Backtesting唔係咁易架葉師傅 | 香港程式交易研究中心 | 歐陽一心 | 蔡嘉民 | 程式交易 | Program Trading

Summary

This video from the Hong Kong Program Trading Research Center emphasizes the importance of backtesting in investment strategies. It outlines three main challenges: programming architecture, data management, and result interpretation. The video explains that while backtesting is crucial, it's often underestimated due to its complexity. It also touches on the value of data, comparing it to oil, and stresses that backtesting is both a science and an art, requiring a deep understanding of the market and data to be effective.

Takeaways

• 😀 Backtesting is crucial for investors but often lacks detailed explanations in many channels.
• 🛠️ The main challenges in backtesting are programming architecture, data, and interpreting results, each posing significant difficulties.
• 💼 Mastery in any of these areas can lead to a full-time job in a large hedge fund, highlighting the expertise required.
• 💻 The complexity of programming architecture includes managing different trading logics, data sources, and asset characteristics.
• 📊 Visualization tools are necessary for quickly understanding backtesting results amidst a vast number of parameter combinations.
• 📈 Data is a significant topic in backtesting, with sourcing and processing being time-consuming and requiring careful handling.
• 💡 The saying 'Data is the new oil' emphasizes the value of data in financial markets, where having quality data can be a competitive advantage.
• ⏰ Timezone and data release timing are critical aspects of data that must be correctly managed to avoid biases like forward-looking bias.
• 📉 Result interpretation in backtesting is complex and subjective, requiring a deep understanding beyond just programming skills.
• 🎨 Backtesting is described as 'more art than science', indicating that it involves a significant amount of judgment and intuition.
• 💡 For those without programming skills, understanding the intricacies of backtesting is essential to effectively communicate and manage programmers.

Q & A

What are the three main difficulties in backtesting according to the speaker?

The speaker identifies the three main difficulties in backtesting as program architecture, data, and interpreting results.

Why is program architecture a challenging aspect of backtesting?

Program architecture is challenging because it involves managing different trading logics, data sources, and asset characteristics, as well as creating a backtesting engine that can handle various financial instruments and visualize results effectively.

What does the speaker mean by differentiating between 'data management' and 'core backtesting engine' in the context of backtesting?

The speaker emphasizes that data management and the core backtesting engine are separate components of backtesting. Data management involves organizing and preparing data for backtesting, while the core engine is where the actual testing of trading strategies takes place.

Why is data considered a significant topic in backtesting?

Data is a significant topic because the quality and accuracy of data directly impact the backtesting results. The speaker mentions that data sources, formats, and processing can be time-consuming and require careful handling to avoid errors like forward-looking bias.

What is the importance of understanding data time zones and adjustments in backtesting?

Understanding data time zones and adjustments is crucial to ensure that the backtesting does not inadvertently use future data, which would lead to incorrect conclusions and violate the principle of backtesting.

Why does the speaker compare data to 'the new oil'?

The speaker compares data to 'the new oil' to highlight its value and importance as a resource in the financial world, similar to how oil is a valuable commodity.

What is the role of visualization tools in backtesting?

Visualization tools play a role in backtesting by helping to quickly and effectively understand the results of numerous backtesting scenarios, making it easier to analyze and compare different strategies.

How does the speaker view the process of interpreting backtesting results?

The speaker views the interpretation of backtesting results as an art rather than a purely scientific process, emphasizing that there are no definitive answers and that judgment and understanding of the market are required.

What is the significance of the Sharpe Ratio and Maximum Drawdown (MDD) in backtesting?

The Sharpe Ratio and Maximum Drawdown (MDD) are significant metrics in backtesting as they provide insights into the risk-adjusted return and the worst-case scenario of a trading strategy, respectively.

Why does the speaker suggest that backtesting is 'more art than science'?

The speaker suggests that backtesting is 'more art than science' because it involves subjective judgment and interpretation of results, which cannot be fully quantified or standardized, despite the quantitative nature of the process.

What are the potential issues with relying on free data sources for backtesting?

Free data sources may have limitations in terms of variety, format, time span, and delivery methods, which can affect the quality and reliability of backtesting results.