Tejapi Application

In the field of growth investing, Derwood S. Chase Jr. is undeniably a legendary and representative figure. In 1958, he founded Chase Investment Counsel Corporation, a firm focused on managing corporate pension funds, trust funds, and individual retirement plans, deliberately avoiding any banking-related business. From 1990 to 2001, its institutional large-cap growth portfolio achieved an impressive annualized return of 16.38%. Through a strategy that combined growth and stability, Chase established a long-term return model that could endure across market cycles, providing a benchmark for investors navigating volatile markets.

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.

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 […]

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.

Over the past two decades, trend-following has been a successful trading strategy used by many fund managers, professional traders, and global macro hedge funds to profit in global futures markets. Recently, the application of trend following strategies to stocks has gained significant attention, with a surge in published research and articles. Increasingly, literature suggests that the trend effects observed in futures markets can similarly apply to stock markets.

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.

We use Python to backtest Kenneth L. Fisher’s growth stocks strategy on the Taiwan stock market. Keyword： Kenneth L. Fisher、Growth Stocks、Strategy Backtest What is Kenneth Fisher’s Growth Stocks Strategy? Kenneth L. Fisher, founder and president of Fisher Investments, and his father Philip A. Fisher, a representative of Qualitative Investment in the United States. Warren Buffett […]

Using the number of margin trading and short selling and volume to establish the herding indicators then analyze by regression model.  Keyword：Herding Indicators、Margin Trading、Application  What are Herding indicators?  The emergence of behavioral finance has challenged the traditional view in investment theory that individuals make rational investment decisions. The “herd behavior” represents the tendency of investors […]