Framework: alphaswarm_docs/analysis-framework.md · UI: alphaswarm_docs/analysis-lab.md.
Every flow in the registry. Each flow is identified by a namespaced
name (namespace.flow), declares a Pydantic params model, and returns
a FlowResult with metrics / rows / chart / optional
arrow_table for Iceberg persistence.
GET /analysis/flows lists every entry with the JSON-schema body
derived from the params model — the lab UI auto-renders forms from
this surface.
profiling.*
| Name | Label | Notes |
|---|
profiling.describe | Column profile | Wraps alphaswarm.data.profiling.compute_profile |
profiling.dtypes | Dtypes | Per-column dtype + memory footprint |
profiling.null_audit | Null audit | Null counts + null fractions |
profiling.topk | Top-K values | Most-frequent values + share |
distribution.*
| Name | Label | Notes |
|---|
distribution.descriptive_stats | Descriptive stats | Mean / median / std / skew / kurt / IQR / MAD / quantiles |
distribution.histogram | Histogram | Equal-width bins + Plotly chart |
distribution.ecdf | Empirical CDF | Sorted-value ECDF (down-sampled to max_points) |
distribution.qq_plot_points | Q-Q plot points | Slope/intercept fit vs. norm/t/uniform/expon |
distribution.shapiro_wilk | Shapiro-Wilk | Normality test (capped at 5000 samples) |
distribution.jarque_bera | Jarque-Bera | Skew + kurt goodness-of-fit |
distribution.kolmogorov_smirnov | K-S | One-sample vs reference dist (norm / t / uniform / expon / lognorm) |
outlier.*
| Name | Label | Notes |
|---|
outlier.zscore | Z-score | Robust (median/MAD) or classical |
outlier.iqr | IQR fences | Tukey [Q1 - kIQR, Q3 + kIQR] |
outlier.iforest | Isolation Forest | sklearn |
outlier.dbscan | DBSCAN | Density-based; -1 is noise |
outlier.lof | LOF | sklearn LocalOutlierFactor |
outlier.ecod | ECOD | PyOD; falls back to z-score |
outlier.pulse_vs_step | Pulse vs Step | Distinguish transient pulses from level shifts |
imputation.*
| Name | Label | Notes |
|---|
imputation.ffill_bfill | Forward / backward fill | Default ffill_then_bfill |
imputation.linear | Linear interpolation | pandas axis=0 |
imputation.spline | Cubic spline | pandas spline (order configurable) |
imputation.knn | KNN imputer | sklearn KNNImputer |
imputation.mice | MICE (IterativeImputer) | sklearn IterativeImputer |
regression.*
| Name | Label | Notes |
|---|
regression.ols_diagnostics | OLS diagnostics | Coefs + SE + t / p + Durbin-Watson + AIC / BIC |
regression.white_test | White's test | Heteroskedasticity (general form) |
regression.breusch_pagan | Breusch-Pagan | Heteroskedasticity vs regressors |
regression.vif | VIF | Variance Inflation Factors per regressor |
regression.fama_macbeth | Fama-MacBeth Two-Step | Cross-sectional risk-premium estimation |
regression.panel_pooled | Pooled panel OLS | alphaswarm/analysis/flows/econometrics_panel.py |
regression.panel_fixed | Fixed-effects panel OLS | alphaswarm/analysis/flows/econometrics_panel.py |
regression.panel_random_effects | Random-effects panel | alphaswarm/analysis/flows/econometrics_panel.py |
regression.panel_between | Between panel estimator | alphaswarm/analysis/flows/econometrics_panel.py |
regression.panel_first_difference | First-difference panel | alphaswarm/analysis/flows/econometrics_panel.py |
time_series.*
| Name | Label | Notes |
|---|
time_series.stl | STL decomposition | Trend / seasonal / residual |
time_series.adf | Augmented Dickey-Fuller | H0 = unit root |
time_series.kpss | KPSS | H0 = stationary (ADF complement) |
time_series.acf_pacf | ACF / PACF | Auto- and partial-autocorrelation series |
time_series.garch | GARCH(p, q) | Volatility model + horizon variance forecast |
time_series.change_point | Change-point | ruptures.KernelCPD with rbf kernel |
time_series.granger_causality | Granger causality | Up to max_lag |
time_series.cointegration | Engle-Granger | Pair cointegration |
time_series.spectral_fft | Spectral (FFT) | Real FFT magnitude + power spectrum |
time_series.spectral_wavelet | Continuous wavelet transform | PyWavelets (optional) |
time_series.hurst_exponent | Hurst exponent | Long-range dependence |
time_series.theil_sen | Theil-Sen slope | Robust median-of-pairwise-slopes |
derivatives.*
| Name | Label | Notes |
|---|
derivatives.bsm | Black-Scholes-Merton | Closed-form European price + Greeks |
derivatives.greeks_surface | Greeks surface | Δ/Γ/ν/Θ/ρ across strikes × expiries |
derivatives.implied_volatility | Implied volatility (Brent) | Recover σ from a market quote |
derivatives.monte_carlo_european | MC European option | Vectorised GBM; opt-in CUDA via cupy |
derivatives.monte_carlo_barrier | MC barrier option | Knock-in / knock-out variants |
derivatives.monte_carlo_asian | MC Asian option | Arithmetic / geometric averaging |
derivatives.sabr_smile | SABR smile (Hagan) | Hagan-Kumar-Lesniewski-Woodward 2002 |
derivatives.bachelier | Bachelier (normal model) | Wraps alphaswarm.options.normal_model |
derivatives.greeks_surface_jax | Greeks surface (JAX/vmap) | JAX-vmap / fast-vollib backend via alphaswarm.options.greeks_jax |
derivatives.lucic_tse_quotes | Lucic-Tse portfolio quotes | Lucic-Tse (2024-2026) optimal bid/ask matrix; see alphaswarm.options.portfolio_mm |
derivatives.chain_summary | Chain summary | Wraps alphaswarm/analysis/flows/derivatives_chain.py |
derivatives.chain_strategies | Chain strategy quotes | Wraps alphaswarm/analysis/flows/derivatives_chain.py |
derivatives.chain_skew | Chain IV skew | Wraps alphaswarm/analysis/flows/derivatives_chain.py |
portfolio.*
| Name | Label | Notes |
|---|
portfolio.markowitz_efficient_frontier | Efficient frontier | cvxpy if available, numpy-only fallback |
portfolio.ledoit_wolf_shrinkage | Ledoit-Wolf covariance | Stabilised covariance matrix |
portfolio.fama_french_5_rolling | FF5 rolling betas | Rolling-window OLS on Mkt-RF / SMB / HML / RMW / CMA |
portfolio.risk_parity | Risk parity | Equal-risk-contribution weights (Spinu 2013) |
portfolio.robust_optimize | Robust Mean-Variance | alphaswarm/analysis/flows/portfolio.py |
portfolio.mdpf_allocate | MDPF Allocation | alphaswarm/analysis/flows/portfolio.py |
portfolio.concentration | Weight Concentration Analysis | alphaswarm/analysis/flows/concentration.py |
factors.*
| Name | Label | Notes |
|---|
factors.evaluate | Factor evaluation | Wraps alphaswarm.data.factors.evaluate_factor (IC + quantile spread + turnover) |
microstructure.*
| Name | Label | Notes |
|---|
microstructure.realised_volatility | Realised volatility (OHLC) | Close-to-close / Parkinson / GK / RS / YZ |
microstructure.order_book_imbalance | Order-book imbalance | Top-of-book |
microstructure.vpin | VPIN | Wraps alphaswarm.data.microstructure.vpin |
Other namespaces
The registry has grown beyond the tables above since this page was last
synced. alphaswarm/analysis/flows/ also ships arbitrage.*
(arbitrage.johansen_basket, arbitrage.pair_signal,
arbitrage.ah_share_basis, arbitrage.adr_basis), futures.*
(futures.continuous_curve), fundamentals.*
(fundamentals.piotroski_fscore, fundamentals.altman_zscore,
fundamentals.beneish_mscore, fundamentals.mohanram_gscore),
market_dynamics.* (market_dynamics.slice_and_merge),
optimal_control.* (optimal_control.avellaneda_stoikov_quotes,
optimal_control.cartea_jaimungal_liquidation,
optimal_control.lucic_tse_portfolio_quotes,
optimal_control.obizhaeva_wang_solve,
optimal_control.toxicity_regime), risk.*
(risk.daily_portfolio), screening.* (screening.rank), and
technical.* (technical.vwap, technical.fib, technical.cones,
technical.clenow). These aren't broken out into full tables here yet
— check GET /analysis/flows or the corresponding module under
alphaswarm/analysis/flows/ for current params.
Optional dependencies
Flows tag their optional deps (optional_dependencies field on the
descriptor). Missing extras raise a friendly RuntimeError("install extra X") instead of crashing the catalog.
| Dep | Used by |
|---|
scikit-learn | outlier.{iforest,dbscan,lof}, imputation.{knn,mice}, portfolio.ledoit_wolf_shrinkage |
statsmodels | regression.*, time_series.{adf,kpss,acf_pacf,granger_causality,cointegration,stl} |
arch | time_series.garch |
ruptures | time_series.change_point |
pywavelets | time_series.spectral_wavelet |
pyod | outlier.ecod (falls back to z-score) |
cvxpy | portfolio.markowitz_efficient_frontier (falls back to numpy projection) |
cupy | derivatives.monte_carlo_* (opt-in GPU acceleration) |