Python

Peter Lynch is a legendary figure in the American mutual fund industry. From 1977 to 1990, he served as the manager of the Fidelity Magellan Fund, during which time the fund’s assets grew from $20 million to $14 billion, achieving an impressive annualized return of 29.2%. He is widely regarded as one of the most successful fund managers in history. Beyond his remarkable performance, Lynch is also known for his accessible investment philosophy, which has inspired countless individual and professional investors. Lynch advocates the principle of “invest in what you know,” believing that individual investors have the advantage of observing the world around them and can often identify promising companies before their fundamentals are reflected in stock prices. He also emphasizes that stock selection should be based on a company’s profitability, growth potential, and financial stability. Using indicators such as the price-to-earnings (P/E) ratio and earnings per share (EPS) growth, Lynch aimed to uncover “undervalued growth stocks.”

Rothman's core investment philosophy is to “buy right and hold tight” or “buy strong and hold long.” He emphasizes selecting financially sound companies with stable and growing earnings, purchasing them at the right price, and holding them patiently for the long term. Without frequent portfolio adjustments, investors can achieve strong long-term returns. This simple yet resolute investment approach reflects Rothman’s practical wisdom and provides a clear, historically validated foundation for the strategy tested in this study.

With the rapid development of the high-tech industry, technology stocks have increasingly become the focus of the market. While these stocks offer significant growth potential, they also come with high volatility and substantial investment risk. Investors seeking high returns may face major losses if they fail to properly assess the associated risks. Therefore, effectively measuring and managing the downside risk of high-tech stocks has become a crucial component of sound investment decision-making.

In the field of investment, the "business cycle" has always been an important reference point. Fluctuations in the overall economy, corporate earnings, and market sentiment all show distinct characteristics during different stages of the cycle. Therefore, being able to grasp the movements of the business cycle can help investors more accurately adjust their asset allocations and gain a relative advantage in the market.

TEJ (Taiwan Economic Journal) is a renowned financial information service platform in Taiwan. It provides a wide range of data and analysis tools covering various aspects such as economics, finance, stock markets, bond markets, and futures. With its extensive database and professional analytical features, the TEJ platform is widely used by financial institutions, research units, and investors to access real-time, accurate market data and conduct in-depth data analysis to support decision-making.

This article will guide you through leveraging the TQuant Lab SuperTrend strategy in combination with the SinoPac API to quickly master the essentials of algorithmic trading. Let’s become pioneers of algorithmic trading in the Taiwan stock market together!

When using TQuant Lab for backtesting and strategy analysis, beginners may encounter various technical challenges and questions. This article provides solutions and tips to help users develop and backtest strategies more smoothly, improving efficiency and accuracy. Hopefully, this information will help resolve common problems faced by newcomers.

How to Gain Insight into Stock Price Movements from Quarterly Reports? This article will use quarterly reports stock selection to filter the top 20% of listed companies in Taiwan by quarterly revenue and implement long positions. Through this revenue-based backtesting approach, we can identify potential strong stocks in the market and verify the effectiveness of this quarterly stock selection strategy with accurate data. It is simple and provides investors with a stable and systematic basis for stock picking, uncovering the hidden signals behind stock price movements.

The SuperTrend Indicator is a technical analysis tool used to identify trends in financial markets. It assists investors in determining relative high and low points during market swings, helping them make buy and sell decisions. However, one drawback of the SuperTrend Indicator is its tendency to be less effective during consolidation phases. To address this, we will use the Average Directional Index (ADX) to optimize the SuperTrend strategy in this article.

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.

Joseph Piotroski's F-score strategy, based on evaluating 9 financial conditions, helps investors gain insights into a company's profitability, safety, and growth potential. This article uses TQuant Lab to construct the F-score strategy, helping investors gain deeper insights into the investment benefits that the F-score can offer.

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.