TEJ API

In TQuant backtesting and live trading, order functions serve as the central link between strategy logic and capital management. Choosing the right order method not only makes the code cleaner and easier to read but also improves the efficiency of risk control and portfolio rebalancing. TQuant provides order functions across three dimensions — share quantity, capital amount, and portfolio weight. For each dimension, there are two variants: a basic order and a target order. In total, this gives us six order placement methods. In the following sections, we will explain the features, parameters, and recommended applications of each.

Discover how Robert Gaddie’s stock-picking method helps uncover hidden small-cap growth gems in Taiwan’s market. This strategy targets under-the-radar companies with earnings momentum and delivers strong backtested returns.

Discover how Peter Lynch’s legendary investment philosophy can be applied to Taiwan’s stock market. This article builds a quantitative GARP strategy using TEJ data to identify undervalued growth stocks—and tests its performance over seven years.

Discover how Derwood Chase’s value-momentum strategy—favoring low P/E stocks with strong price trends—delivers long-term outperformance in Taiwan’s market. Backtest results show strong alpha and lower drawdowns, proving the power of disciplined factor investing.

Rothman's core investment philosophy is to “buy right and hold tight” or “buy strong and hold long.” He emphasizes selecting financially sound companies with stable and growing earnings, purchasing them at the right price, and holding them patiently for the long term. Without frequent portfolio adjustments, investors can achieve strong long-term returns. This simple yet resolute investment approach reflects Rothman’s practical wisdom and provides a clear, historically validated foundation for the strategy tested in this study.

With the rapid development of the high-tech industry, technology stocks have increasingly become the focus of the market. While these stocks offer significant growth potential, they also come with high volatility and substantial investment risk. Investors seeking high returns may face major losses if they fail to properly assess the associated risks. Therefore, effectively measuring and managing the downside risk of high-tech stocks has become a crucial component of sound investment decision-making.

In the field of investing, business cycles have always served as an important reference. Whether it's fluctuations in the macroeconomy or the ups and downs of corporate earnings, these cycles play a crucial role. Charles Brandes, a distinguished disciple of Benjamin Graham, founded Brandes Investment Partners in 1974 and has since grown its assets under management from $130 million to over $75 billion. The firm’s Brandes Global Equity Fund achieved an impressive 20-year annualized return of 17.91%, significantly outperforming the MSCI World Index, and has received Morningstar's five-star rating along with numerous international awards. Another flagship product, the AGF International Value Fund, has also demonstrated outstanding long-term performance. Brandes himself has been repeatedly ranked among the world’s top fund managers.

In the field of investment, the "business cycle" has always been an important reference point. Fluctuations in the overall economy, corporate earnings, and market sentiment all show distinct characteristics during different stages of the cycle. Therefore, being able to grasp the movements of the business cycle can help investors more accurately adjust their asset allocations and gain a relative advantage in the market.

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 […]

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.