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Tag: python

pythonpapertime-seriescnntransformerafter-work

Introducing Financial Time Series Forecasting using CNN and Transformer for after-work quants

Zeng, Kaur, Siddagangappa, Rahimi, Balch and Veloso (J.P. Morgan AI Research, 2023; arXiv:2304.04912) propose CTTS: min-max scale, 1D CNN (k=16, stride 8) tokens, 4-layer Transformer, MLP softmax over up/down/flat. 1-min S&P 500 2019; 80 steps → sign at 81. 2-class 56.7% vs DeepAR 51.1%. Plain-language intro. Not investment advice.

python

paper

time-series

autoformer

transformer

after-work

Introducing Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting for after-work quants

Wu, Xu, Wang and Long (NeurIPS 2021) put moving-average decomposition inside the Transformer and replace point-wise attention with FFT Auto-Correlation (O(L log L)). ~38% average MSE drop vs Informer/LogTrans on energy, traffic, FX, weather, ILI. Plain-language intro. Not investment advice.

pythonpapertime-seriesdlineartransformerafter-work

Introducing Are Transformers Effective for Time Series Forecasting? (DLinear / LTSF-Linear) for after-work quants

Zeng, Chen, Zhang and Xu (2022) ask whether Transformers help long-term time-series forecasting. One-layer LTSF-Linear (DLinear / NLinear) beats Informer, Autoformer, FEDformer on nine benchmarks, often by 20–50% MSE. 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

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.

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

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.