Terms of trade as trading signals

October 11, 2025

Jupyter Notebook

Terms of trade measure an economy’s ratio of export prices to import prices. Changes in these ratios drive divergences in economic performance across currency areas and influence foreign exchange and other asset market returns. Consequently, timely estimates of terms-of-trade dynamics, often derived from commodity prices, have long been valued as leading indicators by investors. Today, point-in-time versions of these measures provide a robust foundation for statistical analysis and backtesting of systematic strategies.
This post illustrates the trading value of real-time terms-of-trade changes through five simple strategies. Individually, such single-signal strategies yield only modest standalone performance ratios. However, their profit-and-loss streams are largely uncorrelated with major market benchmarks and only moderately correlated with each other. This suggests that, when applied systematically and across a broad set of markets, terms-of-trade dynamics can serve as a significant and independent source of true alpha in systematic macro trading.

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The (hidden) trading value of central bank liquidity information

August 30, 2025

Jupyter Notebook

Central banks regularly adjust the economy’s monetary base through foreign exchange interventions and open market operations. Point-in-time information on such intervention-based liquidity expansion has predictive power for asset returns. That is because such operations often come in longer-term trends, and there are lagged effects, for example, through private sector portfolio rebalancing. Alas, the discovery of the economic value of intervention-liquidity signals is often obscured by “ugly backtests”: as a single type of information applied to a single asset type, simulations typically show patchy relevance and uneven value profiles. However, across strategy types and asset classes, intervention-driven liquidity growth has consistently contributed value. A simulation combining two directional and two relative-value strategies demonstrates steady and meaningful PnL generation with low correlation to major market benchmarks.
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Boosting macro trading signals

April 26, 2025

Jupyter Notebook

Boosting is a machine learning ensemble method that combines the predictions of a chain of basic models, whereby each model seeks to address the shortcomings of the previous one. This post applies adaptive boosting (Adaboost) to trading signal optimisation. Signals are constructed with macro factors to guide positioning in a broad range of global FX forwards.
Boosting is beneficial for learning from a wide and heterogeneous set of markets over time, because it is well-suited for exploiting the diversity of experiences across countries and global economic states. Empirically, we generate machine learning-based signals that use regularized regression and random forest regression, and compare processes with and without adaptive boosting methods. For both regression types, machine learning prefers boosting as datasets get larger and, by doing so, creates more profitable signals.

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FX trading signals: Common sense and machine learning

December 21, 2024

Jupyter Notebook

Two valid methods to combine macro trading factors into a single signal are “conceptual parity” and machine learning. Conceptual parity takes a set of conceptually separate normalized factors and gives them equal weights. Machine learning optimizes models and derives weights sequentially, potentially with theoretical restrictions. Both methods support realistic backtests. Conceptual parity works best in the presence of strong theoretical priors. Machine learning works best with large homogenous data sets.
We apply conceptual parity, and two machine learning methods to combine 11 macro-quantamental trading factors for developed and emerging market FX forwards in 16 currencies since 2000. The signals derived by all methods have been highly significant predictors and produced material and uncorrelated risk-adjusted trading returns. Machine learning methods have failed to outperform conceptual parity, probably reflecting that theoretical priors in the FX space are abundant while data are limited and heterogeneous.

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How “beta learning” improves macro trading strategies

June 1, 2024

Jupyter Notebook

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.

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Generic derivative returns and carry (for strategy testing)

February 10, 2024

Backtesting of macro trading strategies requires good approximate profit-and-loss data for standard derivatives positions, particularly in equity, foreign exchange, and rates markets. Practical calculation methods of generic proxy returns not only deliver valid strategy targets but are also the basis of volatility adjustments of trading factors and for calculating nominal and real “carry” of macro derivatives. A methodological summary for equity index futures, FX forwards, and interest rate swaps shows that generic return and carry formulas need not be complicated. However, decisions on how to simplify and set conventions require good judgment and adjustment to institutional needs.

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FX trend following and macro headwinds

May 20, 2023

Jupyter Notebook

Trend following can benefit from consideration of macro trends. One reason is that macroeconomic data indicate headwinds (or tailwinds) for the continuation of market price trends. This is particularly obvious in the foreign-exchange space. For example, the positive return trend of a currency is less likely to be sustained if concurrent economic data signal a deterioration in the competitiveness of the local economy. Macro indicators of such setback risk can slip through the net of statistical detection of return predictors because their effects compete with dominant trends and are often non-linear and concentrated. As a simple example, empirical evidence shows that standard global FX trend following would have benefited significantly merely from adjusting for changes in external balances.

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Macroeconomic cycles and asset class returns

May 6, 2023

Jupyter Notebook

Indicators of growth and inflation cycles are plausible and successful predictors of asset class returns. For proof of concept, we propose a single balanced “cyclical strength score” based on point-in-time quantamental indicators of excess GDP growth, labor market tightening, and excess inflation. It has clear theoretical implications for all major asset markets, as rising operating rates and consumer price pressure raise real discount factors. Empirically, the cyclical strength score has displayed significant predictive power for equity, FX, and fixed income returns, as well as relative asset class positions. The direction of relationships has been in accordance with standard economic theory. Predictive power can be explained by rational inattention. Naïve PnLs based on cyclical strength scores have each produced long-term Sharpe ratios between 0.4 and 1 with little correlation with risk benchmarks. This suggests that a single indicator of cyclical economic strength can be the basis of a diversified portfolio.

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Excess inflation and asset class returns

March 25, 2023

Jupyter Notebook

Excess inflation means consumer price trends over and above the inflation target. In a credible inflation targeting regime, positive excess inflation skews the balance of risks of monetary policy towards tightening. An inflation shortfall tips the risk balance towards easing. Assuming that these shifting balances are not always fully priced by the market, excess inflation in a local currency area should negatively predict local rates market and equity market returns, and positively local-currency FX returns. Indeed, these hypotheses are strongly supported by empirical evidence for 10 developed markets since 2000. For fixed income and FX excess inflation has not just been a directional but also a relative cross-country trading signal. The deployment of excess inflation as a trading signal across asset classes has added notable economic value.

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FX trades after volatility shocks

December 10, 2022

Currency areas with negative external balances are – all other things equal – more vulnerable to financing shocks. Jumps in market price volatility often indicate such shocks. Realistically it takes a few days for the market to fully price the consequences of shocks consistently across currencies. Hence, the products of external balances-based “resilience scores” and volatility shocks are plausible indicators of “post-shock currency hazards”. This means that they should serve as signals for differences in currency returns after market volatility has surged or dropped. An empirical analysis based on 28 currencies since 2000 shows that a most simple “post-shock currency hazard” measure has significantly helped predict subsequent short-term returns and would have added positive PnL to FX trading strategies, particularly in times of turbulence.

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