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Bighead's Algorithm Notes
Bighead's Algorithm Notes
Apr 6, 2026 · Artificial Intelligence

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 TradingTransformerVQ-VAE
0 likes · 17 min read
STORM: A Bidirectional Spatiotemporal Factor Model Achieving Sharpe Ratio >1
Bighead's Algorithm Notes
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 TradingFinAgentagent-based trading
0 likes · 15 min read
FinAgent Orchestration Framework: Shifting from Algorithmic to Agent‑Based Trading
Bighead's Algorithm Notes
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 TradingTechnical Indicator Networksdeep learning
0 likes · 9 min read
Decoding TINs: Reconstructing Classic Technical Analysis with Neural Networks
Bighead's Algorithm Notes
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
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