Tejapi Data Analysis

In financial markets, technical analysis has long been a crucial tool for investors to assess market trends and develop trading strategies. Among these techniques, the Ichimoku Kinko Hyo (Ichimoku Cloud) utilizes five moving averages and a cloud structure to analyze stock support, resistance, and trend changes, enabling traders to quickly identify bullish or bearish signals. However, traditional Ichimoku strategies rely on fixed parameters (9-26-52) and visual interpretation, making them inflexible in adapting to different market conditions. Additionally, in choppy market conditions, Ichimoku-based signals can generate false positives, leading to erroneous trades and capital drawdowns. With the advancement of machine learning, XGBoost (Extreme Gradient Boosting) has become one of the most widely used models in quantitative trading. By leveraging Gradient Boosting Decision Trees (GBDT), XGBoost learns complex high-dimensional relationships between different data points and enhances the filtering and decision-making process of trading signals. Compared to purely relying on technical indicators, XGBoost can integrate various market variables—such as price momentum, volume changes, and market sentiment—to uncover relationships between data and future stock returns. This ultimately improves the accuracy and robustness of trading strategies.

what is the right way to invest? If you’ve had the same doubts, you’re not alone. While seeking answers, I stumbled upon a term that was entirely new to me: quantitative finance. Learning this field helps resolve personal financial challenges and enhances career competitiveness. More importantly, it sharpens your logical thinking and data analysis skills—benefits that are hard to overlook. TQuant Lab provides everything you need to get started efficiently and effectively. Let’s embark on this journey together and explore the endless possibilities of quantitative trading!

No matter which brokerage app you use, you can view the historical trading information and real-time market data of various securities on the app. This allows investors to make informed investment decisions based on accurate information. This is possible because the Stock API facilitates smooth data transmission between the market trading system and the brokerage app. In this article, we will introduce what a Stock API is and highlight the three most commonly used Taiwan Stock Market APIs, providing valuable references for investors.

This article will introduce algorithmic trading with Python, the benefits of using the Python programming language for financial trading, and how to apply it to financial transactions, allowing investors to harness the power of technology to build their ideal investment portfolios.

In the first article—Verifying LSTM Stock Price Prediction Effectiveness Using TQuant Lab (Part 1)—we compared the predicted data with the actual data to conduct an initial evaluation of the performance of two trained models (for stocks 2618 and 8615). The results were promising. For a more detailed analysis, you can click the link above to learn more, as we will not go into further details here due to space constraints.

If you’ve been involved in investing for some time, you’ve likely heard a lot about algorithmic trading and quantitative trading strategies. For beginners with no software or coding background, it might seem daunting at first. However, with the rise of AI in recent years, the ability to automate investment strategies could become an essential tool […]

In recent times, there has been a global surge in AI, with Taiwan's electronics industry attracting a lot of interest from institutional investors due to its comprehensive supply chain. In addition to trading stocks through conventional methods on stock exchange markets, investors can also utilize block trading offered by exchanges for transactions exceeding a fixed amount.

This article will utilize the Stock Exchange's provided 7 financial information indicators and explore their correlation with stock price counter-indicators using the TEJ API.

Data analytics in the stock market plays a vital role in understanding market dynamics, making informed decisions, and optimizing investment strategies. This process involves deep analysis of extensive datasets to extract market trends, actionable insights, and predictive indicators. It's the key to forecasting market movements, identifying investment opportunities, and managing risks with a data-driven approach.

In the Taiwan stock market, retail investors account for the highest proportion and have the highest trading volume in the market. This article uses factor analysis to delve into the relationship between retail investor trading behavior and stock expected returns.

By analyzing the motives behind insider block trades, we aim to explore the relationship between stock prices and insider transfers.

This article explores how the combination of LLM and TEJAPI enhances the efficiency and precision of stock market analysis. It elucidates how this integration contributes to identifying market trends, analyzing stock performance, discovering key information in market news, and providing third-party investment insights. This synergy not only aids professional traders and investors but also offers ordinary investors more ways to grasp the dynamics of the market.