market data

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.

Track QFII 'smart money' footprints in Taiwan large-cap stocks! Learn how the Foreign-Institutional Trading Concentration (conc_qfii) factor predicts returns.

Taiwan daily corporate announcements full-text data provides daily Material Information directly from Taiwan’s official Market Observation Post System (MOPS). This dataset provides full-text corporate announcements from all TWSE and TPEx-listed companies, paired with a bilingual (Chinese/English) mapping of regulatory paragraph categories. By offering a clean, reliable, and standardized data feed, it enables institutional investors to build automated data pipelines and effortlessly stay on top of the latest corporate developments in Taiwan.

Share Distribution data is a dataset published by the Taiwan Depository & Clearing Corporation that classifies investors into 15 groups based on the number of shares held. This information reveals the shareholding ratios and number of shareholders for each group to observe investor behavior. By analyzing these distributions, the data can be transformed into quantitative factors to predict the relationship between investor trading behavior and expected stock returns.

Discover TEJ’s highlights from Neudata Hong Kong Data Summit. Explore our structured Earnings Call Transcripts, 11 categories of PIT Factor Library, and high-fidelity Market Data for Asia.

Burton G. Malkiel is the Chemical Bank Chairman’s Professor of Economics at Princeton University. He previously worked in the investment banking division of Smith Barney & Co. and has served as a director of several large investment institutions, including The Vanguard Group and The Prudential Insurance Company of America. He was also appointed as a member of the U.S. President’s Council of Economic Advisers. In both academic and investment circles, he is a highly respected and influential figure.

Market makers, equipped with high-frequency trading capabilities, institutional-grade cost structures, and real-time creation/redemption privileges, are the primary participants in ETF premium–discount arbitrage. In contrast, Non-Institutional Participants face multiple constraints—including information latency and higher transaction frictions—which make it difficult to capture arbitrage opportunities promptly or profitably. 

LazyBear is a highly influential indicator developer on the internationally renowned trading platform, TradingView. He has created a large number of popular custom technical indicators, and his open-source code has inspired countless quantitative traders and technical analysis enthusiasts around the world. LazyBear's indicators often focus on reducing the lag of traditional indicators and incorporate unique market observations to better capture trends and momentum. One of his representative works, the "Impulse MACD," is adopted here. This indicator is not a traditional Moving Average Convergence Divergence (MACD), but rather a significantly improved version. It uses a zero-lag Double Exponential Moving Average (DEMA) to respond more quickly to price changes and combines a smoothed high-low price channel (SMMA) to determine market "impulse." The core idea is that trading signals are more valuable only when price momentum aligns with the trend direction. This helps to filter out some of the noise typically found in ranging markets.

The concept of the Moving Average (MA) originates from the Dow Theory developed in the early 20th century. Dow Theory emphasizes that markets exhibit trends, and such trends can be observed through price movements themselves. From the Simple Moving Average (SMA) and Exponential Moving Average (EMA) to more sophisticated variants such as the Double Exponential Moving Average (DEMA) and Hull Moving Average (HMA), all these improvements aim to address the inherent lag problem of traditional moving averages, allowing them to reflect price trends more quickly or more smoothly.

In TQuant backtesting and live trading, order functions serve as the central link between strategy logic and capital management. Choosing the right order method not only makes the code cleaner and easier to read but also improves the efficiency of risk control and portfolio rebalancing. TQuant provides order functions across three dimensions — share quantity, capital amount, and portfolio weight. For each dimension, there are two variants: a basic order and a target order. In total, this gives us six order placement methods. In the following sections, we will explain the features, parameters, and recommended applications of each.

In financial markets, Contrary Thinking is a timeless strategic wisdom. It stems from a simple yet profound observation: when most people are overly optimistic, the market is often overheated; when the crowd falls into fear, it may actually present a buying opportunity. However, contrarian investing has long remained at the level of a proverb, lacking concrete and quantifiable standards of action, making it difficult to implement in practice.

Gabriel Watson  is a well-known American growth-momentum portfolio manager. In his early career, he worked at Morgan Stanley Investment Management and William O’Neil & Co., where he accumulated extensive market research experience. Since joining Black Rose Capital Management in 1998, Watson has broken away from the confines of traditional value investing and developed a systematic, rule-based stock-picking method centered on “revenue momentum and price strength.” He calls this framework “The Machine.” In an environment characterized by rapid information flow and swift capital rotation, this approach has enabled him to capture the powerful upward moves of leading stocks.