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: python

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.

pythonpapervarrisk

after-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.

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.

pythonpapergenetic-algorithmforexafter-work

Introducing GA-MSSR for RoboTrading for after-work quants

Zhang & Khushi (2020) evolve FX technical-rule parameters with a fitness that mixes Sharpe and Sterling ratios to penalise variance and drawdowns. Lab study. Not investment advice.

pythonpaperdeeplobquantileafter-work

Introducing Extending Deep Learning Models for Limit Order Books to Quantile Regression for after-work quants

Zhang, Zohren & Roberts (2019) extend DeepLOB with quantile regression: CNN+LSTM branches predict long/short return quantiles from LOBs, then combine them. Plain-language intro. Not investment advice.

pythonpapervolatilitylstmafter-work

Introducing GARCH-Informed Neural Networks for Volatility for after-work quants

Xu, Liechty, Benthall, Skar-Gislinge & McComb (2024) train an LSTM with a loss that mixes true variance and a GARCH teacher—PINN logic for vol. Not investment advice.

pythonpaperensembleportfolioafter-work

Introducing Expert Aggregation for Financial Forecasting for after-work quants

Remlinger, Alasseur, Brière & Mikael (2021) mix thirteen Gu–Kelly–Xiu-style experts with Bernstein Online Aggregation so long-short books adapt when models rotate. Not investment advice.

← Previous

Page 20 of 33