Quantitative Macro Research: Building an Edge Without Future Data

A quantitative research pipeline converts observations into features, model outputs and decisions. Each conversion can introduce an error that looks like predictive skill: a future return placed in a feature column, a full-sample normalisation applied to the past, or a synthetic exercise series mistaken for market evidence. The pipeline should therefore preserve not just the values, but when they were available and what kind of evidence they represent.

Supporting datasets

• multi-asset-teaching-panel.json
• sample-option-chain.json
• synthetic-implied-volatility-history.json
• synthetic-ohlc-series.json

This reference report combines the introductory systematic-trading material, the time-series teaching deck and the accompanying sample datasets. It focuses on data and model interfaces rather than duplicating a trading-strategy backtest. The original numeric rows are retained in anonymised companion CSVs, with their evidential status kept explicit.

Begin with an inventory of the observations

The source files contain four distinct numeric datasets:

Retained datasets and their evidential status — Dataset: Multi-asset panel

Retained datasets and their evidential status — Dataset: Implied-volatility history

Retained datasets and their evidential status — Dataset: Option-chain snapshot

Retained datasets and their evidential status — Dataset: OHLC series

The multi-asset panel’s provenance does not establish that its values are historical exchange observations. Its broad-index-like series begins at approximately 10,890.55 and ends at 35,897.62. A companion lecture describes a different endpoint and 1,559 observations. Preserve the supplied file’s actual 1,566 rows and document the disagreement rather than changing the numbers to match the slide.

The dataset labels have been generalised to remove commercial index and institutional names. Numeric observations and dates remain unchanged. The labels identify economic roles without implying a new data source or authorising a claim that the files are verified market history.

Use three layers: original observations, validated data and features

Separate original inputs, validation and derived features

Keeping these layers separate makes a calculation reproducible and prevents cleaning decisions from silently rewriting the evidence.

For a daily OHLC bar, check that the high is at least as large as the open and close, that the low is no larger, that prices are positive where required and that dates are unique. For an option chain, validate strike, maturity, type, bid-ask ordering and the units of volatility and sensitivity fields.

Different errors need different responses. A duplicate can be a retransmission, a correction or a distinct timestamp. A missing bar can be an exchange holiday or a failed data observation. An extreme return can be a real jump, a corporate action or a bad print. Automatically dropping every unusual row can remove exactly the events a risk study should examine.

Observation date is not availability time

A value labelled with a date may not have been available at the time the strategy trades. A daily closing price becomes known after the close; a revised economic series may be published later; an option-chain snapshot contains its own quote timestamps. Joining everything on the date alone can make the pipeline use information prematurely.

For each input, record the observation interval and the time at which it becomes usable. A signal computed from a completed daily bar can be passed to a next-session execution process, but not automatically to an execution at that same final close. The event clock should remain consistent through every transformation.

This distinction also applies to a feature that appears purely historical. A normalisation using the entire dataset’s mean and standard deviation embeds later observations in earlier rows. A trailing calculation avoids that specific leak, but its initial warm-up and timing still require documentation.

Define returns and transformations deliberately

For positive price levels, log returns are $r_t=\ln(P_t/P_{t-1})$. They add across adjacent periods, which is useful for compounded analysis. Simple returns are $P_t/P_{t-1}-1$ and map directly into one-period wealth changes. Both are useful; they should not be substituted without adjustment.

The time-series deck reports a price-series unit-root-test p-value around 0.8823 and a log-return p-value around 0.0001 in its example. These are source-reported diagnostic outputs, not guarantees that every price series has a unit root or every return series is stationary.

An ADF rejection addresses a specified unit-root null under the test’s assumptions. It does not prove constant variance, independence or suitability for all models. Differencing again is not a universal repair; it can destroy useful structure or create unnecessary noise. Some valid models are designed to use levels, cointegrating relationships or non-stationary states directly.

Features should retain their meaning and scale

The advanced materials combine momentum, short-term reversal, trend strength, volatility state and a cross-asset signal.

One illustrative composite uses weights of

Composite weights in their original listed order

after trailing standardisation.

The weights sum to one, but that does not make the components independent or the final signal risk neutral.

Store the raw feature, the transformation parameters and the normalised feature separately. A momentum measure based on a percentage deviation from a moving average has a different interpretation from a past cumulative return. A volatility level is different from a change in volatility, and a daily percentage change in a volatility index is different from an equity return.

The multi-asset source is particularly useful for this lesson. It places equity-index-like levels, a currency quote and a volatility index in one table. Correlating their raw levels can produce spurious-looking relationships driven by trends. Even after transformation, the choice between levels, differences and returns should follow the question being asked rather than one automatic operation applied to every column.

Factor extraction belongs inside the training window

Principal-component analysis can summarise common variation across several return series. Its loadings depend on the covariance or correlation matrix used and on how features are scaled. If those quantities are estimated on the full sample, later information leaks into the early feature representation.

Fit the transformation on training observations, apply the fixed transformation to the following test interval and repeat according to the research schedule. Record the loading matrix and any normalisation. A factor-neutral residual is then a residual relative to that fitted factor model; it is not automatically an investable alpha signal.

The same rule applies to feature selection, missing-value imputation, clustering, classification thresholds and probability calibration. Anything learned from data must be learned within the information boundary appropriate to the experiment.

Predictive classification is not an investment outcome

A classifier can output the estimated probability that a future return is positive. Precision, recall, F1 and area under the receiver-operating-characteristic curve describe aspects of classification. They do not directly measure the size of gains and losses or the cost of trading them.

An AUC of 0.65 does not mean a 65% trading win rate. A model can rank outcomes reasonably well while making little money after spreads, or miss a small number of large losses that dominate the account. Threshold selection should connect predicted probabilities to expected payoffs and costs, then be evaluated out of sample.

The source uses AUC thresholds of 0.55 and 0.60 in different sections. These are teaching heuristics rather than a universal deployment standard. Class imbalance, changing base rates and the economic asymmetry of outcomes can make a lower or higher AUC meaningful in different ways.

Keep sample data separate from validation evidence

The 252-row synthetic implied-volatility series begins at spot 24,000 and IV 0.125 and ends at spot 27,038.9 and IV 0.1597. The option-chain exercise uses decimal IVs, so 0.1597 means 15.97%. A dashboard displaying 0.1597% would create a factor-of-100 error.

The two option expiries use year fractions of 0.0192 and 0.0767. A pipeline should keep those precise source values while identifying that they are rounded teaching inputs. It should not reverse-engineer an exact expiry date and present that invented date as original evidence.

The 65-row OHLC sample supports checks of return construction, range estimators and rolling-window alignment. The designed shock lets a user see whether a calculation reacts. It does not supply independent evidence that a forecast model can anticipate a real shock or that a strategy has a positive expected return.

Monitor distribution changes without redesigning history

The advanced time-series material considers structural breaks and distribution shifts. A monitoring system can compare recent feature ranges, residual behaviour and prediction calibration with the training period. A test can flag an inconsistency; it does not uniquely identify the cause or prescribe an automatic trading response.

Choosing an in-sample/out-of-sample boundary after inspecting all future break dates contaminates the holdout. The proper operational analogue uses only break evidence available at the time and applies a predeclared response. Historical full-sample break analysis can still be useful as an explanatory study, but it should be labelled retrospective.

Similarly, a performance-degradation ratio becomes unstable when the in-sample Sharpe is near zero or negative. A distributional p-value above a threshold does not prove the two periods have the same distribution. Monitoring should combine statistical evidence, effect size, sample size and the economic context.

Deliver a feature contract to the research engine

A clean interface gives each model the feature values, their timestamps, valid-sample flags and the transformation version. It identifies the forecast horizon, target definition and any overlapping-label interval. Missing or stale inputs should trigger a visible state rather than an arbitrary default that appears to be a confident signal.

The output should then include the prediction, model version, data timestamp and an explanation of any fallback. A separate execution process decides how that prediction maps to a trade. This separation prevents a change in data cleaning from silently changing the meaning of a published strategy result.

A macro research programme begins with a falsifiable question

The quantitative macro playbook contributes a repeatable research cycle rather than personal career advice. Define the economic mechanism, target, information set, benchmark and rejection condition before fitting a model. Predicting an economic release, the surprise relative to consensus, a yield change and an investable return are separate tasks. An accurate inflation forecast can have no trading value if the same information is already reflected in market expectations.

Begin with an interpretable baseline and one motivated extension. Keep a research log of alternatives attempted, including failures. Record how a transformation, window or model specification was chosen. Comparing only the best of many experiments with a single baseline understates the amount of search behind the result. A publication should make the trial history visible enough to judge the claim.

Publication time belongs in the data contract

Every series should record observation period, public release time, revision vintage, acquisition time, unit, seasonal-adjustment status and license. A monthly observation labelled September was not necessarily available during September. Applying its revised value to an earlier decision can leak future information into an otherwise careful backtest.

The St Louis Fed’s ALFRED documentation, checked for this edition, explicitly distinguishes observation dates from vintage dates and supports first-release and revision views. Its real-time-period format records the interval over which a value was the latest available revision. That is an official example of the concept; each market and data provider still needs its own availability policy.

Match validation to the economic decision

Use time-ordered training and evaluation; estimate transformations only within the training window. When forecasting horizons overlap, make the evaluation design account for information overlap rather than treating adjacent errors as independent. Compare forecast errors and the practical cost of being wrong. If the article makes an investment claim, add a tradable instrument, execution timing, financing, transaction costs and risk constraints.

The playbook’s synthetic exercise demonstrates mechanics, not realised alpha. It should remain labelled synthetic wherever reused. A public research record should distinguish data-engineering tests, statistical forecasting evidence and portfolio performance; a clean notebook alone establishes none of the latter two.

Finish with an inspectable research packet

The reusable output is a question statement, variable register, frozen data vintage, baseline, validation design, sensitivity analysis and a short conclusion that can reject the idea. A release-aware macro monitor and an event study can share this infrastructure without sharing a target. Publish dates and revisions explicitly. If a source revises a figure, preserve the earlier published view and record why the new conclusion changes.

This methodological integration excludes the source’s vacancy, degree-eligibility assessment, personal transition timeline and recruiting advice. It preserves the substantive research-production and publication controls within The Stack, while macro interpretations themselves can cross-link to Arthshastra or Global without duplicating the canonical methods article.

Keep numeric preservation separate from interpretive labels

An anonymised data extract should preserve the original numeric tokens and dates while changing only the identifying labels that need removal. Rounding every value to two decimals may look cleaner, but it can alter downstream returns, small price differences and numerical checks. A machine-readable companion can retain full precision while a public article presents rounded figures for readability.

The supplied option-chain data illustrates why both are useful. A rounded premium table is suitable for explaining a structure; the exact numeric rows are needed to reproduce parity differences, implied-volatility inversion or a delta interpolation. Neither presentation should be mistaken for a quote-quality guarantee. Precision in storage does not imply accuracy of the underlying sample.

A data dictionary should identify which columns are prices, annualised decimal volatilities, deltas, year fractions or date labels. It should also distinguish the sample’s observation period from any date of publication. When a source gives only a rounded time-to-expiry, retain that value rather than inventing an exact timestamp.

For the unverified multi-asset panel, provenance uncertainty remains attached to the dataset itself. A later user should encounter that status when loading the file, not only in a separate report they may never read. This prevents an otherwise useful teaching series from gradually acquiring an unsupported reputation as authentic market history through repeated reuse.

A useful research package includes the numeric data, a data dictionary, explicit status labels, transformation definitions and a record of unresolved inconsistencies. It allows another analyst to recompute a feature without needing to guess whether a percentage was a decimal or whether a historical date was an actual market observation.

That is the purpose of preserving the anonymised companion datasets. They retain the quantitative work while removing commercial labels and avoiding an unsupported claim of live-market provenance. The resulting pipeline is valuable because it keeps the distinction between what was observed, what was constructed and what was knowable at the decision time.

Primary sources checked for this edition

• Federal Reserve Bank of St Louis — ALFRED vintage dates and real-time periods preserve historical availability. Source date: current documentation checked 2026-10-08. https://alfred.stlouisfed.org/help/downloaddata