Michael's blog

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As is known to all, diversification is the key to managing the risk of your investment. However, it’ll take a lot of effort and time to hand-pick quality assets that have little correlation with each other. In the Hybrid Asset Allocation trading strategy, Wouter J. Keller and Jan Willem Keuning actually brought up an easier way to introduce diversification into the trading strategy. In this article, we’re going to first talk about HAA strategy and see how it boosts the diversification of our portfolio. Then, we’re also going to backtest the HAA (Hybrid Asset Allocation) trading strategy and several of its variations against the benchmark.

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The best ideas are often inspired by or adapted from the work of others. Likewise, profitable quantitative trading strategies are not necessarily original, but they can be generated by adding personal insights regarding the market or strategy itself. In this post, I’m going to introduce the process that I usually do when discovering a prospering trading strategy.

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In this article, we will discuss how sentiment analysis impacts the financial market, the basics of NLP(Natural Language Processing), and showcase how to process the financial headlines by batch to generate an indicator of the market sentiment.

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Backtrader is a well-known Python open-source library to backtest your quantitative trading strategy. Most of its components can support trading against one single trading target. To step up the game to trade against multiple stocks, there are a few things that need to be fine-tuned to make sure the trading strategy would trade as you expected. In this post, I’m going to share my experience and crucial tips with you as a starting point to build your own.

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In the last post, we learned the basics of performing the pair trading strategy and using cointegration as a method to identify the potential tradable stocks pair. All the theories and the math formulas are so seemingly promising and convincing enough for us to believe it’s a profitable and stable trading strategy. But is it? In order to test and check the profitability and effectiveness of this strategy, we need to backtest this trading strategy to simulate real-world scenarios.

Cointegration is a statistical technique to find out whether a time series closely follows the movement of the other time series. Therefore, it becomes an important technique in the pair trading strategy for us to determine the right stock pair to trade with. In this post, we’re going to see why traders prefer using the cointegration test over the correlation test in pair trading, and whether the cointegration test results can boost our trading performance.

In the last post in this series, we’re going to look at some questions that I discovered while working on connecting to Interactive Broker API. Some of them are due to the obscurity of the configuration and hard to find the right place to configure them, and some of them would need the extra tool to resolve. I put all of them down into one post and share it with you.

Now we come to the third part of this series. In this post, I’m going to show you how I design and build my local database to store IBKR trades and other necessary information for generating meaningful indicators to review our strategy performance.

This is the second part of the Set Up Trading API Template In Python. We’re going to focus on implementing the rest of the functions in our Interactive Broker class.

Building your trading strategy to connect to a broker with the broker’s proprietary API is always dreadful. There are tones of API documentation to read, tones of trial-and-error tests to conduct, and tones of unknown causes and bugs that fail your API test. In this post, I’m going to demonstrate my MVP API template to get my trading strategies to work, so that you can build your own in a way that makes your trading strategies work as well.