fx_pairs

Case Study: FX Spot Pairs

This case study uses daily OHLCV data from OANDA for 20 G10 currency pairs to test whether momentum and carry signals produce tradeable alpha in the most liquid market on earth. FX presents a structurally challenging prediction problem: tight spreads enable low-cost execution, but the cross-section is small and correlated. The daily return-correlation matrix has a participation ratio of 5.27 effective bets. USD and JPY each appear in 7 of the 20 pairs.

The pipeline is a study in hypothesis revision, from short-horizon momentum to multi-horizon carry and a full strategy assessment. It compares signal quality, selection-adjusted Sharpe, and holdout performance against the equal-weight benchmark. The teaching point is what disciplined assessment looks like when a signal has to be judged on interval evidence rather than on a point estimate.

At a Glance

Property	Value
Asset Class	FX spot pairs (G10 currencies)
Frequency	Daily
Universe	20 major and cross pairs
History	2005-2025
Primary Label	fwd_ret_1d
CV Folds	8 (5Y train, 1Y val)
Cost Model	Material (1--8 bps spread)

Pipeline

Stage	Notebook	Chapter	Description	Writes
Feasibility	01_feasibility_analysis	Ch6	Universe breadth at the daily decision, effective independent bets, assumed round-trip cost per pair, move-to-cost scale by horizon, return persistence, and the declared walk-forward folds	none
Labels	02_labels	Ch7	1-day, 5-day, and 21-day forward returns	labels/fwd_ret_1d.parquet, labels/fwd_ret_5d.parquet, labels/fwd_ret_21d.parquet
Features	03_financial_features	Ch8	Momentum, carry, volatility, and mean-reversion features	features/financial.parquet
Temporal	04_model_based_features	Ch9	Walk-forward ARIMA, HMM, and spectral features	features/model_based.parquet
Evaluation	05_evaluation	Ch7--9	Feature-label IC diagnostics across 20 pairs	evaluation/triage_ledger.parquet, evaluation/ic_timeseries.parquet
Linear	06_linear	Ch11	Ridge, LASSO, ElasticNet on momentum and carry signals	Training runs and prediction sets in run_log/registry.db; coefficients under run_log/training/{hash}/, scores under run_log/predictions/{hash}/
GBM	07_gbm	Ch12	LightGBM testing non-linear USD factor and momentum interactions	Training runs and prediction sets; boosters, learning_curves.parquet, and fold_metrics.parquet under run_log/training/{hash}/
Tabular DL	08_tabular_dl	Ch12	TabM attention-style ensembling on the FX feature matrix	Training runs and prediction sets; checkpoints under run_log/training/tabular_dl/
TCN	09_dl_tcn	Ch13	Dilated causal convolutions for daily FX dynamics	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
NLinear	10_dl_nlinear	Ch13	Tests whether FX dynamics are approximately linear	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
LSTM	10a_dl_lstm	Ch13	The recurrent member of the three architectures the deep_learning menu declares; what it carries forward is not fixed the way NLinear's subtraction is	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
Causal DML	11_causal_dml	Ch15	Does FX momentum cause future returns or reflect overshooting?	A row in the registry's causal_runs
Model Analysis	12_model_analysis	--	Cross-model IC comparison and fold stability diagnostics	Nothing - it reads the registry
Backtest	13_backtest	Ch16	Long-short daily FX strategy simulation	One backtest run per prediction set and entry scheme; daily_returns.parquet, weights.parquet, trades.parquet, fills.parquet, equity.parquet, portfolio_state.parquet, and spec.json under run_log/backtest/{hash}/
Portfolio	14_portfolio_management	Ch17	Allocation methods for the small FX cross-section	One backtest run per allocation method, same artifact layout
Risk	15_risk_management	Ch19	Position-level controls compared with the unoverlaid carrier	One backtest run per overlay variant, same artifact layout
Costs	16_costs	Ch18	Spread impact on the selected carrier, controls included	One backtest run per cost level, same artifact layout
Holdout Predictions	17_holdout_predictions	Ch16-20	Refits the validation-selected configuration on the holdout interval	One holdout prediction set at the selected checkpoint
Holdout Backtest	18_holdout_backtest	Ch16-20	Replays the selected strategy against those predictions	One holdout backtest run
Strategy Analysis	19_strategy_analysis	Ch20	Reads the validation and holdout results back and reports them with interval evidence	Registry rows rather than files: cohort_metrics for the candidate cohorts and backtest_paired_metrics for the bootstrapped comparisons

Running

Run every command from the repository root with the project environment. Set ML4T_DATA_PATH to a data root containing the consolidated OANDA files under fx/; see data/fx/README.md. The project requires Python 3.14 and installs with uv sync. The GBM notebook uses the configured GPU when available and falls back to CPU. The TabM, TCN, and NLinear notebooks use their deterministic CPU defaults.

The sequence below reads the registry and its cached artifacts. Notebooks 13-16 default to RUN_SWEEP=False, so this path reports the registered result without rerunning the sweep.

# From repo root
uv run python case_studies/fx_pairs/01_feasibility_analysis.py
uv run python case_studies/fx_pairs/02_labels.py
uv run python case_studies/fx_pairs/03_financial_features.py
uv run python case_studies/fx_pairs/04_model_based_features.py
uv run python case_studies/fx_pairs/05_evaluation.py
uv run python case_studies/fx_pairs/06_linear.py
uv run python case_studies/fx_pairs/07_gbm.py
uv run python case_studies/fx_pairs/08_tabular_dl.py
uv run python case_studies/fx_pairs/09_dl_tcn.py
uv run python case_studies/fx_pairs/10_dl_nlinear.py
uv run python case_studies/fx_pairs/10a_dl_lstm.py
uv run python case_studies/fx_pairs/11_causal_dml.py
uv run python case_studies/fx_pairs/12_model_analysis.py
uv run python case_studies/fx_pairs/13_backtest.py
uv run python case_studies/fx_pairs/14_portfolio_management.py
uv run python case_studies/fx_pairs/15_risk_management.py
uv run python case_studies/fx_pairs/16_costs.py
uv run python case_studies/fx_pairs/17_holdout_predictions.py
uv run python case_studies/fx_pairs/18_holdout_backtest.py
uv run python case_studies/fx_pairs/19_strategy_analysis.py

Results

This README describes how the case study is built, not what it found. Results are not restated here: the registry is rebuilt whenever the case study is re-derived, and a number copied into prose stays correct only until the next rebuild.

19_strategy_analysis reads the registry back and reports the selected configuration with its interval evidence. That notebook, and the registry it reads, are where a result comes from.

To read the results without training anything, download the published bundle, which carries the registry and the artifacts behind it:

uv run python scripts/download_artifacts.py --cs fx_pairs

Two bundles are published, and they are separate generations rather than revisions of one another. v3.1.0-artifacts is current and is what the command above fetches. v3.0.0-artifacts holds the results as first published. The 3.1 rebuild re-keyed every content-addressed hash, so a hash taken from one bundle does not resolve in the other.

Run Log

run_log/registry.db records every training run, prediction set and backtest, each addressed by a hash of the specification that produced it. The artifacts sit beside it under run_log/training/, run_log/predictions/ and run_log/backtest/.