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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
TQuant Lab Aroon Up Down Trading Strategy
Aroon Indicator, developed by Tushar Chande in 1995, is typically for measuring market tendency. It consists of two lines - Aroon Up and Aroon Down.
Quant Data Science
When TEJ API Database Meets Up STREAMLIT Grid Trading App
In previous tutorials, we learned how to create our own STREAMLIT App. For more details, you can refer to this article. In this article, we will use the TEJ API database to connect with the STREAMLIT package and implement a grid trading strategy. We will use tools such as date selection, dropdown menus, and numerical selectors to interact with charts and tables, making the data an interactive app. Grid trading is a trading strategy that selects a range by setting two parameters, the upper bound and the lower bound. We divide the stock price into grid intervals, buying stocks when the price falls and touches the lower grid, and selling stocks when the price rises and exceeds the upper grid. This strategy is a lazy strategy that doesn’t require much manual operation. It can also profit from price fluctuations. However, there are a few points to note which is the efficiency of capital utilization will be lower than manual trading.
Quant Data Science
TQuant Lab Momentum Trade
In recent years, momentum trading has become a frequent topic of discussion in stock market strategies. In the stock market, we often hear discussions about the price-volume relationship, where price is considered a leading indicator of volume, among other concepts. This article aims to explore the back-testing effects of increasing trading volume as an entry strategy.
Quant Data Science
TQuant Lab Price Deviation Ratio Trading Strategy
The Price Deviation Ratio is a common technical indicator that compares the current stock price to the N-day moving average price, reflecting whether the current price is relatively high or low compared to its historical values. Generally, when the stock price consistently exceeds the moving average price, it’s called a ‘positive deviation.’ Conversely, it’s called’ negative deviation’ when it consistently falls below the moving average price.’ Therefore, when positive or negative deviation expands, it is interpreted as a sustained overbought or oversold condition in the market, serving as a basis for entry and exit decisions. However, using only the Price Deviation Ratio can generate too many trading signals. Hence, we include the highest and lowest prices over the past N days as a second filter. The actual strategy is as follows:
Quant Data Science
TQuant Lab Bollinger Bands Trading Strategy
The Bollinger Bands is a technical indicator invented by John Bollinger in the 1980s. It combines the concepts of moving averages and statistical standard deviation to construct a trading strategy based on statistical analysis. This article will demonstrate how to deploy this strategy on the TQuant Lab back testing platform.
Quant Data Science
TQuant Lab MACD Trading Strategy
MACD, which stands for Moving Average Convergence Divergence, is a commonly used tool in technical analysis for measuring the trend changes and momentum of an asset.
Quant Data Science
TQuant Lab Rookie Manual
TQuant Lab offers a robust quantitative back-testing system with high precision performance and risk calculations, top-quality data sources, and a highly realistic simulated trading environment. It aids users in swiftly deploying a wide range of trading strategies. Feel free to click into the article to learn more information.
Quant Data Science
How to avoid common mistakes during trading – Loss Avoidance
“Loss avoidance” is a crucial topic in investing, whether for novice investors or experienced experts. As we pursue investment returns, the risk of losses is ever-present. Therefore, adopting effective loss avoidance strategies is vital to protect our capital and enhance the chances of investment success. In this article, we will use Python and the tejapi to fetch stock price data to examine the differences between implementing loss avoidance and without loss avoidance measures. By understanding and applying loss avoidance, we will be better equipped to protect our investments, reduce potential losses, and enhance long-term returns.
Quant Data Science
Options Pricing with Monte Carlo Simulation
key takeaways! The Reality Gap: Standard theoretical prices often drift away from the market. Our tests prove that reality rarely aligns with static formulas. Find the Truth with Python: Simulate 10,000 paths and use smart fixes to get prices much more accurate than standard formulas. Real Market Backtest at the End: Which code snippets help […]
Quant Data Science
Employee Turnover Rate Prediction
Employee turnover rate refers to the fluctuation in human resources within a company during a specific period due to employee departures and new hires. This metric is a crucial concept for assessing the stability of both the organizational structure and the workforce within a company. A lower turnover rate indicates that there are relatively fewer personnel changes, reflecting stability and continuity within the organization. Conversely, a higher turnover rate may imply organizational issues, job dissatisfaction, or other factors that can have a negative impact on company operations and the work environment. Monitoring employee turnover rates helps companies understand and evaluate the effectiveness of their human resource management strategies. It enables them to take appropriate measures to improve employee retention and satisfaction, ensuring long-term stability and growth for the organization. Predicting turnover rates allows companies to better plan and manage their human resources, reduce costs, increase talent retention, and enhance organizational effectiveness.
Quant Data Science
【Quant】CRR Model
Programming CRR model for calculating options theoretical price. Keyword: CRR model, Options, Call, Put Highlight Preface In our previous article — 【Quant】Black Scholes model and Greeks, we introduce how to program the Black Scholes model. However, Black Scholes has its disadvantages and can not calculate the theoretical price for American options. Therefore, three years after […]
Quant Data Science
【Quant】Black Scholes model and Greeks
In 1997, Robert Merton and Myron Scholes won the Nobel Prize in Economics for their Black-Scholes options pricing formula, beating out many other contenders. The Black-Scholes model is still a widely-used option pricing model in the financial industry and by investors due to its excellent mathematical properties, simplicity, and ease of use. Today, we will focus on programming this model and Greeks derived from Black Scholes model.