TradAO: A Visual Analytics System for Trading Algorithm Optimization

Tsang, Ka Wing; Li, Haotian; Lam, Fuk Ming; Mu, Yifan; Wang, Yong; Qu, Huamin

doi:10.48550/arxiv.2008.11319

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Closer to trading activities, TradAO assists users in exploring the whole optimization process of a trading algorithm and evaluating its detailed performances. 7 Besides financial analysis, trading, and fraud detection, there are emerging visualizations for cryptocurrencies. For example, BitVis enables users to analyze the behavior of their bitcoin accounts.…”

Section: Financial Data Visualizationmentioning

confidence: 99%

HFTViz: Visualization for the exploration of high frequency trading data

Yaali

Grégoire

Hurtut

2021

Information Visualization

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High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.

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Section: Financial Data Visualizationmentioning

confidence: 99%

HFTViz: Visualization for the exploration of high frequency trading data

Yaali

Grégoire

Hurtut

2021

Information Visualization

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iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

Yue

Wang

et al. 2021

Preprint

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The model-based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand-picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of "good" factors and propose effective visualization designs to illustrate the collective performance of financial factors, stock portfolios, and their interactions. We evaluate iQUANT through a formal user study, two case studies, and expert interviews, using a real stock market dataset consisting of 3000 stocks × 6000 days × 56 factors.

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TradAO: A Visual Analytics System for Trading Algorithm Optimization

Cited by 2 publications

References 33 publications

HFTViz: Visualization for the exploration of high frequency trading data

HFTViz: Visualization for the exploration of high frequency trading data

iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

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