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The Growth Stock Investor is a trader/investor who applies a fusion of fundamental, technical, and quantitative strategies for market timing, growth stock selection, and risk management.
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  • Analyzing Market Strength Through Structure
    Lately I have been doing a lot of experimenting with analyzing the market from a breadth like standpoint by looking at the structure of the market. When I say "structure", what I mean is what is making up the market? What kind of stocks are strong and which are weak? How are the high relative strength stocks acting compared to the low relative strength stocks? I have categories stocks in about 20 different ways, each with 10 - 12 attributes, and can "look" or act in about 5-10 different ways. My mind keeps coming up with more, and I research, think, and play with these different ideas, I hope to post them up here. I am going to start with a very simple example of what I am talking about. In the market stocks can up trending up, trending down, or simply range bound. With this simple categorization, there are hundreds of ways to identify a stock as trending or range bound. This is where the fun, creativity and eventually your edge will come into play and benefit you. For purposes of this example lets use the 20 and 50 day moving average along with Kaufman's Efficiency Ratio to help us identify stocks in trends.

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    Here is a pivot table looking at simply up trending stocks and how they are "acting." Baselines can be observed over long periods of time for which to compare these numbers too, but for simplicity, I'll just mention them as we go along. From this table we can see that the number of stock that are strong and in an uptrend are very low. This number often is over 200 in a strong market and can remain there for months. We also see spikes in the number of weak stocks that are still in up trends. On February 2, 2011, the number of stock that weakened while trending up spiked over 150, a very high number for that class of stock (weakening up trending stocks). From there on we have had a range bound weak market. This quick analysis of up trending stocks tells us that the market structure is weak, and the stocks that are in up trends, carrying our indices higher have been weakening and are seeing little signs of strength. Lets look at the weaker stocks that are down trending to see how they are acting.

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    The number of stocks that is week in a downtrend has been growing quite rapidly simultaneously with a very low strength in up trending stocks. This is clearly showing that the market is currently under distribution. When combining this with many other technical tools such as the massive sector rotation I have recently been mentioning, we see the market is quite weak right now. The structure (or components it is made up of) can be broken down to see more clearly what is happening. Once you have the raw data as shown above you can use it to compile "environments." For example, I used some base C# to compile the following environments:

    Get Ready
    : There as been a lack of distribution while accumulation seems to be ramping up. This occurs on short time frame since bottoms are quick. We also look at the volume pouring into the stocks that are strong and their respective price action according to that volume. If it appears to be a bullish environment, then get your watchlist ready.

    Careful
    : This a longer view of a possible topping formation and stocks get weak and distribution dominates the major stocks. As strong stocks weaken and weak stocks break down, this environment tells you to tighten stops and to be careful.

    Now, these are not entries and exits, but simply telling us of environments at hand. The former is something I am working on, however it nowhere near complete as of yet. Currently my main focus is building a platform for extremely accurate and reliable market analysis by analyzing the internal components.

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    It proves somewhat helpful in the recent correction as accumulation did not occur on a large enough scale to turn the markets until the third leg down of the correction, at which point a higher low was formed with an explosive enough thrust to lauch the market (thank you Jackson Hole!).

    I hope I spark some ideas in traders and quants out there!! 
    May 22 6:17 PM | Link | Comment!
  • How I Called the Top in Silver, and You Could Have Too!
    Silver has recently fallen off a cliff, and in it is a great example of how to trade. While not expecting the acceleration of the decline, the decline was foreseeable using basic and easy price/volume relationships. In this, I want to show how to use basic charting for low risk trading. The annotations were made in real-time, and my instrument for trading this move was the ZSL as can be seen in covestor. Silver's recent price action had seen two major acceleration points into a very high volume week as can be seen below.

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    The first acceleration point occurred in late August as SLV made monthly highs and climbed for 8 straight weeks before its first negative week. This was a very strong sign of strength. The holding of its 4 period moving average also showed that this was not market to short. After a brief correction early in the year, the market took off again accelerating even faster. This triple regression trend acceleration as I call it is common in climax patters. There are three different speeds that occur as the trend gets faster. The first acceleration point starts the strength while the second starts the blow-off top.

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    With Silver at all time highs and the media talking about all day, all you had to do was watch the price action. As can be seen on the weekly chart, the time came as volume exploded over 10x the avg volume after already moving up more than 100% in past few months. This type of action after a move already occurred usually signals exhaustion, not strength. Moving to the daily charts we can see a high was formed on 4/25/2011 with almost 5x the average volume. This was the first clue that a top may be forming. However, shorting here is suicide as these markets can continue for days before they drop. Two days later we retested that high and our setup occurred.

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    The "Test of High" point was made on much lower volume than we saw the original high made on a few days earlier (this can be seen on daily and hourly charts). This test immediately failed and came back with much more selling than buying. With a day like the 25th already behind us, and a weekly chart that showed a climactic condition, this is what we were waiting for. I bought ZSL where the red arrow is located expected an 18% drop or so. However sometimes the market gives you much more than you expect. The rules I used to time this trade were very similar to Timothy Ord's rules for buying tests of support and resistance. In his book he goes through detail how watching these levels provides high probability setups with low risk. The best part of the trade was that I was risking a new high in SLV or approximately $0.75 for an 18% move to the downside for a 10:1 trade. With these odds took the trade with a target based on Fibonacci retracements of the accelerated move as well as previous congestion areas as outlined by the orange lines. These areas lined up with the 50 day SMA nicely giving us the green circle for a target. Disclaimer on trade: I am no longer in this trade. I had a tight trailing stop, which hit before my target was reached, due to the volatility of the down move which was completely unexpected to me. For more information on this type of trading, read Timothy Ord's Book: [amazon_link id="047013898X" target="_blank" locale="US" container="" container_class="" ]The Secret Science of Price and Volume: Techniques for Spotting Market Trends, Hot Sectors, and the Best Stocks (Wiley Trading)[/amazon_link]
    May 22 6:16 PM | Link | Comment!
  • Increasing Profits with Position Sizing
    One of the most important aspects of trading is position sizing. How much will I put into this trade? This number can be arrived at a number of ways with different purposes in mind. You can set it up in such a way to:
    • Minimize Drawdown
    • Maximize Return
    • Target Specific Risk
    • Maximize Risk-Adjusted Return
    • etc
    Below I am going to show my simple algorithm for position sizing. I recently started using this about a month ago, and as you can see the volatility in my equity line has decreased (Covestor). My goals were to have a more consistent equity line, removing volatility, minimizing drawdown, and allowing it to take a parabolic nature on an arithmetic scale. In order to lower risk and volatility, I simply adjusted for that in my position sizing.

    Image   Market Strength My first step is a daily market analysis from which I assign a number from 1% to 3%. 2% is a normal market and the rest is quantitatively ranked and weighted from different models. The purpose of this step is from the wisdom best summarized from Mark Minervini, "When the market starts to turn, I just dip my toes in and don't add or increase position sizing until the my positions show a profit." The Market Strength section drives the size of the overall position once it is calculated from other factors. Strat Factor Strategy factor is another position adjuster based off of the risk of the strategy I am buying based off. Some strategies such as BS are very risky in nature, and while the trade has potential for 100%+ gains, it can be volatile in the mean time. This entry is also late and causes some gains to be minimized. For this reason position sizes are lower for the BS trade. This column lowers or raises position sizing based on the specific factor of the strategy itself. R I like to track what "R" my current position is in. From my past trading I know that I need a 1:3 Risk Reward ratio to remain profitable. From here I can watch my positions easily. When This reaches 2 it lights blue, informing me to move my stop to break even, and when it reaches green it tells me that it has reached the minimum objective (3) and allows me to further analyze the position to leave it, take some profits, or possible even just sell the whole thing. ATR Because my main concern is lowering volatility in my equity curve, I use ATR to initially set my position sizing. All position sizing are based of off ATR * ATR Multiple. The ATR is my proxy for the volatility of the position. ATR Multiple and Distance from 10 Week SMA Stocks that are far above the 50 day SMA or 10 Week SMA are inherently riskier in nature. Stocks can only stretch so far from this "heavily watched" moving average. In buying breakouts and growth stocks, some stocks tend to be very high above the 50 day, and this is risk I would like to be adjusted for in my position sizing algorithm. I got this simple idea to adjust for risk in this way from Derek Hernquist. The more distance there is above the 10 Week, the higher the multiple and therefore lower the position sizing used in the eventual outcome. Portfolio Value The last thing that is crucial for position sizing is the size of your portfolio. The original Market Strength rating system is based of off the fact that long-term success tends to come from risking 0.5% to 3% of your portfolio per trade. After being adjusted for various factors, you can see that eventually portfolio risk is smaller than the market strength rating, however, the philosophy is kept in tact. By making it a strict rule that I can NEVER buy more shares than this simple algorithm computes, I will lower risk and keep my portfolio in check, no matter the mania that may ensue in the market. Checkpoints Other things I track are my objectives for certain trading multiples of R as well as the price I must buy below to put the trade on. Knowing my average return and expectancy, I know what my individual position risk must be below in order to initiate a trade, and therefore I can build that as a check in the this size. For more information on expectancy and backing into certain multiples and prices, see this post. Overall the premise is simple, but the effect is powerful. Give some thought to your goals, and what position sizing adjustments may help you reach those goals.
    May 22 6:14 PM | Link | 1 Comment
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