A number of powerful and popular libraries, such as scikit-learn, PyTorch and PyMC, are available in Python to make statistical modelling efforts easy and accessible. The following notebooks describe techniques to create machine learning solutions with macro quantamental indicators, using these libraries together with the macrosynergy package.
Point-in-time indicators of macroeconomic trends and states are strong candidates for trading signals. Conceptual risk parity, i.e. an equally weighted average of normalized scores, is a simple and often successful approach to condense these into a single signal, but there is scope for optimization. Statistical learning offers methods for sequential model selection, as well as associated hyperparameters, for signal generation, thus supporting realistic backtests and automated operation of strategies.
This post and its associated Jupyter Notebook demonstrate sequential signal optimization with the scikit-learn package and some specialized extensions. In particular, the post applies statistical learning to sequential optimization of three important tasks: feature selection, return prediction, and market regime classification.
Regression is one method for combining macro indicators into a single trading signal. Specifically, statistical learning based on regression can optimize both model parameters and hyperparameters sequentially and produce signals based on whichever model has predicted returns best up to a point in time. This method learns from growing datasets and produces valid point-in-time signals for backtesting. However, whether regression delivers good signals depends on managing the bias-variance trade-off.
This post and its associated Jupyter Notebook provides guidance on pre-selecting the right regression models and hyperparameter grids based on theory and empirical evidence. It considers the advantages and disadvantages of various regression methods, including non-negative least squares, elastic net, weighted least squares, least absolute deviations, and nearest neighbors.
Regression-based statistical learning helps build trading signals from multiple candidate constituents. The method optimizes models and hyperparameters sequentially and produces point-in-time signals for backtesting and live trading.
This post applies regression-based learning to macro trading factors for developed market FX trading, using an improved cross-validation method for expanding panel data. Sequentially optimized models consider nine theoretically valid macro trend indicators to predict FX forward returns. The learning process has delivered significant predictors of returns and consistent positive PnL generation for over 20 years. The most important macro-FX signals, in the long run, have been relative labor market trends, manufacturing business sentiment changes, relative inflation expectations, and terms of trade dynamics.
Principal Components Analysis (PCA) is a dimensionality reduction technique that condenses the key information from a large dataset into a smaller set of uncorrelated variables called “principal components.” This smaller set often functions better as features for predictive regressions, stabilizing coefficient estimates and reducing the influence of noise. In this way, principal components can improve statistical learning methods that optimize trading signals.
This post shows how principal components can serve as building blocks of trading signals for developed market interest rate swap positions, condensing the information of macro-quantamental indicators on inflation pressure, activity growth, and credit and money expansion. Compared to a simple combination of these categories, PCA-based statistical learning methods have produced materially higher predictive accuracy and backtested trading profits. PCA methods have also outperformed non-PCA-based regression learning. PCA-based statistical learning in backtesting leaves little scope for data mining or hindsight, and the discovery of trading value has high credibility.
Macro beta is the sensitivity of a financial contract’s return to a broad economic or market factor. Macro betas broaden the traditional concept of equity market betas and can often be estimated using financial contract baskets. Macro sensitivities are endemic in trading strategies, diluting alpha, undermining portfolio diversification, and distorting backtests.
However, it is possible to immunize strategies through “beta learning,” a statistical learning method that supports identifying appropriate models and hyperparameters and allows backtesting of hedged strategies without look-ahead bias. The process can be easily implemented with existing Python classes and methods. This post illustrates the powerful beneficial impact of macro beta estimation and its application on an emerging market FX carry strategy.
Regression-based statistical learning is convenient for combining candidate trading factors into single signals (view post here). Models and signals are updated sequentially using expanding time windows of empirical evidence and offering a realistic basis for backtesting.
However, simple regression-based predictions disregard statistical reliability, which tends to increase as time passes or decrease after structural breaks. This short methodological post proposes signals based on regression coefficients adjusted for statistical precision. The adjustment correctly aligns intertemporal risk-taking with the predictive power of signals. PnLs become less seasonal and outperform as sample size and statistical quality grow.
There is sound reason and evidence for the predictive power of macro indicators for relative sectoral equity returns. However, the relations between economic information and equity sector performance can be complex. Considering the broad range of available point-in-time macro-categories that are now available, statistical learning has become a compelling method for discovering macro predictors and supporting prudent and realistic backtests of related strategies.
This post shows a simple five-step method to use statistical learning to select and combine macro predictors from a broad set of categories for the 11 major equity sectors in 12 developed countries. The learning process produces signals based on changing models and factors per the statistical evidence. These signals have been positive predictors for relative returns of all sectors versus a broad basket. Combined into a single strategy, these signals create material and uncorrelated investor value through sectoral allocation alone.
Random forest regression combines the discovery of complex predictive relations with efficient management of the “bias-variance trade-off” of machine learning. The method is suitable for constructing macro trading signals with statistical learning, particularly when relations between macro factors and market returns are multi-faceted or non-monotonic and do not have clear theoretical priors to go on.
This post shows how random forest regression can be used in a statistical learning pipeline for macro trading signals that chooses and optimizes models sequentially over time. For cross-sector equity allocation using a set of over 50 conceptual macro factors, regression trees have delivered signals with significant predictive power and economic value. Value generation has been higher and less seasonal than for statistical learning with linear regression models.
Data analysis with macro quantamental indicators can be performed in both Python and R using standard data science libraries. The following notebooks contain entry-level analysis examples focusing on standard time series and panel analysis.
The notebook illustrates how to use the popular data visualization library Seaborn with quantamental data. In particular, it shows how to use the package to display historical distributions, panels of timelines, bivariate relations, and various types of heatmaps.
The notebook illustrates various types of quantamental panel analysis in Python. In particular, it shows the application of pooled regression, fixed-effects regression, random-effects regression, linear mixed-effects models, and seemingly unrelated regressions.
The notebook illustrates various types of quantamental panel analysis in R. In particular, it shows the application of pooled models, fixed effects models, and linear mixed-effects models.
This notebook gives a step-by-step strategy research example using quantamental data and the Macrosynergy package. It shows how to check data, how to construct panels with plausible trading factors, and how to value the predictive power and economic value of such factors.
Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.
