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SoloQuant Academy

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

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Articles and updates from SoloQuant Academy.

pythonpaperrlfinrlafter-work

Introducing FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance for after-work quants

Liu, Yang, Gao and Wang (2021, ICAIF) present FinRL as a full-stack pipeline: gym + live APIs, plug-in SB3/RLlib/ElegantRL, train-test-trade, ensemble and crypto notebooks. Plain-language intro. Not investment advice.

pythonpaperrl

dqn

after-work

Introducing An intelligent financial portfolio trading strategy using deep Q-learning for after-work quants

Park, Sim and Choi (2019) train a DQN on discrete buy/hold/sell lots with a mapping for infeasible trades. LSTM encoder + all-feasible-action replay. Plain-language intro. Not investment advice.

pythonpaperrlfinrlafter-work

Introducing FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning for after-work quants

Liu et al. (2022) ship FinRL-Meta: DataOps from vendor dumps to gym-style markets, paper reproductions, and a curriculum of notebooks. Plain-language intro. Not investment advice.

pythonpaperrlfuturesafter-work

Introducing Deep Reinforcement Learning for Trading for after-work quants

Zhang, Zohren and Roberts (2019) map TSMOM features to positions with DQN, PG and A2C, plus volatility scaling, on 50 liquid futures 2011–2019. Plain-language intro. Not investment advice.

pythonpaperrldqnafter-work

Introducing An intelligent financial portfolio trading strategy using deep Q-learning for after-work quants

Park, Sim and Choi (2019) train a DQN on discrete buy/hold/sell lots with a mapping for infeasible trades. LSTM encoder + all-feasible-action replay. Plain-language intro. 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) map portfolio weights with EIIE nets, a Portfolio-Vector Memory for costs, and an explicit log-return reward on Poloniex 30-minute crypto. The FinRL-era template. Plain-language intro. Not investment advice.

pythonpapergarchvolatilityafter-work

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

Wei, Yang & Cui (2025) embed GARCH(1,1) inside GRU and LSTM cells so ω, α, β stay economically readable while the net learns nonlinear vol. Beats pipeline hybrids and Transformers on US indices. Plain-language intro. 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. The GARCH workhorse with high-frequency data. 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 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 Knowing Factors or Factor Loadings, or Neither? for after-work quants

Dai, Lu & Xiu (2019) compare TSR, CSR and PCA large-covariance estimators on noisy asynchronous high-frequency data—known factors, known loadings, or neither. Plain-language intro. Not investment advice.

pythonpaperhrpreinforcement-learningafter-work

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

Sen et al. (2023) compare mean–variance, López de Prado HRP, and deep Q-learning portfolios on NIFTY50 and Indian sector baskets. Plain-language intro. Not investment advice.

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