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.

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Articles and updates from SoloQuant Academy.

pythonpaperrlmarket-makingafter-work

Introducing Market Making with Deep Reinforcement Learning from Limit Order Books for after-work quants

Guo, Lin and Huang (2023) pre-train Attn-LOB on mid-price direction, then PPO/DQN market-make with reservation-price continuous actions and a hybrid PnL/inventory reward on SZSE reconstructed books. Plain-language intro. Not investment advice.

pythonpaperrl

order-book

after-work

Introducing JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading for after-work quants

Frey, Li, Nagy, Sapora, Lu, Zohren, Foerster and Calinescu (2023) implement JAX-LOB: thousands of fixed-size LOBs in parallel on GPU, wrapped in gymnax for recurrent PPO execution. ~7× training vs CPU LOB. Plain-language intro. Not investment advice.

pythonpaperrlddpgafter-work

Introducing Practical Deep Reinforcement Learning Approach for Stock Trading for after-work quants

Liu, Xiong, Zhong, Yang and Walid (2018) train DDPG on DJIA 30 daily prices (2009–2018). State is prices, holdings, cash. Beats min-variance and DJIA on 2016–2018 Sharpe in-sample-style test. Plain-language intro. Not investment advice.

pythonpapertime-seriestransformerafter-work

Introducing A Time Series is Worth 64 Words: Long-term Forecasting with Transformers (PatchTST) for after-work quants

Nie, Nguyen, Sinthong and Kalagnanam (2022) treat each univariate channel as patches (ViT-style) sharing one Transformer. Beats FEDformer/Autoformer/Informer and often DLinear on ETT/Traffic/Electricity/Weather. Plain-language intro. Not investment advice.

pythonpaperrlmodel-basedafter-work

Introducing Model-based Reinforcement Learning for Predictions and Control for Limit Order Books for after-work quants

Wei, Wang, Mangu and Decker (2019) train a CNN autoencoder, RNN-MDN transition and reward head on HKEX LOB+prints, then PG/A2C/Double DQN entirely inside the world model. Policy transfers back to replay. Plain-language intro. Not investment advice.

pythonpaperrlmarket-makingafter-work

Introducing Market Making with Deep Reinforcement Learning from Limit Order Books for after-work quants

pythonpaperrlorder-bookafter-work

Introducing JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading for after-work quants

pythonpaperrlfinrlafter-work

Introducing FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance for after-work quants

Liu et al. (2020) introduce FinRL: layered gym stock envs, fine-tuned DQN/DDPG/PPO/SAC/A2C/TD3, and Jupyter demos for one name, many names, and allocation. Plain-language intro. Not investment advice.

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.

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.

pythonpaperrlfinrlafter-work

Introducing FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance for after-work quants

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