and computational complexity.png
diff --git a/figures/Chapter_18/Figure 18.5 - MNIST sample images.png b/figures/Chapter_18/Figure 18.5 - MNIST sample images.png
diff --git a/figures/Chapter_18/Figure 18.6 - Original and augmented samples.png b/figures/Chapter_18/Figure 18.6 - Original and augmented samples.png
diff --git a/...apter_18/Figure 18.7 - Validation performance and test accuracy on CIFAR-10.png b/...apter_18/Figure 18.7 - Validation performance and test accuracy on CIFAR-10.png
diff --git a/figures/Chapter_18/Figure 18.8 - The VGG16 architecture.png b/figures/Chapter_18/Figure 18.8 - The VGG16 architecture.png
diff --git a/.../Figure 18.9 - Cross-validation performance accuracy and cross-entropy loss.png b/.../Figure 18.9 - Cross-validation performance accuracy and cross-entropy loss.png
diff --git a/figures/Chapter_19/Figure 19.1 - Various types of sequence-to-sequence models.png b/figures/Chapter_19/Figure 19.1 - Various types of sequence-to-sequence models.png
diff --git a/..._19/Figure 19.10 - Stacked LSTM classification—cross-validation performance.png b/..._19/Figure 19.10 - Stacked LSTM classification—cross-validation performance.png
diff --git a/...Chapter_19/Figure 19.11 - Stacked LSTM regression—out-of-sample performance.png b/...Chapter_19/Figure 19.11 - Stacked LSTM regression—out-of-sample performance.png
diff --git a/figures/Chapter_19/Figure 19.12 - Original and transformed time series.png b/figures/Chapter_19/Figure 19.12 - Original and transformed time series.png
diff --git a/....13 - Cross-validation and test results for RNNs with multiple macro series.png b/....13 - Cross-validation and test results for RNNs with multiple macro series.png
diff --git a/...ure 19.14 - Cross-validation for RNN using IMDB data with custom embeddings.png b/...ure 19.14 - Cross-validation for RNN using IMDB data with custom embeddings.png
diff --git a/....15 - Cross-validation and test results for RNNs with multiple macro series.png b/....15 - Cross-validation and test results for RNNs with multiple macro series.png
diff --git a/....16 - Cross-validation and test results for RNNs with multiple macro series.png b/....16 - Cross-validation and test results for RNNs with multiple macro series.png
diff --git a/...alidation test results for RNNs using SEC filings to predict weekly returns.png b/...alidation test results for RNNs using SEC filings to predict weekly returns.png
diff --git a/...nrolled view of the computational graph of an RNN with a single hidden unit.png b/...nrolled view of the computational graph of an RNN with a single hidden unit.png
diff --git a/...res/Chapter_19/Figure 19.3 - Information flow through an unrolled LSTM cell.png b/...res/Chapter_19/Figure 19.3 - Information flow through an unrolled LSTM cell.png
diff --git a/figures/Chapter_19/Figure 19.4 - The logic of, and math behind, an LSTM cell.png b/figures/Chapter_19/Figure 19.4 - The logic of, and math behind, an LSTM cell.png
diff --git a/figures/Chapter_19/Figure 19.5 - The three dimensions of an RNN input tensor.png b/figures/Chapter_19/Figure 19.5 - The three dimensions of an RNN input tensor.png
diff --git a/figures/Chapter_19/Figure 19.6 - Input-output pairs with a T=5 size window.png b/figures/Chapter_19/Figure 19.6 - Input-output pairs with a T=5 size window.png
diff --git a/figures/Chapter_19/Figure 19.7 - Cross-validation performance.png b/figures/Chapter_19/Figure 19.7 - Cross-validation performance.png
diff --git a/figures/Chapter_19/Figure 19.8 - RNN performance on S&P 500 predictions.png b/figures/Chapter_19/Figure 19.8 - RNN performance on S&P 500 predictions.png
diff --git a/...Chapter_19/Figure 19.9 - Stacked LSTM architecture with additional features.png b/...Chapter_19/Figure 19.9 - Stacked LSTM architecture with additional features.png
diff --git a/...r_2/Figure 2.1 - The share of traded value of the 50 most traded securities.png b/...r_2/Figure 2.1 - The share of traded value of the 50 most traded securities.png
diff --git a/figures/Chapter_2/Figure 2.10 - Quandl oil price example.png b/figures/Chapter_2/Figure 2.10 - Quandl oil price example.png
diff --git a/figures/Chapter_2/Figure 2.11 - Trailing PE ratio from EDGAR filings.png b/figures/Chapter_2/Figure 2.11 - Trailing PE ratio from EDGAR filings.png
diff --git a/figures/Chapter_2/Figure 2.12 - Storage benchmarks.png b/figures/Chapter_2/Figure 2.12 - Storage benchmarks.png
diff --git a/...es/Chapter_2/Figure 2.2 - AAPL market liquidity according to the order book.png b/...es/Chapter_2/Figure 2.2 - AAPL market liquidity according to the order book.png
diff --git a/figures/Chapter_2/Figure 2.3 - Tick bars.png b/figures/Chapter_2/Figure 2.3 - Tick bars.png
diff --git a/figures/Chapter_2/Figure 2.4 - Time bars.png b/figures/Chapter_2/Figure 2.4 - Time bars.png
diff --git a/figures/Chapter_2/Figure 2.5 - Bokeh candlestick plot.png b/figures/Chapter_2/Figure 2.5 - Bokeh candlestick plot.png
diff --git a/figures/Chapter_2/Figure 2.6 - Volume bars.png b/figures/Chapter_2/Figure 2.6 - Volume bars.png
diff --git a/figures/Chapter_2/Figure 2.7 - Dollar bars.png b/figures/Chapter_2/Figure 2.7 - Dollar bars.png
diff --git a/figures/Chapter_2/Figure 2.8 - Plotly candlestick plot.png b/figures/Chapter_2/Figure 2.8 - Plotly candlestick plot.png
diff --git a/figures/Chapter_2/Figure 2.9 - Zipline data access.png b/figures/Chapter_2/Figure 2.9 - Zipline data access.png
diff --git a/figures/Chapter_20/Figure 20.1 - Undercomplete encoder-decoder architecture.png b/figures/Chapter_20/Figure 20.1 - Undercomplete encoder-decoder architecture.png
diff --git a/...gure 20.10 - Cumulative returns of quintile-based and long-short portfolios.png b/...gure 20.10 - Cumulative returns of quintile-based and long-short portfolios.png
diff --git a/figures/Chapter_20/Figure 20.2 - Fashion MNIST sample images.png b/figures/Chapter_20/Figure 20.2 - Fashion MNIST sample images.png
diff --git a/...er_20/Figure 20.3 - Sample Fashion MNIST images, original and reconstructed.png b/...er_20/Figure 20.3 - Sample Fashion MNIST images, original and reconstructed.png
diff --git a/...igure 20.4 - t-SNE visualization of the Fashion MNIST autoencoder embedding.png b/...igure 20.4 - t-SNE visualization of the Fashion MNIST autoencoder embedding.png
diff --git a/figures/Chapter_20/Figure 20.5 - Denoising input and output examples.png b/figures/Chapter_20/Figure 20.5 - Denoising input and output examples.png
diff --git a/...20/Figure 20.6 - Number of tickers over time and per - stock characteristic.png b/...20/Figure 20.6 - Number of tickers over time and per - stock characteristic.png
diff --git a/...apter_20/Figure 20.7 - Conditional autoencoder architecture designed by GKX.png b/...apter_20/Figure 20.7 - Conditional autoencoder architecture designed by GKX.png
diff --git a/...ss-validation performance for all factor and hidden-layer size combinations.png b/...ss-validation performance for all factor and hidden-layer size combinations.png
diff --git a/...res/Chapter_20/Figure 20.9 - Mean period-wise spread by prediction quintile.png b/...res/Chapter_20/Figure 20.9 - Mean period-wise spread by prediction quintile.png
diff --git a/figures/Chapter_21/Figure 21.1 - GAN architecture.png b/figures/Chapter_21/Figure 21.1 - GAN architecture.png
diff --git a/figures/Chapter_21/Figure 21.2 - DCGAN TensorFlow 2 model architecture.png b/figures/Chapter_21/Figure 21.2 - DCGAN TensorFlow 2 model architecture.png
diff --git a/figures/Chapter_21/Figure 21.3 - A sample of synthetic Fashion-MNIST images.png b/figures/Chapter_21/Figure 21.3 - A sample of synthetic Fashion-MNIST images.png
diff --git a/figures/Chapter_21/Figure 21.4 - The components of the TimeGAN architecture.png b/figures/Chapter_21/Figure 21.4 - The components of the TimeGAN architecture.png
diff --git a/...res/Chapter_21/Figure 21.5 - The TimeGAN input—six real stock prices series.png b/...res/Chapter_21/Figure 21.5 - The TimeGAN input—six real stock prices series.png
diff --git a/....6 - TimeGAN output—six synthetic prices series and their real counterparts.png b/....6 - TimeGAN output—six synthetic prices series and their real counterparts.png
diff --git a/...r_21/Figure 21.7 - 250 samples of real and synthetic data in two dimensions.png b/...r_21/Figure 21.7 - 250 samples of real and synthetic data in two dimensions.png
diff --git a/... - Train and test performance of the time-series classifier over 250 epochs.png b/... - Train and test performance of the time-series classifier over 250 epochs.png
diff --git a/...in and test performance of the time-series prediction model over 100 epochs.png b/...in and test performance of the time-series prediction model over 100 epochs.png
diff --git a/figures/Chapter_22/Figure 22.1 - Components of an RL system.png b/figures/Chapter_22/Figure 22.1 - Components of an RL system.png
diff --git a/.../Figure 22.2 - The recursive relationship expressed by the Bellman equation.png b/.../Figure 22.2 - The recursive relationship expressed by the Bellman equation.png
diff --git a/...s/Chapter_22/Figure 22.3 - Convergence of policy evaluation and improvement.png b/...s/Chapter_22/Figure 22.3 - Convergence of policy evaluation and improvement.png
diff --git a/..._22/Figure 22.4 - 3×4 gridworld rewards, value function, and optimal policy.png b/..._22/Figure 22.4 - 3×4 gridworld rewards, value function, and optimal policy.png
diff --git a/...hapter_22/Figure 22.5 - RL agent's behavior during the Lunar Lander episode.png b/...hapter_22/Figure 22.5 - RL agent's behavior during the Lunar Lander episode.png
diff --git a/.../Figure 22.6 - The DDQN agent's performance in the Lunar Lander environment.png b/.../Figure 22.6 - The DDQN agent's performance in the Lunar Lander environment.png
diff --git a/...s/Chapter_22/Figure 22.7 - Trading agent performance relative to the market.png b/...s/Chapter_22/Figure 22.7 - Trading agent performance relative to the market.png
diff --git a/figures/Chapter_23/Figure 23.1 - Key elements of using ML for trading.png b/figures/Chapter_23/Figure 23.1 - Key elements of using ML for trading.png
diff --git a/figures/Chapter_23/Figure 23.2 - The ML workflow.png b/figures/Chapter_23/Figure 23.2 - The ML workflow.png
diff --git a/...3.1 - Usefulness and usage of alternative data (Source Ernst & Young, 2017).png b/...3.1 - Usefulness and usage of alternative data (Source Ernst & Young, 2017).png
diff --git a/figures/Chapter_3/Figure 3.2 - OpenTable data summary.png b/figures/Chapter_3/Figure 3.2 - OpenTable data summary.png
diff --git a/figures/Chapter_4/Figure 4.1 - Alpha factor research and execution workflow.png b/figures/Chapter_4/Figure 4.1 - Alpha factor research and execution workflow.png
diff --git a/...er_4/Figure 4.10 - Cumulative return by quantile for a 5-day holding period.png b/...er_4/Figure 4.10 - Cumulative return by quantile for a 5-day holding period.png
diff --git a/...r_4/Figure 4.11 - Distribution of the period-wise return by factor quintile.png b/...r_4/Figure 4.11 - Distribution of the period-wise return by factor quintile.png
diff --git a/figures/Chapter_4/Figure 4.12 - Moving average of the IC for 5-day horizon.png b/figures/Chapter_4/Figure 4.12 - Moving average of the IC for 5-day horizon.png
diff --git a/figures/Chapter_4/Figure 4.13 - IC by year.png b/figures/Chapter_4/Figure 4.13 - IC by year.png
diff --git a/figures/Chapter_4/Figure 4.2 - Returns on various risk factors.png b/figures/Chapter_4/Figure 4.2 - Returns on various risk factors.png
diff --git a/figures/Chapter_4/Figure 4.3 - Correlation between the VIX and the S&P 500.png b/figures/Chapter_4/Figure 4.3 - Correlation between the VIX and the S&P 500.png
diff --git a/figures/Chapter_4/Figure 4.4 - Bollinger Bands and relative strength index.png b/figures/Chapter_4/Figure 4.4 - Bollinger Bands and relative strength index.png
diff --git a/figures/Chapter_4/Figure 4.5 - Kalman filter as a graphical model.png b/figures/Chapter_4/Figure 4.5 - Kalman filter as a graphical model.png
diff --git a/figures/Chapter_4/Figure 4.6 - Kalman filter versus moving average.png b/figures/Chapter_4/Figure 4.6 - Kalman filter versus moving average.png
diff --git a/figures/Chapter_4/Figure 4.7 - Daubechies wavelets.png b/figures/Chapter_4/Figure 4.7 - Daubechies wavelets.png
diff --git a/figures/Chapter_4/Figure 4.8 - Wavelet denoising with different thresholds.png b/figures/Chapter_4/Figure 4.8 - Wavelet denoising with different thresholds.png
diff --git a/figures/Chapter_4/Figure 4.9 - Mean period return by factor quantile.png b/figures/Chapter_4/Figure 4.9 - Mean period return by factor quantile.png
diff --git a/...ormation ratios for different values of breadth and information coefficient.png b/...ormation ratios for different values of breadth and information coefficient.png
diff --git a/...er_5/Figure 5.2 - The efficient frontier and different optimized portfolios.png b/...er_5/Figure 5.2 - The efficient frontier and different optimized portfolios.png
diff --git a/...hapter_5/Figure 5.3 - Mean-variance vs equal-weighted portfolio performance.png b/...hapter_5/Figure 5.3 - Mean-variance vs equal-weighted portfolio performance.png
diff --git a/figures/Chapter_5/Figure 5.4 - Pyfolio cumulative performance plot.png b/figures/Chapter_5/Figure 5.4 - Pyfolio cumulative performance plot.png
diff --git a/figures/Chapter_5/Figure 5.5 - Pyfolio performance statistic plot.png b/figures/Chapter_5/Figure 5.5 - Pyfolio performance statistic plot.png
diff --git a/figures/Chapter_5/Figure 5.6 - Various pyfolio plots of performance over time.png b/figures/Chapter_5/Figure 5.6 - Various pyfolio plots of performance over time.png
diff --git a/figures/Chapter_5/Figure 5.7 - Pyfolio event risk analysis.png b/figures/Chapter_5/Figure 5.7 - Pyfolio event risk analysis.png
diff --git a/figures/Chapter_6/Figure 6.1 - Key steps of the machine learning workflow.png b/figures/Chapter_6/Figure 6.1 - Key steps of the machine learning workflow.png
diff --git a/figures/Chapter_6/Figure 6.10 - Train, validation, and hold-out test set.png b/figures/Chapter_6/Figure 6.10 - Train, validation, and hold-out test set.png
diff --git a/figures/Chapter_6/Figure 6.11 - Validation and learning curves.png b/figures/Chapter_6/Figure 6.11 - Validation and learning curves.png
diff --git a/figures/Chapter_6/Figure 6.2 - In-sample regression errors.png b/figures/Chapter_6/Figure 6.2 - In-sample regression errors.png
diff --git a/figures/Chapter_6/Figure 6.3 - Confusion matrix and related error metrics.png b/figures/Chapter_6/Figure 6.3 - Confusion matrix and related error metrics.png
diff --git a/...ver-Operating Characteristics, Precision-Recall Curve, and F1 Scores charts.png b/...ver-Operating Characteristics, Precision-Recall Curve, and F1 Scores charts.png
diff --git a/figures/Chapter_6/Figure 6.5 - Pairwise scatter plots of outcome and features.png b/figures/Chapter_6/Figure 6.5 - Pairwise scatter plots of outcome and features.png
diff --git a/...res/Chapter_6/Figure 6.6 - A visual example of overfitting with polynomials.png b/...res/Chapter_6/Figure 6.6 - A visual example of overfitting with polynomials.png
diff --git a/...- Errors and out-of-sample predictions for polynomials of different degrees.png b/...- Errors and out-of-sample predictions for polynomials of different degrees.png
diff --git a/figures/Chapter_6/Figure 6.8 - Learning curves and bias-variance tradeoff.png b/figures/Chapter_6/Figure 6.8 - Learning curves and bias-variance tradeoff.png
diff --git a/figures/Chapter_6/Figure 6.9 - Training and test set.png b/figures/Chapter_6/Figure 6.9 - Training and test set.png
diff --git a/figures/Chapter_7/Figure 7.1 - Gradient descent.png b/figures/Chapter_7/Figure 7.1 - Gradient descent.png
diff --git a/figures/Chapter_7/Figure 7.10 - Lasso cross-validation results.png b/figures/Chapter_7/Figure 7.10 - Lasso cross-validation results.png
diff --git a/...Chapter_7/Figure 7.11 - Lasso daily IC distribution and top 10 coefficients.png b/...Chapter_7/Figure 7.11 - Lasso daily IC distribution and top 10 coefficients.png
diff --git a/figures/Chapter_7/Figure 7.12 - Mean and median daily IC for the three models.png b/figures/Chapter_7/Figure 7.12 - Mean and median daily IC for the three models.png
diff --git a/figures/Chapter_7/Figure 7.13 - Logit regression results.png b/figures/Chapter_7/Figure 7.13 - Logit regression results.png
diff --git a/figures/Chapter_7/Figure 7.14 -Logistic regression.png b/figures/Chapter_7/Figure 7.14 -Logistic regression.png
diff --git a/figures/Chapter_7/Figure 7.2 - OLS Regression Results summary.png b/figures/Chapter_7/Figure 7.2 - OLS Regression Results summary.png
diff --git a/figures/Chapter_7/Figure 7.3 - Regression hyperplane.png b/figures/Chapter_7/Figure 7.3 - Regression hyperplane.png
diff --git a/figures/Chapter_7/Figure 7.4 - LinearFactorModel estimation summary.png b/figures/Chapter_7/Figure 7.4 - LinearFactorModel estimation summary.png
diff --git a/figures/Chapter_7/Figure 7.5 - Residual distribution and autocorrelation plots.png b/figures/Chapter_7/Figure 7.5 - Residual distribution and autocorrelation plots.png
diff --git a/figures/Chapter_7/Figure 7.6 - Daily and pooled IC for linear regression.png b/figures/Chapter_7/Figure 7.6 - Daily and pooled IC for linear regression.png
diff --git a/...y rolling average for the daily IC and RMSE for the linear regression model.png b/...y rolling average for the daily IC and RMSE for the linear regression model.png
diff --git a/figures/Chapter_7/Figure 7.8 - Ridge regression cross-validation results.png b/figures/Chapter_7/Figure 7.8 - Ridge regression cross-validation results.png
diff --git a/...hapter_7/Figure 7.9 - Daily IC distribution and most important coefficients.png b/...hapter_7/Figure 7.9 - Daily IC distribution and most important coefficients.png
diff --git a/figures/Chapter_8/Figure 8.1 - The ML4T workflow.png b/figures/Chapter_8/Figure 8.1 - The ML4T workflow.png
diff --git a/...es/Chapter_8/Figure 8.10 - Zipline backtest performance with model training.png b/...es/Chapter_8/Figure 8.10 - Zipline backtest performance with model training.png
diff --git a/figures/Chapter_8/Figure 8.2 - Vectorized backtest results.png b/figures/Chapter_8/Figure 8.2 - Vectorized backtest results.png
diff --git a/figures/Chapter_8/Figure 8.3 - The backtrader Cerebro architecture.png b/figures/Chapter_8/Figure 8.3 - The backtrader Cerebro architecture.png
diff --git a/figures/Chapter_8/Figure 8.4 - backtrader results.png b/figures/Chapter_8/Figure 8.4 - backtrader results.png
diff --git a/figures/Chapter_8/Figure 8.5 - The Zipline architecture.png b/figures/Chapter_8/Figure 8.5 - The Zipline architecture.png
diff --git a/figures/Chapter_8/Figure 8.6 - ML signal backtest using Zipline's Pipeline API.png b/figures/Chapter_8/Figure 8.6 - ML signal backtest using Zipline's Pipeline API.png
diff --git a/figures/Chapter_8/Figure 8.7 - Zipline backtest results.png b/figures/Chapter_8/Figure 8.7 - Zipline backtest results.png
diff --git a/...es/Chapter_8/Figure 8.8 - Flowchart of Zipline backtest with model training.png b/...es/Chapter_8/Figure 8.8 - Flowchart of Zipline backtest with model training.png
diff --git a/figures/Chapter_8/Figure 8.9 - Model out-of-sample performance.png b/figures/Chapter_8/Figure 8.9 - Model out-of-sample performance.png
diff --git a/...gure 9.1 - Time-series decomposition into trend, seasonality, and residuals.png b/...gure 9.1 - Time-series decomposition into trend, seasonality, and residuals.png
diff --git a/...9.10 - Transformed time series industrial production and consumer sentiment.png b/...9.10 - Transformed time series industrial production and consumer sentiment.png
diff --git a/figures/Chapter_9/Figure 9.11 - VAR(1) model results.png b/figures/Chapter_9/Figure 9.11 - VAR(1) model results.png
diff --git a/figures/Chapter_9/Figure 9.12 - statsmodels VAR model diagnostic plot.png b/figures/Chapter_9/Figure 9.12 - statsmodels VAR model diagnostic plot.png
diff --git a/figures/Chapter_9/Figure 9.13 - VAR model predictions versus actuals.png b/figures/Chapter_9/Figure 9.13 - VAR model predictions versus actuals.png
diff --git a/... of heuristics, broken down by the significance of both cointegration tests.png b/... of heuristics, broken down by the significance of both cointegration tests.png
diff --git a/..._9/Figure 9.15 - Price series for two selected pairs over the sample period.png b/..._9/Figure 9.15 - Price series for two selected pairs over the sample period.png
diff --git a/figures/Chapter_9/Figure 9.16 - Strategy performance metrics.png b/figures/Chapter_9/Figure 9.16 - Strategy performance metrics.png
diff --git a/...apter_9/Figure 9.2 - Time-series transformations and unit-root test results.png b/...apter_9/Figure 9.2 - Time-series transformations and unit-root test results.png
diff --git a/.../Figure 9.3 - Descriptive statistics for transformed NASDAQ Composite index.png b/.../Figure 9.3 - Descriptive statistics for transformed NASDAQ Composite index.png
diff --git a/...re 9.4 - Descri ptive statistics for transformed industrial production data.png b/...re 9.4 - Descri ptive statistics for transformed industrial production data.png
diff --git a/figures/Chapter_9/Figure 9.5 - SARMAX model results.png b/figures/Chapter_9/Figure 9.5 - SARMAX model results.png
diff --git a/figures/Chapter_9/Figure 9.6 - SARIMAX model diagnostics.png b/figures/Chapter_9/Figure 9.6 - SARIMAX model diagnostics.png
diff --git a/figures/Chapter_9/Figure 9.7 - Daily NASDAQ composite volatility.png b/figures/Chapter_9/Figure 9.7 - Daily NASDAQ composite volatility.png
diff --git a/figures/Chapter_9/Figure 9.8 - GARCH Model results.png b/figures/Chapter_9/Figure 9.8 - GARCH Model results.png
diff --git a/...Figure 9.9 - Interactions in univariate and multivariate time-series models.png b/...Figure 9.9 - Interactions in univariate and multivariate time-series models.png