Value investing is an investing strategy that has been popular for decades. The main idea involves picking quality stocks among distressed companies, buying and holding them for the long-term (over several years), and expecting the good quality stocks to remain good and rebound from the valley. In short, value investing is to buy good companies at a good price. In this post, we’re going to go through the framework that some of the value investors use to evaluate the company value.
We talked about how to use MACD indicator and other secondary indicators to tell when to long certain stocks in the previous articles. If you haven’t read them yet, below are the links to catch up on where we left. But, is this the end of the optimization of our trading strategy? In this article, we’re going to demonstrate the power of RSI indicators and see how this indicator can be a help to our current trading strategy.
After reading the post How to Improve Investment Portfolio with Rebalancing Strategy in Python by Bee Guan Teo, I was thrilled to know that this trading strategy can be that powerful and the portfolio return is greater than any of my existing trading strategies. Therefore I decided to give it a try and backtest this strategy to verify the profitability it claimed.
Previous reading:
In the previous post 【Pair Trading】In-depth analysis of 5 distance approach strategies in pair trading, we have conducted various research against each variation of distance approaches in pair trading strategy. Despite the research has given us probable insights whether there is an $\alpha$ exists in the strategy we constructed, we still haven’t established our portfolio construction strategy. Here we introduce a concept called the “long-short equity (LSE)” strategy.
Therefore, the objectives of this post are consists of three:
| Previous readings |
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| 【Pair Trading】Introduction to pair trading strategy |
After knowing what the pair trading strategy is about from the previous post, we’re going to use the QuantConnect platform to research and validate each variation of the distance method. The agenda of this post would be:
Let’s get it started!
Pair trading is just like a man leashing a dog. They never apart too far away.
New Column started! In this new column, we’re going to start by introducing the idea and principle behind this famous strategy. In the later posts, we’ll do research using different famous methods in pair trading. Hopefully, we’ll cover the fundamental knowledge of pair trading as much as possible.
Pairs trading is among the most popular trading strategies in many markets. This particular strategy involves simultaneous taking two correlated assets in different directions, using one asset in the pair to hedge the risk of the other one. Essentially, it is a market-neutral strategy.
In the post How to save your silver bullets with MACD strategy?, we have backtested the strategy of MACD and the other six different momentum indicators. In the end, the combination of MACD and the awesome oscillator is the better and seemingly profitable one among all six combinations. But, we won’t stop right there. In order to raise our portfolio win rate and return, we will discuss a few variants of MACD and Awesome Oscillator to derive different buy and sell signals.
Backtesting is the most important stage of testing and verifying the result of your trading strategy. You can either build your own backtesting script or use a python package such as backtrader to simulate your portfolio return with downloaded stock prices CSV files. Either way would require you to download the stock price in minute or day bar beforehand. Also, you need to deal with the stock market events such as stock splitting, ticker name changing, or delisting. So effectively using an existing online tool like QuantConnect could save you a lot of time dealing with edge cases yourself.
I’ve been spending my time learning how to work with QuantConnect platform and its features in the past month. So in this post, I’m going to introduce the web-based backtest platform by finding out the common things between Quantopian and QuantConnect. Then I’ll talk about the good and bad I found in trying out these two platforms. Hopefully, the experience that I shared in this post can better help people who are looking for a solution of validating their trading ideas.
In the post 【Machine Learning】 Part II - How to build a machine learning boilerplate?, we have successfully built our machine learning boilerplate. By having this template, we can develop an advanced machine learning trading strategy upon. However, even with the strategy result that looks profoundly profitable, we still won’t be able to know how much money we can make by looking at the accuracy rate of our machine learning trading algorithm.
In order to better understand whether the results from the output of our model are really concerning our portfolio return, I put together a rather simple strategy and run several backtests with different parameters. In the end, we’re going to answer several frequently asked questions in order to decrypt the myths of machine learning trading algorithms.
To follow up on what we have learned in the last post, here we’re going to quickly demonstrate how to build your own machine learning boilerplate. We’re going to cover the steps that we introduced in the post:
