A regime-aware macro-financial research framework for European markets. Built end-to-end in Python across data infrastructure, regime detection, risk analysis, tactical allocation backtesting, stress testing, and an interactive dashboard.
This platform detects macroeconomic regimes across European cross-asset markets, computes regime-conditional risk metrics on a multi-asset portfolio, and tests tactical allocation strategies under realistic walk-forward backtest conditions. It is structured around six phases — data spine, feature engineering, regime detection, risk engine, tactical allocation, stress testing + deliverables — each producing concrete artifacts.
The project's defining characteristic is its commitment to walk-forward methodology, lookahead-bias enforcement, and honest reporting of both positive and negative findings. The backtest results contain a deliberately documented negative finding: at weekly horizon, regime-based and ML-based tactical timing strategies underperform equal-weight, while a risk-parity baseline reduces volatility by ~30% with similar Sharpe. The institutional payoff of the regime detection layer is in risk monitoring, not return timing.
Left: walk-forward Sharpe by strategy (2011–2026, net of 5bp costs) — every tactical-timing strategy lands below the equal-weight benchmark (dashed line). Right: 99% historical Expected Shortfall by regime — the institutionally useful result. Re-run python scripts/build_strategies.py for the full equity-curve and drawdown plots, or python make_figures.py to regenerate this summary.
Regime detection works. A 3-state Gaussian Hidden Markov Model on five macro features cleanly identifies every major European macro stress event 2008-2026 — GFC, sovereign crisis, 2015 Bund tantrum, Q1 2016 China/oil, COVID, 2022 inflation/Russia — plus the leading-into-Lehman period 2007-08. Cross-tab against a 2-state baseline shows the third state earns its keep by surfacing transition periods (wider IT-DE spreads, calm equity vol) that pure equity-vol classifiers miss.
Regime-conditional risk metrics work. Historical Expected Shortfall at 99% confidence is 6.9% in calm regimes vs 17.5% in crisis — a 2.5x scaling that an unconditional VaR would mis-price. This is the institutionally credible application of the regime layer.
99% Cornish-Fisher VaR is 2x the parametric Gaussian VaR. Portfolio skew -0.80 and excess kurtosis 4.4 (unconditionally) mean Gaussian VaR understates 99% tail risk by more than half.
Tactical allocation underperformed equal-weight, and we explain why. Six strategies were walk-forward backtested with 5bp transaction costs and ESTR-financed leverage, scored on a common 2011-2026 window:
| Strategy | Sharpe | Max DD | Ann turnover |
|---|---|---|---|
| Equal Weight | 0.29 | -44.7% | 0% |
| ERC (risk parity) | 0.26 | -36.7% | 16% |
| VT-ERC | 0.21 | -40.2% | 24% |
| Regime-Tilt | 0.17 | -43.0% | 30% |
| Vol-Forecast (LightGBM) | 0.21 | -40.2% | 37% |
| DD-Predict (LightGBM classifier) | 0.16 | -40.2% | 206% |
(Figures above are the original full-period run. The backtest now (a) classifies regimes out-of-sample with an annually-refit walk-forward HMM, (b) charges the spliced ESTR rate on leverage instead of assuming it free, and (c) scores all six strategies on the common post-2011 window so the timing strategies are not credited with the pre-signal VT-ERC path through the 2008 GFC. These shift the levels but not the ordering or the conclusion; re-run build_strategies.py to regenerate.)
The regime-tilted strategy failed because the HMM crisis state captures both crash and recovery weeks; crisis-regime annualized return is +20.8% for EW, so cutting equity in crisis sells low and buys high. The drawdown classifier achieved AUC 0.51 — essentially no predictive signal at weekly horizon. Conclusion: regime detection is a risk-monitoring tool, not a return-timing signal.
macro_regime/
├── config/ # ticker registry
├── data/
│ ├── ingestion/ # yfinance, FRED, ECB fetchers
│ ├── alignment.py # weekly Friday-close harmonization
│ └── storage.py # parquet I/O
├── features/ # Phase 2 — 63-feature library
│ ├── returns.py
│ ├── volatility.py
│ ├── spreads.py
│ ├── correlations.py
│ ├── momentum.py
│ ├── macro_lag.py # publication-lag enforcement
│ ├── freshness.py # staleness check
│ └── short_rate.py # EONIA/ESTR splice
├── regimes/ # Phase 3 — HMM core
│ ├── data_prep.py
│ ├── hmm_model.py # multi-seed Gaussian HMM (full-sample)
│ ├── walk_forward_hmm.py # annually-refit, out-of-sample HMM for tactical use
│ └── diagnostics.py
├── risk/ # Phase 4 — risk engine
│ ├── portfolio.py
│ ├── var.py # historical, parametric, Cornish-Fisher
│ ├── expected_shortfall.py
│ ├── drawdown.py
│ ├── beta.py
│ ├── regime_conditional.py
│ └── excel_export.py # parallel openpyxl workbook
├── forecast/ # Phase 5 / 5b — LightGBM models
│ ├── vol_model.py # walk-forward vol forecast
│ └── drawdown_model.py # walk-forward classifier
├── portfolio/ # Phase 5 — allocation rules
│ ├── risk_parity.py # ERC solver + vol targeting
│ └── weights.py # tilting functions
├── backtest/ # Phase 5 — walk-forward engine
│ ├── engine.py
│ └── metrics.py
├── stress/ # Phase 6 — stress engine
│ ├── scenarios.py
│ └── transmission.py # empirical-beta-based
├── dashboard/
│ └── app.py # Streamlit 4-tab dashboard
├── scripts/ # orchestrators (one per phase)
│ ├── build_panel.py
│ ├── build_features.py
│ ├── build_regimes.py
│ ├── build_risk.py
│ ├── build_strategies.py
│ ├── build_stress.py
│ └── build_deck.py
├── reports/ # generated artifacts (PNG, XLSX, PPTX)
├── data_store/ # gitignored cache (raw + processed parquet)
├── requirements.txt
├── sources.md # data provenance audit trail
└── README.md
Python 3.12+ recommended (3.10+ should work; the codebase avoids deprecated APIs).
python -m venv .venv
source .venv/bin/activate # Windows Git Bash: source .venv/Scripts/activate
pip install -r requirements.txtEach phase has one orchestrator. Run in order; later phases depend on earlier outputs.
py scripts/build_panel.py # Phase 1 — fetch and align the data spine
py scripts/build_features.py # Phase 2 — build the 63-feature library
py scripts/build_regimes.py # Phase 3 — fit 2- and 3-state HMMs with diagnostics
py scripts/build_risk.py # Phase 4 — risk metrics + Excel workbook
py scripts/build_strategies.py # Phase 5/5b — six strategies, walk-forward backtest
py scripts/build_stress.py # Phase 6 — stress testing
py scripts/build_deck.py # Phase 6 — generate the methodology PowerPoint
streamlit run dashboard/app.py # Phase 6 — launch the interactive dashboardTotal fresh-install runtime: approximately 5-7 minutes on a typical laptop.
(Use python instead of py on macOS/Linux.)
| Phase | Deliverable | Output |
|---|---|---|
| 1 | Data spine | data_store/panel/master_panel.parquet (~1,114 weekly rows, 2005-2026) |
| 2 | Feature library | data_store/panel/master_features.parquet (63 features) |
| 3 | HMM regime detection | regime_overlay_3state.png, regime_probabilities_3state.png, persisted viterbi/probabilities/transitions/emissions |
| 4 | Risk engine | risk_workbook.xlsx (4 sheets with named ranges), regime-conditional metrics |
| 5/5b | Tactical backtest | strategy_comparison.png, strategy_drawdowns.png, drawdown_predictions.png, per-strategy weights + returns |
| 6 | Stress + dashboard + deck | stress_results.parquet, Streamlit dashboard, macro_regime_methodology_deck.pptx (16 slides) |
Lookahead-bias enforcement. Macro releases (HICP) are forward-shifted by their publication lag before entering any model. LightGBM training at refit date d uses only rows whose forecast target was observable by d.
Walk-forward. LightGBM models refit every 13 weeks on expanding window. Covariance for ERC recomputed at each weekly rebalance on trailing 156-week window. The regime HMM used for tactical tilting is refit annually on an expanding window, standardized with training-window statistics only, and each week is classified by Viterbi-decoding the expanding prefix — so no future observation informs a week's regime label.
Funding cost. Vol-targeted leverage is financed at the spliced ESTR/EONIA overnight rate rather than assumed free; idle cash earns the same rate.
Short-rate splice. ESTR began October 2019; EONIA (its predecessor) is spliced in for pre-2019 history with the 8.5bp adjustment per ECB Recommendation 2019/C 295/02, producing a level-consistent overnight rate back to 2005.
HMM stability. Best-of-5 random-seed initialization for the 3-state model defends against local optima.
Realistic frictions. 5bp one-way transaction cost charged on actual turnover.
- HMM regime labels for tactical allocation are now fitted walk-forward. The tilt consumes an annually-refit, expanding-window HMM (
regimes/walk_forward_hmm.py) with per-window standardization and prefix-decoded, no-lookahead inference. Worth noting for interpretation: the earlier full-sample fit handed the regime-tilt strategy a lookahead advantage and it still lost to equal-weight — so removing that advantage can only reinforce the null finding, not overturn it. The full-sample labels are retained solely for the in-sample regime-conditional risk table, where they are a descriptive, not predictive, statistic. - Stress transmission uses linear empirical betas. Tail co-movement is non-linear in practice; a more sophisticated version would use conditional copulas or a regime-switching factor model.
- Weekly frequency limits ML training data. The drawdown classifier achieved AUC 0.51, partly because weekly horizon yields few positive examples per refit window.
- Italian and French 10Y yields can run ~90 days stale due to FRED's OECD update cadence. Flagged at build time by the freshness check.
- VSTOXX (V2X) unavailable on Yahoo Finance. Replaced by SX5E rolling realized volatility.
- Modeling: hmmlearn (HMM), LightGBM (forecasting), SciPy (optimization)
- Data: pandas, numpy, pyarrow (parquet), requests, yfinance
- Visualization: matplotlib, plotly (Streamlit)
- Deliverables: openpyxl (Excel), python-pptx (PowerPoint), Streamlit (dashboard)
- All code: Python 3.12, modular architecture
Shrish Dhuria · ESSEC Master in Finance · May 2026
Built as a research framework demonstrating institutional methodology in regime detection, multi-asset risk analysis, and walk-forward tactical allocation. The negative finding on tactical regime-tilting is the deliberate intellectual contribution — research that documents what doesn't work, and why, is more useful than work that papers over null results.
Scope conditions on the headline finding:
- The null result is conditional on the design. It holds for this feature set, a 3-state Gaussian HMM, weekly horizon, and a European cross-asset universe; a different model, horizon, or market could differ — the contribution is the rigorous demonstration, not a universal law.
- HMM assumptions are strong. Gaussian emissions and a first-order Markov structure miss fat tails and longer-memory regime persistence.
- Limited regime switches in the OOS sample. Honest walk-forward leaves only a handful of true regime transitions post-2011, so statistical power on the timing claim is modest (the risk-monitoring claim is far better supported).
- Publication lags are approximated (e.g. 30 days for HICP) rather than reconstructed from true data vintages.
- Costs strengthen, not weaken, the finding. Transaction costs and ESTR-financed leverage are modelled but simplified; tightening them pushes the timing strategies further below equal-weight.
A pytest suite under tests/ validates the two claims a reviewer would
challenge first — that the risk maths is correct and that the walk-forward
labelling is genuinely out-of-sample. Fixtures are synthetic two-regime panels,
so the suite is self-contained.
- Cornish-Fisher VaR — collapses to the Gaussian VaR when skew and excess kurtosis vanish, and inflates the left tail under negative skew / fat tails.
- HMM regime detection — recovers a known two-regime synthetic series (valid stochastic transition matrix; the "stress" state carries the higher fitted equity-vol mean; Viterbi accuracy > 0.9).
- No look-ahead (the headline guarantee) — truncating the panel at any date t* leaves every walk-forward label dated ≤ t* unchanged, proving labels never use future information.
pip install -r requirements-dev.txt # includes hmmlearn
pytest tests/ -q # 5 testsTests run automatically on every push via GitHub Actions (.github/workflows/tests.yml).
Use freely for research and educational purposes. No warranty.