Insights
In-depth research and data-driven insights on quantitative finance, factor investing, risk, and ESG from the TEJ research team.
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Fundamental Factor Research: Monthly Revenue Information – part1
The Taiwan equity market possesses a rare institutional advantage globally: under the Securities and Exchange Act, listed companies are required to announce and report their operational results for the preceding month by the 10th of each month (Exception: starting from FY2026, insurance companies and entities with insurance subsidiaries may extend their disclosure deadline to the 15th of each month). This is commonly referred to in the market as "Monthly Revenue".
Factor Strategy – Integrating Broker Consensus to Enhance Foreign Concentration Strategies – QFII Part 2
Boost your quantitative strategy with QFII concentration & broker consensus! Discover how the conc_qfii fusion strategy delivers a 30.12% annualized return in the Taiwan large-cap market.
Factor Research – Tracking Smart Money Footprints via Foreign Institutional Concentration – QFII Part 1
Track QFII ‘smart money’ footprints in Taiwan large-cap stocks! Learn how the Foreign-Institutional Trading Concentration (conc_qfii) factor predicts returns.
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Quant Data Science
How to Use Python for Algorithmic Trading? A Guide to Understanding the Benefits and Operation of Algorithmic Trading
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.
Quant Data Science
Verifying LSTM Stock Price Prediction Effectiveness Using TQuant Lab (Part 2)
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.
Quant Data Science
Verifying LSTM Stock Price Prediction Effectiveness Using TQuant Lab (Part 1)
This article uses the LSTM time series model for deep learning-based LSTM stock price prediction, utilizing the opening, high, low, and closing prices of the past five days, quarterly ROE, MOM (indicating the magnitude of price trend changes, and the direction of market trends), and RSI indicators to predict the next day’s closing price.
Quant Data Science
Tutorial on Running TQuant Lab in Google Colab and Common Errors
For students who want to use TQuant Lab, in addition to the original GitHub installation tutorial, we now offer a faster and simpler way to use it directly on Google Colab. The tutorial on running TQuant Lab in Google Colab eliminates the need to set up a virtual environment, significantly lowering the barrier to entry.
Market Knowledge & Data Guides
Counter-Indicator Analysis: Using TEJ API to Examine the Relationship Between Stock Prices and Counter-Indicators Issued By Authority”
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.
Quant Data Science
TQuant Lab Williams %R, looking for stock price turning points
This time, the Williams %R strategy was used for backtesting. The Williams indicator is also called the Williams index, wmsr, or W%R. Its English name is The Williams Percent Range. It was created by the famous American trader Larry Williams in 1973. It is a standard indicator in technical analysis. One. The KD line and other indicators commonly used by investors to judge overbought or oversold are developed based on the William indicator.
Quant Data Science
TQuant Lab Ichimoku Kinko Hyo Strategy, A Self-contained Technical Analysis Indicator
The Ichimoku Kinko Hyo strategy employed in this simulation utilizes the concept of the three-line conversion in the Ichimoku Cloud chart for backtesting, coupled with trailing stop-loss testing for profitability. Under the pen name Ichimoku Sanjin (いちもくさんじん / Ichimoku Sanjin), Goichi Hosoda authored seven series of works detailing the philosophy behind this indicator and its trading system.
Quant Data Science
TQuant Lab KD Indicator Strategy: Exploring Stock Price Reversal Timing?
The KD Indicator is a practical and widely used tool in technical analysis. It’s primarily used to determine the short-term strength of stock prices and potential reversal timing.
Quant Data Science
TQuant Lab RSI Moving Average Strategy – Identifying Reversals in Oversold Conditions
Creating a convergence strategy with the RSI moving average strategy. The RSI is an oscillating technical indicator representing the comparative strength between buyers and sellers in the market.
Quant Data Science
TQuant Lab Loss Aversion Strategy — Average True Range
The Average True Range (ATR) is designed to assess the extent of price fluctuations within a specific period. ATR is commonly utilized as a tool in technical analysis, assisting traders to determine entry points, exit points, and stop-loss levels for loss aversion purpose.
Quant Data Science
Is There an Election Market Trend ? Research on Presidential Elections Using TEJ API
During the election period, the internet is full of election-related news; some even make relevant expectations on the stock market, affecting investor’s sentiment. However, does the so-called “election market trend” really exist? In this article, we will conduct a quantitative analysis on Taiwan stock index in previous presidential elections using TEJ API.
Quant Data Science
Implementation of Deviation Rate Trading Strategy using TEJAPI and LLM
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.