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Tag: after-work

pythonpyarrowparquetarrowafter-work

PyArrow 25 after work: Parquet bloom fold, hypot, then 25.0.1

Apache Arrow 25.0.0 (10 Jul 2026) plus patch 25.0.1 (10 Aug 2026). Bloom filters fold to the configured FPP, hypot, per-key NULLS FIRST/LAST, ListView in Parquet. 25.0.1: to_pylist without per-element Scalars, SVE unpack fix, Feather deprecation narrowed to V1. Three things to try tonight. Not investment advice.

pythonplotly

maplibre

visualization

after-work

Plotly.py 7 after work: MapLibre maps, drop Mapbox traces

Plotly.py v7.0.0 (25 Aug 2026) bundles Plotly.js 4 and removes Mapbox traces (scattermapbox, choroplethmapbox, densitymapbox, layout.mapbox, mapboxAccessToken) in favour of MapLibre *map. Notebook: px.scatter_mapbox → px.scatter_map. Also gone: old Figure Factories, Kaleido <1, Orca. Upgrade checklist. Not investment advice.

pythonsecedgardatasetsafter-work

Top 5 free open finance datasets after work (SEC first)

Weeknight listicle: SEC companyfacts bulk ZIP (nightly ~3am ET), SEC Financial Statement Data Sets 2026 Q2 ZIP, EdgarTools, pitedgar, optional Kaggle CSV. Download → Polars/pandas → a ratio screen. Not a yfinance vs Polygon bake-off. Not investment advice.

pythonportfolioriskvolatilityafter-work

Top 5 Python packages for portfolio risk and volatility after work

A weeknight listicle: Riskfolio-Lib 7.3.0 (31 May 2026, Entropy Pooling + MVSK), PyPortfolioOpt risk_models, arch GARCH, cvxpy, and statsmodels/scipy. pip plus a CVaR / HRP / vol sketch. Not FinRL, not vectorbt/Nautilus/Lean, not a ranking contest. Not investment advice.

pythonscipysignalarray-apiafter-work

SciPy 1.18 after work: Whittaker–Henderson, Array API, Python 3.15 wheels

SciPy 1.18.0 (19 Jun 2026) adds scipy.signal.whittaker_henderson (HP-like at penalty order 2) and broader Array API / JAX stats. 1.18.1 (21 Aug 2026) is the first PyPI release with Python 3.15 binaries. Upgrade tonight? Not investment advice.

pythonscipysignalarray-apiafter-work

SciPy 1.18 after work: Whittaker–Henderson, Array API, Python 3.15 wheels

pythonpolarssqlicebergafter-work

Polars 1.44 after work: Iceberg schema merge, cloud rate limits, SQL subqueries

Polars 1.44 (26 Aug 2026, Thijs Nieuwdorp) adds sink_iceberg schema_mode=merge, an adaptive S3/GCS/Azure rate limiter, correlated SQL subqueries, and selective when/then speedups. Three things to try tonight. Not a pandas bake-off. Not investment advice.

pythonpolarssqlicebergafter-work

Polars 1.44 after work: Iceberg schema merge, cloud rate limits, SQL subqueries

pythonstatsmodelsforecastingtime-seriesafter-work

Statsmodels 0.15 after work: Diebold–Mariano, Pesaran–Timmermann, Hamilton filter

Statsmodels 0.15.0 (27 Aug 2026) adds Diebold–Mariano, Pesaran–Timmermann, the Hamilton filter, NamedTuple results, rng= (SPEC-007), and optional Polars input converted to pandas at entry. After-work upgrade checklist plus a mini forecast-eval notebook. Not investment advice.

pythonpapertime-seriestransformerssurveyafter-work

Introducing Transformers in Time Series: A Survey for after-work quants

Wen, Zhou, Zhang, Chen, Ma, Yan and Sun (IJCAI 2023; arXiv:2202.07125) survey Transformers for time series. Taxonomy: module tweaks (positional encodings, sparse/low-rank attention) vs architecture (hierarchical). Applications: forecasting, anomaly, classification. Their ETTm2 checks: longer inputs often hurt; 3–6 layers beat 48; seasonal-trend decomposition can cut MSE 50–80%. Map before you pick Informer vs PatchTST. Plain-language intro. Not investment advice.

pythonpapertime-seriesitransformerforecastingafter-work

Introducing iTransformer: Inverted Transformers Are Effective for Time Series Forecasting for after-work quants

Liu, Hu, Zhang, Wu, Wang, Ma and Long (ICLR 2024; arXiv:2310.06625) invert the Transformer: each variate’s full look-back is one token. Attention mixes assets; FFN (shared MLP) encodes that series; LayerNorm is per variate. No new attention gadget. Strong on high-dimensional ECL/Traffic/PEMS; inverting also lifts vanilla/Informer/Reformer. thuml/iTransformer. Plain-language intro. Not investment advice.

pythonpapertime-seriestimesnetforecastingafter-work

Introducing TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis for after-work quants

Wu, Hu, Liu, Zhou, Wang and Long (ICLR 2023; arXiv:2210.02186) argue 1D series hide intraperiod vs interperiod variation. FFT picks top-k periods, reshape each into a 2D tensor (columns = inside a period, rows = same phase across periods), run a shared Inception block, softmax-amplitude mix. SOTA-ish across forecasting, imputation, UEA classification, anomaly detection. thuml/TimesNet. Plain-language intro. Not investment advice.

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