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Short evening lessons for people who work. Quant trading, Python, data, systems, architecture.

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pythonpapergicscovarianceafter-work

Introducing Incorporating GICS into Portfolio Allocation for after-work quants

Fan, Furger & Xiu (2016) sort S&P 500 residuals by GICS, keep industry blocks, add Fama–French and sector ETFs on high-frequency data, then allocate. Plain-language intro. Not investment advice.

pythonpaperganbacktest

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after-work

Introducing Improving Robustness of Trading Strategy Backtesting with Boltzmann/GANs for after-work quants

Lezmi, Roche, Roncalli & Xu (2020) use RBMs, GANs and Wasserstein models as market generators so backtest statistics have a distribution, not one lucky path. Plain-language intro. Not investment advice.

pythonpapercovarianceshrinkageafter-work

Introducing Honey, I Shrunk the Sample Covariance Matrix for after-work quants

Ledoit & Wolf (2004) shrink the noisy sample covariance toward a structured target so Markowitz weights stop exploding. Plain-language intro for after-work Python learners. Not investment advice.

pythonpapervolatilitygarchafter-work

Introducing Realized GARCH for after-work quants

Hansen, Huang & Shek (2012) complete GARCH-X with a measurement equation linking realised variance to latent h_t, plus a leverage function. The daily-return-only GARCH is the nested special case. Not investment advice.

pythonpapervarcnnafter-work

Introducing Quantile CNN VaR for after-work quants

Petneházi (2019) forecasts VaR with a dilated causal CNN + pinball/quantile loss, trained jointly across many stocks. Single-name QCNN overshoots; the joint net is the point. Not investment advice.

pythonpapervarneural-networksafter-work

Introducing Neural Networks and Value at Risk for after-work quants

Arimond, Borth, Hoepner, Klawunn & Weisheit (2020) train generative regime-switching nets for VaR: HMM init, a two-objective loss, and a data-size warning. Not investment advice.

pythonpapermvohrpreinforcement-learningafter-work

Introducing MVO vs HRP vs RL Portfolio Comparison for after-work quants

Sen et al. (2023) bake off mean–variance optimisation, hierarchical risk parity, and reinforcement-learning allocators on the same playground. Plain-language intro. Not investment advice.

pythonpapercovariancehigh-frequencyfactorsafter-work

Introducing Knowing Factors or Factor Loadings, or Neither? for after-work quants

Dai, Lu & Xiu (2019) compare large-covariance estimators from noisy high-frequency data when you know factors, know loadings (BARRA-style), or know neither. Plain-language intro. Not investment advice.

pythonpapergicscovariancehigh-frequencyafter-work

Introducing Incorporating GICS into Portfolio Allocation for after-work quants

Fan, Furger & Xiu (2016) fold GICS industry structure into large covariance estimates from high-frequency data so portfolio risk is not a dense noise matrix. Plain-language intro. Not investment advice.

pythonpaperganbacktestrobustnessafter-work

Introducing Improving Robustness of Trading Strategy Backtesting with Boltzmann/GANs for after-work quants

Lezmi et al. (2020) use Boltzmann machines and GANs to simulate extra market paths so a strategy’s backtest is less of a one-history lottery. Plain-language intro. Not investment advice.

pythonpapergarchlstmafter-work

Introducing Unified GARCH-RNN Volatility Forecasting for after-work quants

Wei, Yang & Cui (2025) embed GARCH(1,1) inside GRU and LSTM cells—joint ω, α, β plus neural gates—on SPX, DJI and NASDAQ. Not investment advice.

pythonpaperreinforcement-learningportfolioafter-work

Introducing A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem for after-work quants

Jiang, Xu & Liang (2017) propose EIIE + Portfolio-Vector Memory + online batch learning: model-free RL that outputs portfolio weights on Poloniex. Not investment advice.