TimelySetup

There is a reason why this blog is called “TimelySetup” which comes from the words “Setup” and “timely”.

Setup is a set of favorable conditions which tilt the statistical probability of a particular trade to go in our favor.  Strictly speaking, a trade should not be entered without appearance of a particular setup. Beneath the surface, reliable setup exists if it captures repeated behavior of certain group of market participants, e.g. how large players execute big positions, how mutual fund managers react to earnings result, or how short-sellers need to cover their positions on certain occasion. Unfortunately, we never know the motivation behind trades. However, each distinct repeatable behavior creates distinct repeatable foot-prints which become the basis of our implementation of a setup.

There are many kind of setup implementations freely available in public domain. Swing traders often rely on  range expansion setup with the expectation that price will continue moving in the direction of range expansion. Pairs traders rely on deviation convergence setup with the expectation that deviation in prices of similar stocks is temporary. Darvas has a well defined setup to capture continuation of growth stocks after pullback. These setups are often implemented as a set of rules to enter a position.

Exploiting repeatable behavior of certain market players will tilt the statistical probability of a trade going in our favor. However, we must recognize that we never know whether the behavior which our setup is trying to catch is actually there. The set of rules which define our setup implementation may identify something wrongly.

There are market phase where a particular setup is likely to work best and market phase where a particular setup is not likely to work. It happens because the targeted behavior of the market players varies depending on market phase. For instance, mutual fund managers will only accumulate stocks when there are money coming to them to manage. If beneath the surface, the setup is exploiting the behavior of mutual fund managers in accumulating stocks, then this setup will work well when it is indeed the season for mutual fund managers to accumulate stocks.

That’s why awareness of market phase which are best aligned to a particular setup is very important. That’s why we execute only a timely setup.

Let’s take a simple range expansion setup which is most relevant to swing trader. This setup, based on daily data, identifies stocks which after consolidation and volatility compression suddenly shows expansion in range accompanied by high volume. We study the Long side of range expansion setup.

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One of the key idea which I have come to appreciate more and more is the wealth of information which can be gained when the market correct itself.

Market corrections is a given. To move higher, market needs to correct itself, presumably to shake out short-term traders who mostly lose money over the long run.

There are at least two reasons why we should look forward to market corrections:

1. it is the best time to identify stocks being accumulated and thus likely to outperform when the market moves higher. The assumption is that those who know the company really well use market correction as opportunity to accumulate shares cheaply.
2. it is the best time to gauge the strength of the market. If the market wants to move higher, market correction is usually reversed relatively quickly. Otherwise, selling begets more selling.

You can expand on these two simple concepts to create a powerful stock selection strategy as well as an early market timing system.

This is an article about a tough and highly competitive business of intraday trading.

Most traders might hope to make money consistently in the market by exploiting players with longer investment horizon (by front running them) or those with about the same investment horizon (by, for the lack of better words, trapping them). It is more difficult to make money out of players with shorter investment horizon.

At any point in time, we want to initiate a trade in the direction of least resistance.

This direction of least resistance depends on what kind of players are currently active in the market. In this case, for simplicity, I divide market players into three categories:

• Longer term players who need to be in the market today to load or unload their stock holdings or put on a hedge. Let’s call them LT players. No mater what, LT players need to accomplish whatever he plan to do during the day.
• Intraday players who hope to earn a living through riding intraday swings in the market. Let’s call them ST players. ST players closely watch market fluctuations, make some judgement about where the market is going in the short term, put on a trade and decide how to limit their risks.
• Market maker who takes any order imbalances from the two groups described above, sell at the Ask and buy at the Bid while continuously trying to neutralize its exposure by adjusting their limit order books and prices.

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One of the unexpected benefit for me in writing this blog is the chance to know incredible and generous people who happened to share some interest in my writings and analysis. Every now and then, I received email from generous readers who shared their knowledge or proprietary data which I would not otherwise get. One of this generous gentlemen shared his passion about Python and its possible application to help automate trading decisions.

As you might already aware, the market analysis in this blog is purely data driven and it uses logical rules to decide whether to go Long, Short, or stay Neutral. I personally use R, an open source programming language, to perform data processing to generate the market timing output. R is a high level programming language, thus it is relatively easy to master. Its strength lies in its time series and statistical packages developed by many experts in scientific community. In addition, like Matlab, it can handle matrix very well. Because of its high level nature, programming in R is like writing in English. For research purposes, this is a good property because we can test ideas quickly. It is also a full-scale programming language, thus you can implement anything imaginable. Once an idea is found to be solid, we can then move on to implement it in a platform which is good at speed.

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Perhaps because of my research background, I am comfortable with reading academic research paper. One such paper is Market Cycles and the Performance of Relative-Strength Strategies after it was recently reviewed by Cxoadvisory. Among others, the paper investigates whether stock level relative strength (momentum) strategies and industry level momentum strategies are dependent on market cycles.

Reading such paper by academic, I usually just quickly scanned through the paper without going into detail (unless something caught my attention) and go straight to the results:  figures and tables. Fortunately, academic usually put a self-explanatory graphs and tables. If the explanation is not sufficient, then I  will go back to the main report to search for more explanation. Usually, by reading the tables and graphs, you can relate with the conclusion drawn. Sometimes, you can infer your own other conclusion not mentioned in the paper.

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Perhaps one of the most stressful and difficult challenge for momentum trader is to identify whether counter trend move against us represent a change of trend or just a pullback move which stop all newbies out of the market before the trend continues its march.

Fortunately, we have market breadth statistics to help us do just that in the stock market. The premise is that market trend requires participation of all common stocks. Market trend cannot be sustained by trending move in just a handful of highly capitalized stocks.

In a bull market, money flows to the stock market as risk appetite grows and ultimately reach lower quality stocks. When risk appetite is not that great, only the best quality of them will continue to rise.

Near the end of a bear market, less and less stock participate in its march down. Higher quality stocks which were sold during the panic will be accumulated by those in the know. As a result, participation to the downside diminish. Before the market return to bull market, though, participation from lower quality stocks is needed before a bull market can be sustained. Often, this start with bang! There will be repeated days with high number of stocks being accumulated without high number of stocks being distributed in between.

Now assume there is a  tendency for the market to move in a manner necessary to frustrate the majority of players. Short lived counter trend move is one such way for the market to frustrate the majority of us because it will turn our profitable position quickly into a loss and leave us behind before the market move in our favor again — but without us. Nothing is more frustrating than this. Money will flow from the accounts of these frustrated newbies to those few with more experiences.

Now, what is the most cost effective way for a counter trend rally in the market indexes to happen? Yes, move the few stocks with the most influence in the indexes. These are usually highly capitalized and liquid stocks. Being liquid, the transaction cost of moving them is minimal. Being highly capitalized, its move significantly affect the index. This operation cannot be done in less liquid small cap stocks. This is the key to the success of market breadth statistics in separating real trend change versus counter trend move in market indexes.

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Every once and a while, I sit back, relax, and think about *what is the market* to me and how to understand it better. Below is one of my thought about a subject as complex as the Market. What is the Market to you?

The market is like the invisible version of our natural environment. Many creatures live in the market assuming, often unconsciously, specific roles (species) in the “food-supply-chain” of the market: from global macro trader, liquidity provider, long term fundamental investor, value trader, momentum trader, arbitrageur, scalper, noise trader, etc. We are at once the predator of certain species and the prey for other species.

When certain imbalance in the environment occur, either triggered internally or externally, certain species are supplied more abundantly by mother nature. When this season come, the predator which most efficiently capture the bonanza will prosper. What these minority predator posses are tools to identify the arrival of the season, tools to capture the prey, and the wisdom to use these tools to position themselves close to the prey and capture them.

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