STORM: A Bidirectional Spatiotemporal Factor Model Achieving Sharpe Ratio >1

STORM introduces a bidirectional VQ‑VAE‑based spatiotemporal factor model that extracts fine‑grained time‑series and cross‑sectional features, uses discrete codebooks for orthogonal, diverse factor embeddings, and outperforms nine baselines on portfolio management and algorithmic trading tasks, delivering Sharpe ratios exceeding 1.

Algorithmic TradingPortfolio ManagementQuantitative Finance

0 likes · 17 min read

STORM: A Bidirectional Spatiotemporal Factor Model Achieving Sharpe Ratio >1

Bighead's Algorithm Notes

Jan 25, 2026 · Artificial Intelligence

FinAgent Orchestration Framework: Shifting from Algorithmic to Agent‑Based Trading

The article presents FinAgent, a multi‑agent orchestration framework that maps traditional algorithmic trading components to autonomous agents, validates it on hourly stock and minute‑level Bitcoin back‑tests, and reports superior risk control, auditability, and scalability compared with standard benchmarks.

Algorithmic TradingFinAgentFinancial AI

0 likes · 15 min read

FinAgent Orchestration Framework: Shifting from Algorithmic to Agent‑Based Trading

Bighead's Algorithm Notes

Sep 3, 2025 · Artificial Intelligence

Decoding TINs: Reconstructing Classic Technical Analysis with Neural Networks

The paper introduces Technical Indicator Networks (TINs), a framework that maps traditional technical analysis formulas to neural‑network topologies, initializes weights to preserve indicator behavior, and uses reinforcement learning for dynamic optimization, achieving significantly higher Sharpe, Sortino, and cumulative returns on US30 component stocks than conventional MACD approaches.

Algorithmic TradingFinancial AITechnical Indicator Networks

0 likes · 9 min read

Decoding TINs: Reconstructing Classic Technical Analysis with Neural Networks

Bighead's Algorithm Notes

Feb 15, 2025 · Artificial Intelligence

FinRL‑DeepSeek: How Integrating DeepSeek with RL Improves Portfolio Returns (Code Open‑Source)

This article reviews a new risk‑sensitive trading agent that combines reinforcement learning with large language models to extract stock recommendations and news‑based risk scores, describes the extended CVaR‑PPO algorithm, presents extensive experiments on the FNSPID dataset, and discusses the resulting performance gains and future work.

Algorithmic TradingCVaRDeepSeek

0 likes · 10 min read

FinRL‑DeepSeek: How Integrating DeepSeek with RL Improves Portfolio Returns (Code Open‑Source)

Python Crawling & Data Mining

Jun 7, 2021 · Artificial Intelligence

How to Build and Backtest Low‑Frequency Trading Strategies in Python

This article introduces two low‑frequency Python trading strategies—a grid‑based price‑difference approach and an intraday T‑strategy—explains their implementation on the RiceQuant platform, provides sample code, and presents back‑testing results that demonstrate their performance and practical considerations.

Algorithmic TradingGrid StrategyIntraday T Strategy

0 likes · 10 min read

How to Build and Backtest Low‑Frequency Trading Strategies in Python

MaGe Linux Operations

May 16, 2017 · Fundamentals

Build a Simple Moving‑Average Stock Strategy on Ricequant in Minutes

This step‑by‑step guide shows how to implement, backtest, and run a single‑stock 5‑day versus 30‑day moving‑average trading strategy on the Ricequant platform, covering code setup, cash handling, order execution, and both daily and minute‑level simulations.

Algorithmic TradingPythonQuantitative Trading

0 likes · 10 min read

Build a Simple Moving‑Average Stock Strategy on Ricequant in Minutes