Tejapi Quant

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.

Since the establishment in April 1990, TEJ (Taiwan Economic Journal) has devoted itself to building financial databases, focusing on providing basic information for investors in the security market. After 30 years of hard efforts, TEJ has now become one of the most representative financial database providers for Taiwan. Most of all, instead of sticking to the status quo, TEJ aligns with the current trends, and has successfully transformed into a financial full service consulting company. 

The article delves into the performance of momentum strategy under different take-profit and stop-loss conditions, analyzing how to balance risk and reward in a highly volatile market using actual backtesting data.

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 […]

During Jensen Huang's speech at National Taiwan University, he revealed a list of NVIDIA's business partners in Taiwan, which led to the viral spread of the term "the world's strongest AI ETF". What impact did this announcement have on the stocks of these business partners? We will use TEJAPI to calculate the Sharpe ratio to determine whether the effect was positive or negative. Additionally, we will visualize the observed results.

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.

2024 International Conference of Taiwan Finance Association (TFA) is held at National Kaohsiung University of Science and Technology (NKUST) on June 7 and 8. The conference theme this year is “Reflection on FinTech Innovation: Opportunities and Risks”, where TEJ is invited to lecture on "ESG Index – Strategy Construction and Backtesting”.

In the realm of financial analysis, understanding the sources of investment information is paramount. Discover where creditors and investors obtain crucial data to make informed decisions about a company. Explore the significant role of investment information sources in quantitative investment analysis, shaping the landscape of informed financial decisions.

Unlock business success with accurate and comprehensive financial data – the lifeblood of strategic decisions and market understanding. Master the art of financial data sourcing for successful analysis. Navigate the dynamic market with astute judgment to ensure relevant insights for effective financial modeling.