SoloQuant Flagship Program Early Bird · $420 USD (Save $379) · Sep 1–Dec 1, 2026 · Launch Jan 1, 2027 →

SoloQuant Academy

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

Information Hub Student Portal Privacy Policy Terms & Conditions

Academy notes

Information Hub

Articles and updates from SoloQuant Academy.

Tag: paper

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.

pythonpapergarch

lstm

after-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.

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.

pythonpapergarchvolatilityafter-work

Introducing Realized GARCH for after-work quants

Hansen, Huang & Shek (2012) jointly model daily returns and realized measures: RV is a noisy observation of latent variance plus a leverage polynomial. High-frequency data into the GARCH workhorse. Plain-language intro. Not investment advice.

pythonpapervarcnnafter-work

Introducing Quantile CNN VaR for after-work quants

Petneházi (2019) turns a dilated causal CNN into a VaR machine with quantile (pinball) loss, and shows joint training on many stocks beats one-ticker nets. Plain-language intro. Not investment advice.

pythonpapervarriskafter-work

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

Arimond, Borth, Hoepner, Klawunn & Weisheit (2020) generate regime-switching returns for VaR: HMM init, a balanced RNN loss, and a data-hungry caveat. Not investment advice.

pythonpapervarriskafter-work

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

Arimond et al. (2020) run Monte-Carlo VaR from generative regime-switching nets. HMM init, a balanced bull/bear loss, and data-hungry RNNs. Plain-language intro. Not investment advice.

pythonpapercovariancehigh-frequencyafter-work

Introducing Large Covariance from Noisy High-Frequency Data for after-work quants

Dai, Lu & Xiu (2019) horse-race TSR, CSR and PCA factor covariances on noisy, asynchronous ticks with pre-averaging and thresholding. Plain-language intro. Not investment advice.

pythonpaperportfoliohrpafter-work

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

Sen et al. (2023) compare mean-variance, hierarchical risk parity, and deep Q-learning portfolios on NIFTY50 and Indian sector stocks. Plain-language intro. Not investment advice.

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.

pythonpaperganbacktestafter-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.

← Previous

Page 7 of 13