Beginners Guide

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.

“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.

Look-ahead bias is the phenomenon that unconsciously uses unavailable or unrevealed data in analyzing or simulating historical events. It exists in the processes of making decisions or evaluations which use information or data that was unknown at that time.  Look-ahead bias may cause distortion and misleadingness of the result because it violates the principle of using only information available during analysis. It could emerge in any field, such as finance, economy, and data analysis, and influence investment strategy, backtesting of the trading system, and performance grading.

  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, […]

Principal Component Analysis (PCA) is a key technique in unsupervised learning widely used in machine learning and statistics to analyze data and reduce data dimensionality. Its core idea is to break down the original data into representative principal components, achieving dimensionality reduction and providing a new description of the data.

The alpha obtained from the Fama&French three-factor model is used to construct a long-short strategy and backtest the performance against the market return. Highlights： Difficulty：★★☆☆☆ The Fama&French three-factor model is used to calculate the alpha of Taiwan-listed stocks, and the top 20% of stocks with the highest alpha and the bottom 20% of stocks with […]

Demonstrate how to get data from TEJ API financial database Preface Financial data is the information firms disclose each season, or financial indicators based on that information. Common financial data mainly comes from balance sheets, income statements or cash flow statements. Besides, TEJ API financial database also includes more comprehensive information such as financial ratios, related […]

Building the financial dashboard through python. ( the top 5 buy list of the foreign & other investors, and combine the trading volume of foreign investors with the stock price.) Preface We have introduced some functions of the most-used python packages such as matplotlib, numpy, and pandas in previous articles. In this episode, we will further […]