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Trade Review


My entries were around the lower range of the day for both of these trades.

My exit for CAAS was made lower. BCLI met the target today so I’ll look for a failure to make new highs or take out a candle low or I may exit on a close below the target.

caas bcli

Maybe a reentry in CAAS?

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Trading System: Cliff Notes


1)Own no more than 20 *active* option positions

2)Own no more than 40 option positions total.

3)1% position size per option with 5 possible exceptions.

4)Those 5 exceptions may all be 2%. max 2 may be 3% and max 1 may be 4% positions.

5)Target max 50% of stock positions ~5 positions of 10% or less.

6)10% income position that is always on except sold to avoid margin.

7)Remaining capital with 1-5% in asset allocation options. (commodities, currency/cash, stocks/short VXX, bonds/income) plus possible 2% hedge.

8)Only take a trade where reward is 3 times the risk or more.

9)Monitor breadth to add more when it’s oversold or when it makes a strong breadth thrust off of oversold, add normally when it’s not overbought, and add proportional to rate of selling or slower when it’s overbought or trending down from overbought (until oversold signal).

*A position that falls 75% below it’s original value you basically write off as a loss.


-Watchlist is developed through OABOT’s top 400 and manually filtered from there to usually 20-60 names.

-Import watchlist into a spreadsheet with suggested stops and targets and current price

-Reward/risk will automatically update once it’s in the watchlist.

-Trading rules for entry that must follow the above portfolio rules and also an entry checklist which should also be using triggers.


1)As stocks are bought, input the stop, target, entry price into spreadsheet.

2)Since many of you are already trading, don’t worry about importing a large list of current holdings… just update the next trade until you phase out the old trades.

3)Stock must be below stop 5-10 minutes before trading close to trigger a sell.

4)Stock above target has a separate rules of waiting for the candle to close below prior candle low or failing to close above prior candle high Sell before the following candle’s close.

5)Which timeframe you use when stock is above target depends on condition but in general: With investments you may use monthly or weekly chart. With stock trades and options that have more than a week remaining til expiry you use daily chart. on options expiry week you might use a 1hr or 4hr chart. 1 day before options expiry you may use a 30 minute chart. On options expiry day you may use a 5m chart.

6)Generally update and check spreadsheet every hour. Also monitor watchlist stocks for purchase. On options expiry day check them every 10-15 minutes. One trick with that is if the stock is higher than the last time you checked, you don’t have to look at a chart.


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Backtesting Patterns And Triggers

Some people like to short breakdowns of bearish patterns or “shorting into the hole”. Let me show you why that has been a terrible idea at least during a bull market.

The following is the results of simply buying the breakdown and holding for 3 months. No stop. No targets. No complicated additional signals. Virtually every bearish “breakdown” reverses so hard it ends up positive and many of them actually beat the market. You could have bought the broadening top breakdown or a bearish pennant breakdown or a diamond top or double top breakdown and end up beating the market.

contrarian buys

These results of course are volatile and ever changing, so don’t expect the future to necessarily resemble the past. Still it’s a good reminder of why fighting bull markets by “shorting into the hole” is probably a terrible idea.

Certainly you might argue that you’ll be in and out much sooner than 3 months, but had I put another criteria on it I’m sure the results would also have been bad for the short. Maybe you’ll find the one exit strategy that works for this strategy, but I’m not going to bet on it.

Let’s look at some bullish patterns of buying after the stock breaks out.(buying late)


Only falling wedges (which has been an awesome pattern) beat buying the top 3 traditionally bearish patterns after the breakdown.  Some of them didn’t even beat the dow. Buying falling wedges BEFORE they breakout is probably better still.

I don’t know if anything is necessarily actionable here in terms of adjusting your strategy because there was a time when diamond bottoms were one of the top patterns and falling wedges were only slightly above average at least if you bought from the breakout. There also was a time when inverse H+S worked brilliantly and times when it didn’t work very well.

But there is a lesson here.

Be early or don’t bother. (Generally).

Also, since the strategy is to be early, we could work to try to fine tune what exactly triggers an entrypoint and how we prioritize which stocks to enter in a watchlist. We can look at setup quality to some extent but that’s a little subjective and might only help us narrow down a large list to a smaller one.

So we can use backtesting for looking at “triggers”. If you can find a simple signal to trigger a trade within the context of a developing pattern and backtest it, it should beat the market if it’s a better signal than just “randomly” buying.

The bullish hammer candlestick pattern:


And the RSI(5) crossing below 20 (buying oversold).

The RSI particularly is not a good signal to use on it’s own even though the results are good. This only backtested S&P stocks which reduces the risk of holding a stock going down to zero as a removal from the S&P would constitute a sell. There’s really no risk management if you’re buying something oversold without likely selling it more oversold. However, if you buy oversold with a developing pattern providing a clear support area, you can manage the stock by selling a failure to hold support and that actually probably makes some sense since there’s still a range of buyers now selling. Also, if we are using OTM options to buy we have a built in risk management mechanism of the full premium so we still have exposure after the pattern breaks down and it still can reverse.

A candlestick pattern can be used on its own in some regards since only holding it for 5 days limits the probability of it going down too far and has a proportional chance at an equal upside. Also, if you had to you could sell on a close below the candlestick pattern low itself or find a candlestick near support of other candles and sell on a close below one of those. Nevertheless, the only time I like using candlestick patterns on their own for a signal is as a hedge. It can be hard to find a bearish pattern worth trading during a bull market unless you have a process to quickly identify one that will at least underperform and reduce risk exposure, if not actually decline in a bull market.

There are other worthwhile triggers from your watchlist to activate a trade.

The purpose of a trigger should be one of the following (or more)

1)To increase chances of the trade working in your favor (the signal should have better than 50% chance of an equal move to the upside as to the downside).

2)To increase the risk/reward (the entry should be lower or closer to support).

3)To decrease the time waiting for the trade to work (The entry should be closer to the breakout point).

4)To narrow down a group of stocks to the one that is most likely to provide the best risk adjusted return.

Aside from RSI oversold or a hammer candlestick there are a few more triggers:
-Buy at support or near support or even below support intraday and exit on a close below support (better R/R)
-Pattern within pattern setup. (momentum of intraday pattern may trigger the actual pattern you’re trading to break out… plus this generally means greater consolidation and greater volatility/range expansion usually results)
-Breakout of pattern within pattern (Rather than buying before the intraday pattern goes you wait until it starts to move and this way you may get it closer to the regular pattern’s breakout point)
-Break above prior day low in bottom half of pattern (usually a move above the prior candle leads to some sort of short term price movement that may trigger a breakout)
-Tight multiday range (volatility compression leads to volatility expansion A.K.A. breakouts)
-Near Apex of pattern (shortens your holding period waiting for the pattern to develop and break)

If you are still having trouble deciding which stock to add from a watchlist, there are other factors you may consider:
-Picking the stock from your watchlist with the highest short interest
-Picking the highest reward/risk
-Looking at the underlying options and finding the option with the best reward/risk

-Using the best reward/risk and calculating the amount you have to pay to get that reward/risk for every remaining stock and putting a good till canceled limit order there and then just watching to make sure the pattern is still in tact and canceling the trade if it breaks.

Not all of these ideas are easily testable with the available backtesting tools, but you should at least have a process that clearly defines or provides rough guidelines for a method to decide which trade to enter, how many trades you can enter with the same expiry, how much max option exposure (current portfolio), max exposure (by initial purchase), max number of purchases in a single day/week/month… and a checklist to go through in the morning before trading and before placing each trade to help you navigate these decisions.

I’m still working on defining this, and I’ve been trading for >10 years so it isn’t a must… but I’m pretty sure I’d have better results if I was more organized and had a more precise process… At a minimum it’d bother me less if I missed a buy… because this way it won’t be because of lack of organization, but instead just because of the way I chose to define the system.

update: Here’s an outline of a trading system I’m working on to more clearly define decisions and to eliminate uncertainty with regards to decisions.

Generally you should aim for the best R/R on the trade you make itself if any one trade is clearly above the others… outside of that.

Here’s a possible priority list:

1)RSI (5) combined with intraday RSI oversold (1m,5m, or 30m) while in the lower half of the pattern and near support.

2)RSI(5) combined with hammer candlestick at support (rejecting breakdown of support)

3)Hammer candlestick at support

4)At pattern support independent of any other signal.

5)RSI (5)

6)Tight day range

7)Hammer candlestick somewhere near support

8)Pattern within pattern

9)Close above prior day

10)Breakout of pattern within pattern

11)Buy the failure of the pattern (breakdown) and wait longer.

12)Breakout of actual pattern but within 3% of breakout point and 10% of low.

I still kind of think that calculating the cost of an option at support at the current day and prioritizing by the risk/reward at that price (and then canceling the remaining orders if you hit your maximum) might be the best approach… but with multiple strike prices and expiry cycles and different targets depending on expiry cycles that can be a little challenging too.

Even having this priority list isn’t really enough to tell you how patient to be waiting for a priority 1 vs whether or not you should just take any one of these triggers. It can tell you if you have 5 stocks you like and only want to buy 2 how to decide which ones to choose, but it is only a small part of the trading system.

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Neo Enters The Matrix

I’m going to continue doing some work to improve the OABOT 2.0 a.k.a. “Neo”. Neo has been removed from the matrix of the market data and now must begin to be trained “how to see the code”. To start with Neo needs to develop how to discern reality from illusion which means being able to remove the setups that get him in trouble and add skills like a Kung fu master so that he can identify when to strike.


The next optimization step is ATR divided by beta. Beta is a measurement using I believe a year’s worth of data to measure average movement. ATR is a measurement of average price movement in dollars over the last 14 days. ATR divided by price gives you an average percentage move. When ATR is small relative to beta, in theory it suggests a more recent consolidation and range in the last 14 days than is typical. This is common among consolidation patterns.

In theory this should give you stocks moving less in the most recent 14 days than in the prior year.


So I made major changes to the scoring. What I noticed was the setups that I was getting tended to be a little bit longer developing.

The best patterns began to show up as I got after around 300 names. This suggests the punishment knocked off a few good names and the reward also rewarded too many bad setups. In other words, compared to before, the score is too high and not as important as some of the other variables. However, it may also be that the score is important when ATR/price divided by beta is less than 3% but much less important when it’s less than 2.5% and 2%. In fact, it’s even possible that taking an extreme reading like less than 0.50% and punishing stocks for being in that tight of range will eliminate many of the merger/aqcuisition types where the stock has a ceiling equal to the buyout offer and thus it doesn’t hardly move at all over 14 days. This can be tested later. For now I have to get through the main scoring and worry about that later.

Next test 1/6th the size 100/-33. I definitely thought the results improves from the 600/-200 but the original 12/-4 was still better. Now is something like 12/-4 optimal or is something like 20/-6 better? Or perhaps 4/-1 is more optimal. For now I’ll just return it to 12/-4 and leave a note to test 20/-6 and 4/-1 to try to improve it later. The effort to try to optimize this seems less worth the effort than to optimize other criteria so that’s all the work I need to do for now.

Now we have ATR vs monthly volatility. ATR measures average move over 14 day. Monthly volatility measures 30 day volatility. If 14 day “volatility” (in the form of average daily move during that timeframe) is less than 30 day volatility it should represent consolidation if they are calculated in similar ways. This is similar to the first test we did where we wanted weekly volatility (7 day) less than ATR/price (14 day) except now it’s 14 day less than 30 day and represents a little bit longer time horizon of consolidation. I think this is one we’ll want to switch to a larger score than we did last time. Prior to changing it this was the settings.


Last time with the 7 day / 14 day I may have started with too small of score even though I thought the score was pretty massive. It would have been better if I started with 1000/-750 just so I could rule out a higher score then I’d have a better sense of direction. Instead I went with 200/-150 and now I have to test 400/-300 and 100/-75 instead of just one additional test.

So I’m going to start this one off with 1000/-333. There certainly were some good setups. But also some not so good. In fact, some of the highest scores were filled with false positives of the merger/acquisition variety. That stuck out to me which led to changes I make that I describe later. But there were still some decent setups mixed in the top 50. That probably means that if ATR/Price is less than something like .25 or even .125 or lower I could actually substantially punish all stocks and probably eliminate a huge amount of false positives. After those the setups started to pop up. When I searched the top 300-400 range I couldn’t really see as many quality setups as in the 200-300 range but because the score was so large as was the punishment I decided to look at the 800-1000 ranked stock range and the setups improved. As predicted the score and/or punishment is too high. Since I started with such a large number directionally it is clear I have to reduce this number. I went with 800/-200 instead of 800/-266 so I reduced the punishment proportional to the reward. I’m pretty confident the punishment for not meeting these criteria was a little too high because of the quality of setups in the 800-1000 range. I’m also going to change the score for the less than .25 to -10,000 and punishment to zero so that it is obvious which ones were filtered out and I can quickly look at them. Then I can adjust this number to .125 or .33 depending on whether or not it filtered out too many good stocks or not enough merger-acquisition stocks.  These were the only 12 stocks that the score filtered out. Looks like there’s a pretty low chance I’d be missing out on anything as a result of this score. negative

I changed the criteria to punish any stock less than .46 the volatility and I still didn’t really miss out on any and filtered out over 2 dozen. Occasionally I may miss a name, but it isn’t very likely. A stock scoring less than .50 was another requirement, there won’t be hardly any stocks between .46 and .50 and if I change it to something like .7 then there isn’t much room between .7 and .75 so I have to really change all of the points of measurement.

I also decided to test all stocks between .45 and .65 to see how many stocks are worth giving a good score in that range or if I should make additional adjustments. They weren’t necessarily all that great, filled with utilities and defensive names but also had a couple good setups. I decreased the amount of score that I gave those between .45 and .65 and eventually moved it up to .70.

I decided the same problem probably existed for the weekly volatility and it did so I had to remove those less than .4 it removed a few non merger+Acquisitions at .4 but mostly bond funds and defensive funds so I wasn’t bothered by it.

Because of the changes I made, I had to sort of slide all of the scores on both metrics. It currently looks like this but isn’t really necessarily anything close to optimal just yet, it’s just much better than before. As such these numbers are likely to change soon but for now I need to take a break.

nowI’m probably going to have to increase the first criteria’s scoring and decrease the second until I’m satisfied as I think the recent week having less consolidation than the I also may decide to play with the sliders and the exact multipliers of volatility.

It seems that the best stocks may actually be in a range of volatility of not too quiet and not too volatile, but generally less is better. So I’ll have to figure out where that range is to continue to make improvements from here.

I was also going to test weekly change divided by beta, but since ATR divided by beta wasn’t super helpful I probably don’t need to change this one too much so for now I’m going to skip it and circle back to it.

Everything remaining that I want to test in the intermediate term rank is related to the moving averages relative to other volatility metrics. If a stock has departed significantly from the moving average then it represents a larger move away from a range, the goal will be to find the levels at which we can remove stocks as going to far and what comparative metrics helps with this. Perhaps a stock with smaller change relative to the 20 day moving average than the monthly volatility or smaller daily or weekly change relative to the 20 day will capture volatility contraction. It’s really not clear. A 20 day moving average can also use as a reference point to manage risk with a stop on a cross under the average if the stock is above it, so a stock being close to the 20 day may represent a good entry.

I will probably test the long term consolidation (monthly to quarterly change and beta) rank before the short term.

It is unfortunate that there isn’t a “quarterly volatility” or “yearly volatility” or more yearly based volatility metrics aside from beta and even 5 year and 10 year volatility metrics. But for now that’s the limitations I am working with. If the 20 day moving average works well, maybe I’ll use the 50 day and 20 day vs the 50 day and such.

What’s left to do?

1)Adjusting the moving average based rankings

2)Adjusting the long term consolidation rank

3)Adjusting the weightings for the “total consolidation rank” (which combines short term, long term and intermediate term)

4)Possibly adding 50 day moving average into long term consolidation rank.

5)Cycling through a couple more times to fine tune the score.

6)Possibly making more significant changes to eliminate sort of the rising wedge patterns or reclassifying them so that when I sort through stocks I can eliminate them if I’d like.

6)Considering categorizing the stocks based on setup by using the stock’s proximity to highs/lows and behavior and making unique scores in the changes over the week/day/month as well as change in trend that signal the pattern or type of setup. Then if a stock meets this criteria, having a separate score just for that “pattern”. This is probably the best way to go. I sort of did something similar the first time I did OABOT but I’ll probably just copy and paste most of the old method rather than start over.

7)Probably setting up a better “summary”/”cover” tab. I like stocks that set up together, but if I don’t have any sort of way to differentiate a bullish consolidation pattern vs a bearish one it won’t be as useful.

Right now it looks something like this


It has stocks categorized by market, by sector, by industry and market cap size is next to be added. This is another reason I’d like to see how certain “types” of stocks are doing like “stocks near highs”, “stocks IPOd in the last 2 years”, stocks 5-15% off highs, stocks 15-40% off highs, stocks near lows, etc. That’d probably be a good way to get a feel for the market’s risk appetite, but I can also use a feature that sums up a list of individual stocks and lists the average score.The last cover tab was more about what is “working” now and where money was flowing. This is more about consolidation ranking but I may add other breadth metrics and such on there as well. I’m all ears to new ideas too.

I probably will have a duplicate of my spreadsheet because for the cover tab I need to have very few if any false positives that might skew the average where as a research tool to find specific stocks I have no problem looking at several false positives.

I also have a portion of the main tab setup for entering tickers if you just want to see how a group of say the FANG stocks compares to a hand pick group or market averages, or your own GARP index or whatever. This is how I constructed the S&P, Dow, Russel2000, etc market indicies average scores.


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2014 Goals Streamlining The Process Part 2

The analysis and grading system discussed in part one will look something like this but have more in depth data and calculations and filtering systems along with the ability to categorize based upon the data and pull the information to a coversheet where it will have a summery of the findings that is more clear.

more detailed breakdown and how sub categories will work.

stocks overview

At this point, it is mostly just a concept in my head that I have recently started to get on paper along with a brief draft of one aspect of what it will look like and how it is possible. I don’t even know how far I am going to be able to take this spreadsheet and how much can really be automated, vs how much I will have to manually setup. I have a number of real rough, general pictures in my head of all these spreadsheets and how they will work together so that I just press a few buttons (ideally as few as possible, but as many as necessary for quality results) and get a result, some of which I manually will go into finviz and look over and then look at charts and assess risk/rewards from however many I want, sort those by best available (ideally streaming updates) by expectations per equal unit of risk, and combine them together into the risk simulator to see how the broad strategy will help me meet my goals, so I know how those pieces fit within the broad strategy. With that in mind, the spreadsheet will pull a combination of the possible trades into different categories, make suggestions which I will be able to confirm by adding it to my trading journal for tracking, categorizing and reviewing my results in a way that looks at what I did, what condition the market was in and other variables that I want to be able to track and review over the course of many years to continue to look at areas I need to improve, trades I need to avoid making, trades I should make more of and strategies that could use some tweaking. My trading journal then will be able to adjust to reflect the “best fit” match relative to the target “allocations” and what not, and hopefully account for fees and evaluate whether or not the benefit is worth the costs of “rebalancing” and/or adding new positions and provide a suggestion on position sizing or a look at some simulations of how it would look assuming all opportunities are available and reflect reality.

But to go from conceptual rough draft to an actual concrete set of spreadsheets and what not is a huge leap. One step at a time. The first step will be to really get into the specifics of what I want just one of these spreadsheets to accomplish, and work from there.

Since I have done work on the position sizing/trading system simulator, I have a few adjustments I want to make, likely before year end.

1)Allow the spreadsheet to add in deposits or withdrawals on a per trade basis.

2)Allow the spreadsheet to adjust the “drawdown killswitch” AFTER subtracting the amount added after each trade and adjusting for the drawdown not including deposits.

3)Allow the grand total gain to subtract all capital added and starting amount to get a net gain.

4)Binary Yes/No function if drawdown killswitch is hit so you can track percentage chance that you hit the drawdown killswitch over X trades or less to potentially simulate the percentage of traders over a time frame that meet those results.

5)Consider adding in a “target goal” that functions as a “reverse kill switch” where trading is halted after goal is made

6)Binary Yes/No for “target reached” so you can estimate percentage chance of reaching target in X amount of trades or less given the assumptions you plugged in about expectations of the system(s).

7)Secondary portfolio targets and dynamically adjusted risk – Set it up so IF a particular portfolio target is reached, the risk percentage per trade is then adjusted and/or the amount deposit/withdrawn is adjusted to simulate reaching a goal in which you will attempt to retire from job while managing the sudden need to withdraw from account while being more conservative in your strategy. OR so you can increase the chances of getting to your target so if you get really close you don’t take unnecessary risk to get there at the cost of greater volatility that is not needed if you have traded well

8)Experiment with correlated trades held simultaneously with the same trading system. (the results of one influences the probability of another)

9)If that works, experiment with correlated trades held simultaneously with DIFFERENT expectations (such as a stock trading system combined with an option trading system) with different risk amounts

10)… ideally some sort of adjustment is going to have to be made to allow different average holding periods so the simulation can match up to more accurately reflect the timing of the trades.

11)If you can do 8 and 9, you should be able to set it up for up to 5 simultaneous trades for up to 5 unique “trading systems” simultaneously within portfolio, but may require a lot of busy work.

12)Come up with ideas to test a lot of different assumptions/strategies.

13)Use the spreadsheet to do a lot of testing of those assumptions.

update:You can check out the progress of the OA Bot.

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2014 Goals: Streamlining The Process Part 1

I find goals are far more likely to be successful when you allow time to let the old goals develop beyond what you intended and see where they take you and then make goal setting more of an active process to manage rather than a single event that could otherwise overwhelm. Goal setting around New Year’s day I feel has become more of a marketing ploy and a distraction if anything from what you really want to accomplishment. So what I like to do is let the old play out for awhile. Then I start with a relatively unspecific long term goal that I observe my past works progressing towards. In this case it is “streamlining the investment/trading process”.

It is certainly not the only goal as constant improvement, education, and improving my ability to carry out the strategies more efficiently and effectively are other important trading goals. But by limiting it to just one general concept, I can apply the focus this year towards it. That is a “big picture” idea as the spreadsheet will help me with just one of those elements which is analyzing and managing risks with more precision in what I can expect. From this idea I can begin work on aspects of it, for awhile. Based upon how much I can get done in a sample period, how much time I can spend per week, and how much I have to do… I can then have a goal that reflects reality.

I find that this method of goal setting allows for expedient results.

Setting financial goals is a bit tricky as it involves chance and uncertainty, but setting financial goals is something that can be done as you can see here.

So from that I have a number of ideas as how different elements may coordinate together to help me streamline the process. First I need to sort out some of the ideas of things I want in a perfect world
1)Market analysis, Sector Analysis and Industry Analysis.
2)Using the above, develop a Ranking or Grading Systems of individual stocks based upon UNIQUE classifications on whether or not the market, sector, industry, and classification of stock and market cap size are currently “in phase” currently, “on deck” or “not in phase”, that has DIFFERENT applications/formulas for ranking them according to the particular TYPE/classification of stock it is AND what stage it and the industry/sector is in.
3)A tool that can quickly look at both stock and option pricing and a manual assessment of expectation, probability and timing to compare risk/rewards and analyze the effects of using any one particular option with the others and with the stock and position sizing.
4)An ability to take the inputs and add it to the trading journal spreadsheet where I can track and manage the various allocations by a number of categorical breakdowns if wanted.
5)A more multifaceted trading simulator which considers multiple, simultaneous and overlapping approaches on different timelines and compares strategies of shifting allocation towards each strategy, adjusting risk and adding/reducing capital from your account over time.
6)Ultimately a more flexible, dynamical approach to allocation that not only has the flexibility to adjust to maintain certain general allocations without having to sell individual positions short of their targets to do so, but also adapt in their allocations according to the behavior and numbers and relates expectations from every asset class or strategy allocation to others as well as future opportunity.
7)Summarize all the data in order to factor in everything (fees, risk tolerance, alternative investments, expectation) and quickly convert the data into a recommendation based upon my own inputs to optimize the portfolio.

This will be a process that will take a lot of time in developing, that will likely be an ongoing project over the next few years, so I have to make the spreadsheet flexible enough to be able to change with my strategies and positions and expectations, so very few elements will be “set in stone” and most will be inputs which I can change and very few assumptions will be made that are fixed.

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Going Against The Great

I find myself in an unusual position. For the first time that I know of, I see things from the exact opposite perspective as the Option Addict.. And with conviction. I purchased some LNKD today near the end of the day on a day where option addict was selling his calls. Going forward is my TOP idea right now.

I say this with a tremendous amount of respect. I really do look up to “OA” and 80% of what I do is because of him.  Much of what I do differently than him is still because of “OA”. What I mean by that the approach of slicing up the market in multiple layers and identifying a good risk/reward among other things I got from him. So even if I am using different criteria and specific techniques to analyze are different, a lot of my ability to sort through it all and come up with a highly actionable setup can only be because I learned the majority of the process from him.

Betting against “a great” is not something that I like to do, and betting against the Fly or a tabbed blogger typically will get you nowhere fast. Yet I truly believe I am in a position where I have an edge on this trade. Even if I am wrong, I have learned that sometimes the only way you can learn to develop a strong intuition is going with your gut when your eyes confirm what you feel. I’ve watched great trades pass me by as a result of not trusting my gut but both my gut and eyes have been fine-tuned to being very productive. In this case, BOTH are signalling to me that going forward there is or will be tremendous opportunity in LNKD for at least a setup with profitable expectation.

Here is what I am looking at.

Item 1)The longer term rotation by the big money creates long term trends. Here I am comparing LNKD with another social media giant FB and using the GOOG/BIDU relationship to show how it works. daily and weekly chart. Although there is often an overlap and pretty significant correlation being in the same space, in terms of trends you can tell what’s in favor and how it rotates. You can often anticipate who is the next to lead and which is next to lag.Jeff of course has talked about the risk cycle in a slightly more advanced way for more of a swing trader’s perspective, but I believe in many cases when the stocks are similar enough in size and type of company you can look at a longer term cycle of multiweek/multimonth moves. Eventually based upon this analysis, I believe a multimonth move is coming in LNKD eventually.

The first one to bottom or breakout typically leads with stronger trends upwards. The first one to peak typically begins to consolidate first and the other follows.

leader laggard cycle

To emphasize what I see.

GOOG starts with a stronger trend while bidu consolidates. Bidu then forms a relative low while GOOG is having troubles showing strength and as bidu rips higher IT then begins to catch up aggressively and thus becomes a leader for the time being. THEN the rate of ascent slows as GOOG continues consolidating. Now GOOG makes an initiative move and the rate of ascent favors GOOGLE as a leader confirmed as BIDU is the first to top out which at some point may be good for BIDU as at some point it will consolidate again while GOOG then is late to decline and BIDU will at some point in the future likely take off another leg higher while GOOG continues to consolidate again and the cycle continues.

Now FB/LNKD. LNKD was strong while FB lagged. There are some subtle differences in the relationship as to the nature of how they lead and lag. As FB peaked in feb 2013 suggesting LNKD may follow and eventually it did, but LNKD remained very strong and actually even began the leg up first before FB, actually continuing a second leg of leadership. FB followed late, midway through LNKD’s advance and then continued as LNKD sputtered, then continued again but this time the rate of decline slowed as basically FB consolidated and made it’s next leg up as LNKD continued to tighten much like GOOG did to BIDU as BIDU still advanced, but as soon as GOOG broke out, it’s rate of ascent was much higher.

LNKD initially peaked first, suggesting it would consolidate but on FB’s next major high LNKD actually took out that high on October 21st while FB failed to do so suggesting that a subtle rotation may be beginning. Even though FB defiantly continued another leg higher this time while LNKD continued it’s long consolidation, LNKD continued to set up with a tighter consolidation readying for a multiweek or multimonth advance to take the baton from FB. While FB is still technically leading, it is on it’s 2nd leg higher. The last time LNKD did that it spent a lot of time consolidating.


So right now I see LNKD as having been largely ignored by the big money which create the trends and in my mind, that creates opportunity of greater value for lnkd than it does for FB at SOME point. Based upon technical analysis I think that will cause LNKD to lead SOON, the question is only WHEN.

Now let’s isolate LNKD as a stock and do some more specific technical work with today’s closing chart.

Item 2)Chart 1:


LNKD has about 80% of it’s outstanding shares floating. That means it is heavily traded but not necessarily owned heavily by management types. That means I am more likely to interpret the volume as simply temporary action as opposed to permanent “value” investor positions. Therefore, I think those who bought 150-180 probably took some profits above $220. The aggressive buying everytime you think the stock is breaking down to a lower low tells me they want their shares back badly at lower prices. The smart money to me has been slapping the dumb money around who comes in late and chases the rips higher and panics late lower. Even if that interpretation is incorrect, I think the 150-180 volume will be able to “shoulder” the selling, and possibly even create that one rip higher where the dumb money learns there lesson so they stop chasing higher the next time or two around and suddenly they find themselves missing out and the stock at new highs. At SOME point I think that will happen. Regardless, EVERYTIME you had a new low, you saw an eventual rejection of that low and a very sharp up move from that new low that carried on into the following week.

Item 3)”Bullish Divergence” The RSI and Slow Stochastic have a large degree of overlap which means they essentially are measuring many of the same things (momentum). So I won’t call it a “quadruple divergence. However, on MULTIPLE timeframes (using OA’s triple oversold indicator) the RSI on the daily chart is showing a clear picture of HIGHER relative strength lows even as the stock goes lower. That signals strengthening momentum even as stock is available at lower prices which tends to be bullish.



I believe we are near levels of support however you look at it. If we get a fast down, I think there will be a quick flush , capitulation and a chase higher and the support as we near 180 will be very aggressively ready to buy. Even if we do not, we may still be at the levels where the consolidation pattern is in a state of support and we could still rip higher once more. If we do not flush lower and reverse we still could see that eventually happen and I may be early, but I like my odds here on multiple timeframes for multiple reasons.


HIGH conviction name. I am not saying it might not flush first and reject. Short it if you wish, but to me looking forward there is tremendous potential. I may be far too early in the trade, but nevertheless I like it here even though many will see it as “crazy”.

Finally: For future consideration and what will eventually be described in more details in my 2014 outlook I would like you all to consider that there are generational trends driven by the relationship between bond prices, interest rates and pension funds having to reach certain thresholds of earnings to stay solvent. That these trends are only just starting to potentially become relevant with regard to stock prices. As a consequence these trends along with how the smart money anticipates and capital reacts, create a “liquidity cycle” that potentially change whether or not we are in a stock market or market of stocks and which in turn drives the “risk cycle”.

Disclaimer: I grabbed some LNKD near the end of the day today (2/12)

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Modeling Your Past Trading Results At Different Risk Levels

So In the last few post I have really focused in on objectively modeling risk within a portfolio given a particular system. Before I go and amend the heck out of the spreadsheet to upgrade it to include the possible input of fees, of monthly addition of new capital to combat some of the decay, adding a “kill switch” input/function where the results are automatically capped at a particular loss if you draw down below a certain amount of your initial risk, and possible other features at some point I may add, I wanted to actually use it as is using objective numbers rather than an arbitrary 20% probability of a particular result with a set expectation.

So I have set up all my trading results not including open positions over 2013 using options while setting stock trades aside separately. I chose to include hedges in the calculation. I did not update a few trades including my 1200% gain in twitter calls. Here are how I looked at the results. I had 281 closed trades since I started tracking. I have room for 5 inputs of theoretical “results” so how I break this down may create a slight difference from reality and theory here, but this is just a model. I want to keep all 100% losses together separately. I have 71 trades that expired worthless. That is 71/281=~25.267%. That is better than I thought I would get because of the aggressiveness of the options.

Now, I want to take a few slightly better than break even and slightly worse that average zero%. I basically took any option trade that made between -18 to 18% and got 33 trades that effectively equal a scratch 33/281=~11.744%

Then I want to average all of the remaining losses not included in the “scratch” area. These most likely will be premium that I salvaged to avoid the 100% loss and those nearing expiration that had failed to move enough.. The average loss here is ~63.8143% and there were 74 trades in this category 74/281=~26.3345% The actual expectation was positive but less than 1% but I will round down to 0%

Now the WINS. Any win over 100% deserves its own category. There are 43 of these 43/281=~15.3025% of all trades with average ROI of ~293.0585%.

And the remaining WINS. These were most likely trades that either I managed poorly and took off before it reached my target, or ones in which I sold and/or rolled out as expiration forced the issue. There are 60 of these 60/281=~21.3523% for an average ROI of ~53.87795%.

So… Now we can define our system. I like to list the GAINS from highest to lowest for easy and consistent interpretation when I look at other systems or modify the expectations.
This is one way to show what the system looks like.


We confirm that the probabilities are correct because these all add up to one. 37% of my trades produce a win, 48% win or approximately break even. But the largest gains clearly outweigh the losses.

The old way I used to do things would plug this in a kelly criterion calculator, find out that a full kelly I could risk 10% per trade if traded a single trade at a time, and then use my own calculator that factored in fees and correlation and multiple bets at the same time and fees and based upon a $10,000 account would conclude that the “optimal” number of trades at a 60% combined correlation would be 14 trades at 2.6% risk per trade for total of 36.4% capital at risk. Then I would curb that to aim for maybe 7 trades at 2% each.

But now I have learned that 10% resembles more of a “lotto ticket” even after 300 trades. But, I have my baseline of 10% as the max and can make an entirely new distribution at 1% 2% 5% and 10% to show you the difference between this system and one with a 20% probability of either 150% 50% 0% -50% or -100%.
First let us redisplay the results from the arbitrarily determined system

Now that we have real numbers, I want to be a bit more thorough than just using 1000 simulations. I will bump it up to 10,000 simulations per risk level. I will leave it at 1,000 just so the numbers remain the same. Keep in mind that the kelly criterion for my trades in reality is 10% vs the theoretical system is 14% so 1% risk actually is MORE aggressive with my system than the theoretical one. As a result you should expect a higher standard deviation and a higher average. With a larger simulation alone you will get a larger minimum and a larger maximum as well anyways.

And here is what the histograms and data looks like


I think more telling than the distribution since it is so difficult to see at what level the large decline really starts when you deal with such large numbers is the sample equity curves. So I will run a few of those. Click here to look at the equity curve of the theoretical model. and here are a few sample equity curves modeled after my trading abilities.



1p risk


2p risk

2p risk2


5p risk

AND half a dozen examples of 10% risk and the vicious account volatility

10p6 10p5 10p4 10p 3 10p2 10p risk

It’s important to understand that 10 simultaneous trades at 1% functions much differently than 1 trade at 10% or 10 trades over 10 trading periods at 1%. Unfortunately it isn’t so easy to model this and the results depend greatly upon how correlated the trades end up being (the lower, the better provided you can do so while still having the system be as profitable). For informational purposes, assuming no fees, you will see 10 simultaneous trades at 1% each function as a cross between 1 trade over 10 trading periods and 10% as you get some of the low risk volatility benefits of small position size and some of the high return benefits of 10% risk. The result is usually a better return per risk.

Going forward, I am working on improving this simulator so that it can allow for additional inputs that will help test how fees, adding capital, multiple simultaneously partially correlated bets and having “complimentary systems” can potentially positively influence return while also reducing risk.

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Equity Curve Of Risk – How Risk Influences Expecations

In the last post I discussed how I used a system and position sizing simulator to look at the ENDING equity of thousands of traders trading a theoretical system. I mentioned I would be showing sample equity curves at a given amount of risk by pulling up a random trader. It’s a lot easier on the spreadsheet to get a better sense as you can just press the F9 key to recalculate the random iterations and thus instantly bring up an entirely new random equity curve with all the same settings. You can go through several examples in a short amount of time. It is a bit more time consuming to create new JPEG images of each of them and then post them here so I will only be showing a few.

To further illustrate the type of “risk” you are taking by a particular strategy I provide just one random “trader’s” equity curve of each of them. Understand that results may not be entirely typical but pay attention to the % drawdowns to get a broad sense of the type of risk you may look at and endure.

Please note: The actual expectations of the system you use will drastically impact the type of volatility you see with every 1% change in risk. These sample equity curves are only made with the trading system with an expectation of a 20% chance of each of a 50% loss, 50% gain, no change, 100% loss and 150% gain.

1% risk

1p risk

2% risk

2p risk

5% risk

5p risk

As you increase risk, the results become more polarized and more extreme, so I will provide a few examples for those at the supposed “optimal” risk percentage of 14% risk

14d2 14d 14.2 14

The phenomenal results of a few skew the results of the rest. The drawdowns are insane as you see 70% and 80% drawdowns.

Can you stand 80 trades of being down steadily as your account drives lower to HALF of what it started with? Most people can not and would capitulate so even putting 5% of your capital into this “system” becomes problematic. Granted multiple bets with a lower correlation that adds up to 5% or even more may be actually “lower risk” than 5%. Granted, you can potentially use strategies that actually profit from market overall volatility such as allocation models and rebalancing and modern portfolio theory and hedging and pairs trades and such, you can put in some income and weight a lot of your portfolio with stock that have more of a slow and steady drift upwards that 70% of the time actually provides more stability and increased liquidity that can comba the negative effects of account volatility. Granted, a MORE profitable system can allow you to risk quite a bit more without the same drawdown expectations…. But even so, we are talking about a winning system where even at 1/3rd of what some quants would suggest to be “optimal” over a finite amount of time the returns are very likely to be terrible over a significant period of time.

Can you see why long term capital management went bust now as they did not test their assumptions while taking only a small sliver of time in the past by which to evaluate their “expected risk”?

I could get into how uncertain the world is and how your estimated “edge” within a system is also not a certainty which is still an assumption that this model must make to provide results, but at least can be recalculated with different sets of expectations. But I hope that this post has been educational enough for you to make at a minimum slight, productive adjustments to your way of thinking, if nothing else.

Don’t blow up like LTCM… Test all of even your most basic assumptions… Evaluate your risk in as many ways as you can. Understand risk and how to manage it. Control your destiny rather than being a victim of your own emotional compulsions to sell at the worst point of time and capitulate just before your system takes off because your system is too volatile. Understand the dynamic nature of reality and how increasingly large leverage and risk may be increasingly more volatile while also being more vulnerable to small changes in the conditions by which you based your assumptions. Understand the need to be well capitalized and that fees aren’t factored in and more negatively impact the volatile systems that have an increased probability of drawing down significantly from the starting point. Constantly seek to let the facts guide your conclusions, and seek productive improvement on the way you look at things. Then risk can serve you, rather than you “getting Serrrrrrved” by risk.


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