Insights

The Zipline engine, integrated within TQuant Lab, offers a high-quality and realistic backtesting framework. It leverages four core functions—initialize、handle_data、analyze、run_algorithm——to construct a comprehensive simulation environment. These components enable trading strategies to dynamically adjust to market conditions, incorporating elements such as slippage and transaction costs to ensure that backtest results closely reflect real-world performance. This article begins with a brief overview of the four key components and their applications. It then focuses in detail on the initialize function, exploring its specific configurations and role within the backtesting process.

Preface In financial investment, mastering accurate and comprehensive data is an indispensable skill, and effectively managing stock pools and acquiring stock price data is the key to unlocking this skill. With the tools and APIs provided by TQuant Lab, we can easily define screening criteria, quickly establish stock pools that meet specific requirements, and retrieve […]

Since the launch of ChatGPT, the world has begun to witness the transformative power of AI. While much of the spotlight remains on ChatGPT, a Chinese AI model has quietly emerged on the global stage—DeepSeek. It boasts a training cost of  just 1/20 of OpenAI’s and 1/10 of Meta’s. Recently, DeepSeek has even received recognition from tech giants including Apple’s Tim Cook, NVIDIA’s Jensen Huang, Microsoft’s Satya Nadella, Amazon’s Andy Jassy, Alphabet’s Sundar Pichai, and Meta’s Mark Zuckerberg. This article will explore the impact of "DeepSeek" on Taiwan's electronics industry.

Taiwan’s stock market is a shallow dish market. A shallow dish means it has no depth and is susceptible to external shocks. In contrast, the stock market is easily disturbed by external forces. It has significant ups and downs, such as being influenced by news or large purchases of a single stock by large investors, which can significantly affect stock price performance. This article uses several chip data to explore the impact on stock prices when chips are concentrated in stock and presents them in an interactive chart.

Traditional Ichimoku strategies rely on fixed parameters (9-26-52) and visual interpretation, making them inflexible in adapting to different market conditions. XGBoost learns complex high-dimensional relationships between different data points and enhances the filtering and decision-making process of trading signals. This article use XGBoost to enhance investment performance of Ichimoku Cloud.

In the world of investing, accurate predictions and systematic analysis are key to achieving long-term success. Strategists who can decipher market trends become sought-after figures for investors eager to learn. Michael Sivy is undoubtedly one of the best. With precise market forecasts and unique investment insights, he has built a stellar reputation in the U.S. investment community. From warning of the 1987 stock market crash to predicting a market turnaround in 1995 and foreseeing the bursting of the tech bubble in 1999, Sivy has demonstrated exceptional market foresight through decades of research and hands-on experience. His book, The Rule of 72, has become a must-read classic, offering systematic analysis and clear guidelines for investors of all types.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.