Quantitative Analysis

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.

This momentum factor strategy utilizes the concept of momentum factor: "stocks with good past performance are likely to perform well in the future, and vice versa" to simulate backtesting and to verify the profitability of "the strong get stronger, and the weak get weaker" in the Taiwan stock market.

This article selects five critical stock selection criteria from Buffett’s Business Investment Rules. From utilizing TejToolAPI to fetch financial data and screen targets to using TQuant Lab to backtest investment risks and performance, we will explore Buffett’s investment style and appreciate the ability of Business Investment Rules to generate excess returns.

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.

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.

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.

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.

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:

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.

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.

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.

  Highlights: Preface Profit-chasing and risk-averse are the innate naturals of all investors. One way to achieve these goals is to predict the future stock movement. In the past, time series models such as ARIMA and GARCH are widely used to characterize the trajectory of future stock prices. Nowadays, As the boom of artificial intelligence, […]