OABOT 4/16 info

Overall, in general the market rewarded Momentum today with Quality not far behind. That suggests we may be transitioning from quality to momentum phase with laggards up next.

OAbot rewards stocks with contracting volatility, it classifies stocks, and it grades stocks differently depending on which risk cycle classification it is in (and other data). It then looks over multiple categories and awards bonuses based upon how well each category is doing. (market cap, classification, industry, sector, above/below $10,etc)
The OAbot is not yet set up to anticipate rotations. It can either rate stocks based on “what’s working right now” (and average scores of individual stocks in categories and looking at relative volume) from a multitude of categories. Or it can give you a good picture of what is working and you can manually find some picks based upon that if you do some manual work… it will all be automated eventually so that “what’s next” also get’s rewarded. The easiest thing to improve it will be to adjust and tweak the existing formulas, the difficulty is in finding the time and setting up the formulas to pull from the necessary areas. So as of right now I have not put the required effort into making the ranking perfect just yet.
Right now, OAbot says that momentum is working in every sector today except financials where quality is still ahead by a narrow margin, and basic materials where it’s neck and neck but momentum still has the lead. So laggards as a whole may be a good spot to look, or momentum names that have not yet moved just yet.

OABOT is in the early stages of development to be able to anticipate, and can at least provide guidelines where manual work can be done to both confirm it and look at the names.

I will look at all the stocks names in each industry+category that OABOT highlights based upon it having enough stocks in each category to draw a conclusion, and based upon anticipating what is working next, after looking at what is working now. This is still very incomplete as the current version only looks at the “larger fractal’ by looking at which categories have been “working”(making extreme moves) including daily, weekly, monthly, quarterly, 6mo and 12mo returns. As such, the number of industries that qualify are always limited.

The following industries are popping up as relevant in risk rotation:
(listed by the stock type you should anticipate)
Laggards
Business Services
Medical Appliances & Equipment
Industrial Metals & Minerals
Semiconductor Equipment & Materials

Momentum
Oil & Gas Drilling & Exploration
Credit Services

Short Squeeze
Independent Oil & Gas

Full list of stocks that are in both the risk cycle and industries that you can use to anticipate according to OABOT:
http://finviz.com/screener.ashx?v=111&t=ABCO,ABMD,ACI,ACRX,ADS,AMDA,ARC,ASM,ASPS,BLOX,BTU,CARB,CASS,CATM,CNSI,COHU,CSII,CVG,CVO,CYBX,CYNO,DLX,DNN,DSS,EDAP,ENOC,ENV,ETRM,EXAC,EXLS,G,GMED,GNMK,GPN,GSM,GSOL,HBM,HOLX,INWK,ISRG,IVC,KLIC,KOOL,LDRH,LMNS,LPSN,LTXC,MCO,MDXG,MIL,MMS,NANO,NAVB,NRP,NSP,NSPR,NUVA,NVMI,NXTM,OFIX,ONE,PFMT,PFSW,PKE,PLAB,PLG,PLM,POWR,PRGX,PVG,PZG,REE,REIS,RIOM,RRD,RTIX,SFE,SMA,SNN,SNPS,SNX,SPNC,SSH,STJ,STXS,SURG,TAHO,TAS,TCK,TER,TGB,TISI,TNET,TRQ,TSRA,UMC,UPI,URG,URRE,URZ,USAT,UTEK,VALE,VASC,VOLC,VRSK,WMGI,ZLTQ,GDP,SFY,CLR,PVA,CRK,UPL,KWK,MILL,EOX,END,COF,SLM,DFS,CACC,CSH,AGM,DLLR,CSE,AXP,NBR,BBEP,VET,VNR,CRT,REXX,MHR,PWE,FXEN

When it comes to anticipating, you also have to come up with a way to anticipate a rotation of industry and/or sector…. That is a tricky task because in certain cases, the market should reward industries which are just slightly off high, other times, you want industries that are “working” right now, and other times you want the beaten down industries that have been neglected the most. I don’t know if I am sophsiticated as a trader just yet myself to really master industry rotation or how to put sector rotation into code so I’m not sure how far I can get on this regard. But I have counterbalanced OAbot answering “what’s working? and how much?” with bonuses applied after answering “What industry/sector has a high average (multiple stocks with good setups)?”

I have a feeling I’m not explaining myself very well because I don’t have the time, but ask if you have questions and I will answer when I can.

 

p.s.LANFORCE (sorry, put here strictly for traffic and page views of lanforce deprived AH members… lol)

Dude, your breadth is bad

 

yellenteethBad Janet Yellen breath leads to bad market breadth.
One of the features I have implemented into OAbot is a sort of market breadth overview to give me a “big picture” idea of what is going on. Here is what it looks like:

breadth

I took the % advancers divided by the amount of significant movers (advancers+decliners) to get the % of stocks of that particular move that are bullish. I made an adjustment since obviously a stock cannot be down 150% over a year so that it was the equivalent down move needed to bring a stock back to where it started. For example if a stock was 100% a 10% up move would bring it to $110. A $10 loss would take it back to $100 or a 9.09% decline. Therefore when I say “10%+ monthly movers” I am really comparing 10% up movers to 9.09% down movers. When I talk about 50%+ movers I am comparing the number of 50% up movers with 33.33% down movers.

You may notice some deteriorating breadth. It started with a big bearish move on the daily on Friday which continued on Monday on the daily and started turning the weekly bearish. Tuesday showed a lack of conviction on the dip buy and today on Wednesday the Weekly breadth is sharply lower and even the monthly is starting to turn. Various measurements via looking at stocks above their moving average have declined. The market had been overbought for some time and the sharp change on the daily is concerning.

If we are to see aggressive conviction buying it will look like this: The extreme moves will not only make a dramatic shift from oversold to become bullish, but it will be lead by the larger of the two numbers will be signalling leadership. In other words, the 4% movers on the day will be even more bullish than the 1% movers on the day. The weekly 15% will be greater than the weekly 5% and the weekly 30% should be greater than the 10%. That is a bullish divergence in breadth that means business. A lot of breadth moves depends upon the context. Afterall, it really is just looking at movement independent of market cap to see what the market is doing. But put in context it can be powerful. But doing so some times is tricky. Overall breadth is still bullish, but it is difficult to tell if this is just a shakeout to the downside before another leg higher and the longer term signals to get aggressively bought and more overbought, or if the market is showing signs of rolling over.
sectorbreadth

Further analysis can be done to the individual sectors to spot oversold areas, or areas that are leading off of significant lows, or just a more in depth litmus for the market. Unfortunately there is not always enough movers of a particular type within a sector to draw any conclusions. a 0% or 100% signal or an error message may just be a small sample size of movers.

The information may be more valuable if one were to track this data and provide a moving average of sorts over time to smooth out the results over the last 3-5 days or even over the last 10 days.

I am a bit confused, mostly because the bull run has been going on for 5 years so it is hard to interpret a shift off of every signal showing aggressive, perhaps some may say overbought conditions to this sell-off that is spilling over from daily to weekly and even monthly data is starting to shift bearish now. Nevertheless, I will let people know how I see it though.

My interpretation is that one of the bullish things about this is that you are seeing individual leadership with Financials and consumer goods which probably wouldn’t happen if this was the start of a monster correlated rush for the exits pre crash or if this was a MAJOR top. It still could be a high/minor top before a substantial orderly correction, OR there could be a few disillusioned investors in these areas (less likely). The market had been severely overbought in terms of many of the longer term breadth signals, particularly the % of stocks within 1% or 5% of their 52week marks. This could be interpreted as most of the severe laggards on the “larger fractal” (higher time frame) have already gone, which may mean we are setting up for an epic short squeeze and euphoric conditions where the trash stocks start making their explosive moves and the market is just trying to shake people out before the move.

While it easily could go into some kind of euphoric, mania stage as many major tops do, and this is unlikely to be a major high or the top, eventually one of these kind of actions will lead to a lot more pain so it is prudent to be cautious. It is usually aggressive selling of everything that happens before significant corrections and bear markets. Even though you have a couple sectors leading for now, that may still change. Conversely it is the aggressive buying of everything that occurs during the early phases of a bull market (2009 as an example).

There are reasons to be cautious until we see some more significant signals. Healthcare which tends to go near tops had been very strong prior to the last couple weeks action particularly fueled by some biotechs really selling off. Utilities have had the best setups, remain strong and have had some days with large relative volume. Also a danger of nearing the tops…. I don’t want to downplay the risks that exist here.

With that being said, a part of me can’t shake the idea I’ve had since the end of last year which is that there will be a correlated selloff or two in early-mid 2014 which will lead to a breakout in late 2014-early 2015. The idea behind that is that the market is attempting to get the masses to miss out on stocks before even the dow begins to enter the secular trend mode and everything with it in a huge rip higher like the 1980s. The pension funds have to rotate into risk to stay solvent, so large and mega cap companies will have to trend higher, and one would think that the smart money would stay ahead of this move and rotate into some large and mid caps as well, sell into the pension fund buying and rotate into smaller and smaller cap stocks over time. Perhaps this is just a big rotation out of the small and mid caps and into the large and mega caps, and to interpret it as a sign of a correction is wrong. Nevertheless, so many stocks suggesting that the market is selling off is still a very cautionary signal that one cannot ignore.

The trouble of course with breadth is that it is often open to interpretation and is only useful when taken in proper context and when framed according to objectives and risk. Getting the context right is only occasionally easy. I will keep an eye on this as it develops. For now I am happy to be hedged, playing fewer positions and looking to raise a bit of cash when I can while rotating into financials.

Some Financials OAbot likes right now.

http://finviz.com/screener.ashx?v=211&t=AON,AJG,LPLA,RJF,HIG,EGBN,CHCO,ABCB,FBNC,CCBG,HST,BEE

 

How to Really Set and Accomplish Your Financial Goals

How to Really Set and Accomplish Your Financial Goals

In the last article I described the detailed process of goal setting and used the example of weight targeting; now we are going to relate the same exact process to financial targeting via stock trading.

In this case, specific goals will not be “inevitable” over a fixed period of time like they essentially are with the weight targeting if we follow the procedures. There is too much uncertainty, variability and chaos involved in trading. However, what you can do it plan for an “exact probability” of achieving a particular goal or more given certain assumptions with the right tool, or an approximate probability given that the assumptions are also approximately accurate.

First, set goal and break it down. You want to return 100% with lower risk. That might translate into a consistent and reliable 6% per month compounded. You might look at trading system expectancy and use 1% of risk per trade and determine how many trades you need to complete. For example, if you earn an increase of 1 times your risk per trade you need 100 trades with 1% risk over the course of the year.

Realistically though, there are some issues that we didn’t have with the weight-loss example. The above measurements are flawed. If you had a 50% drawdown and you need 100% gains to get back to even so if you have a 50% drawdown and 100% loss, the “average” result is 25% gain but the net result is still no change. So you need a tool that turns this into more accurate and actionable information. Additionally, success is not inevitable and therefore, you only have a particular probability of obtaining your result in a year, given your strategy.

I created a tool that would help me simulate results given certain expectations and assumptions and compare results at different levels of risk.

simulator

If the system above produced 200 trades a year I could simply look at the simulated results of thousands of simulations and determine estimated probabilities to measure expectations and risk level.

Based upon the above assumptions, I was able to run 5000 simulations and determine:
1) The number of times out of 5000 (probability) that my portfolio is down at all after 200 simulated trades.
2) The number of times out of 5000 (probability) that my portfolio is up 100% or more after 200 simulated trades. (probability my goal is reached)
3) The number of times out of 5000 (probability) that my portfolio “kill switch” is hit at all after these 200 simulated trades with kill switches of:
a)15% b)20% c)25% d)30% e)35%
4) The probability that goal is met with kill switches of
a)15% b)20% c)25% d)30% e)35%

Using these I am able to monitor actual goals based upon what I consider a realistic set of expectations to balance my personal desire for reward with my desire to avoid risk and avoid drawdowns. There is always a tradeoff. From there, I am able to adjust position sizing to determine what position size most accurately reflects my goals.

Chance you will be up after 200 trades given position sizing of:
1%:87.5% chance (fees eat into results increasing change of a decline)
2%:95% chance
3%:95.4% chance
4%:95.7% chance

Chance you will produce a 100% return after 200 trades given position sizing of:
1%:1.2% chance
2%:46% chance
3%:70% chance
4%:79.4% chance

You may be looking at this like “oh, sweet, I guess I should risk larger position sizes”. You’d probably be wrong.

The greatest danger is in declaring the results “aren’t good” they “don’t work” or “they don’t represent reality”. It is balancing the odds of their being “something wrong” with the system as large drawdowns occur, with your own personal ability to continue to trade the system as well as your own personal ability to withstand large changes in account size. This is why determining the probability that drawdowns occur is valuable.

Let’s look at the probability of a 15% drawdown from highs occurring with this system at different position sizes:
1%: 28.2%
2%: 82.2%
3%: 98.1%
4%: 99.99%
Now let’s relate this to the probability you reach your 100% goal WITH a 15% drawdown in place:
1%: 1.5%
2%: 21.5%
3%: 14.7%
4%: 11.3%

Not letting the system work comes at a cost. But are you going to just let the system work even when your account continues to decline? That also comes with a risk that the initial assumptions about the system were wrong.

But let’s say you have faith/conviction/high confidence in the “system” and understand what you are using has a high degree of leverage. So you are emotionally calm even in the face of a 25% drawdown, what then?

What are your chances of a 25% drawdown over 200 trades given position size, given the assumptions are correct?
1%: 3%
2%: 24.5%
3%: 60.1%
4%: 87%
As a result, what are your odds of a 100% goal being met over 200 trades if you quit after a 25% drawdown?
1%: 1.5%
2%: 40.4%
3%: 45.7%
4%: 34.3%

A 3% position size may still increase your chances of accomplishing your goals at the cost of stress,
If you have the peace of mind of a Zen master, or don’t mind the stress induced ulcers, vomiting, heart attack, etc… Then you may be able to endure 35% drawdowns or greater.
What are the chances of a 35% drawdown occurring with given risk size?
1%: 0.1%
2%: 4.5%
3%: 21.7%
4%: 46.9%
As a result, what are your odds of a 100% goal being met over 200 trades given that you quit after a 35% drawdown?
1%: 1.5%
2%: 44.7%
3%: 65%
4%: 58.7%

Let’s say you create an intelligent robot so that emotions aren’t an issue and for health issues you intentionally are not even going to be aware of what is happening in the system until 1 year from now. Only a 50% drawdown will automatically cause the robot to stop trading. What then?
Chances of 50% drawdown at given risk size:
1%: less than 1%
2%: less than 1%
3%: 2.3%
4%: 10.4%
5%: 23.3%
100% goal met over 200 trades as a result
1%: 1.5%
2%: 46%
3%: 69.6%
4%: 77%
5%: 75%

The previous image is what the relationship between risk and volatility of account is assuming:

1) Unlimited time. (With limited time the results either ARE better than expected or worse. The probability of worse than expected results increases as you increase risk when time is limited. )

2) Certainty in the set of expectations. (The reality is uncertain and therefore it rewards risking less.)

3) That you can weather HUGE volatile swings. Note specifically that the cost in terms of volatility skyrockets as the benefit flattens out and then declines.

4) That fees are never an issue. Fees can make both very aggressive strategies due to volatility and very conservative strategies unprofitable.

5) That your edge remains consistent and results are normally distributed over time. If they are not, it favors more conservative risk.

6) That there is zero correlation between trades as correlation increases, the benefit for additional trades begins to decrease.

7) Probably some other things I haven’t considered.

With a finite time, increased risk skews results so that very few outliers of phenomenal results skew the “mean” results so the mean is greater than the median. The Distribution of Results at 1% is relatively normally distributed but with 3% risk, 5000 simulations of results are distributed like this:

avg

The simulation allows you to better understand the relationship of returns given that these assumptions are different by running simulations with different numbers. You can manage your expectations IF the results vary in either direction to better manage uncertainty.

So how you make financial goals should really contain in it not only a very deep understanding of the type of drawdown you want to endure, the awareness of the cost, but also contain the probability of a particular result with an understanding of the downside and so on. For example, your “goal” should really be considered after running some simulations and broken down to:

1) Probability of achieving goal (you may even consider probability of exceeding it by a particular amount)

2) Probability of breaking even

3) Probability of avoiding certain drawdowns at various levels

4) The Average Expectation of results

For example you might set the following goal:

1) 100% return or more

2) 90% chance of breaking even or better

3) Probability of a drawdown greater than 35% less than 10%, probability of drawdown greater than 20% less than 25%.

4) Probability of obtaining goal greater than 40% with a kill switch of 40%.

Given that goal, and GIVEN the expectancy of the system described, you can determine what position size meets those goals and if not what the tradeoff is (from which you might adjust your goal). You will then find that a 3% position size is too much, a position size of 1% is too small. So you need something between 1 and 3% 2% works but a 1.8% or 2.4% might also work.  It depends on if you’re more willing to take a better average and greater probability at expense of more volatility and greater probability of drawdowns, or prefer increasing the chances of not having a bad result and decrease the volatility and drawdowns. Alternatively, you might actually wish to define a range and be a bit discretionary with position size as long as it falls in the range in exchange for less awareness of what your odds of attaining your goals. Let’s say 1.8% is the smallest position size that will meet all of your goals and 2.4% is the largest position size. You might set a RANGE of position size, and then aim for 1.8% when your confidence is low and/or R/R and/or expectancy is lower than the average for the system, and aim for closer to 2.4% when it is higher. Additionally, you might also decide that there are certain instances of the market where you get a good oversold signal and market breadth has just flipped after a serious decline and you have a larger than usual edge across the board. You may want to position conservatively in all other instances so that you have the capital and ability to take on new aggressive positions when this moment comes aiming for maybe 20% of the year to make up 80% of your gains. There are many ways to break it down, but ultimately the combination of your system, your tolerance to drawdowns and volatility, your desired distribution of results, your need for awareness of expectancy and position size will help you to monitor and achieve your goals with the help of a position sizing simulator.

 

How to Really Set and Accomplish Your Goals

There is a story which says that there once was a man that prayed every day to his God to win the lottery. Years went buy until he was on his death bed having never won the lottery. When he arrived on heaven’s gate he asked God why he never won the lottery, to which he heard a booming voice from his God who replied, “You never bought a ticket”.

Goal setting is not just about “hope” and “prayer” alone. There are certain laws of the universe that exist and “thinking really hard” is NOT going to change them. “thinking really hard” MAY change your beliefs, which may in turn cause you to alter your behavior, form habits, which lead to certain tasks being accomplished in a certain way that may happen to produce the results you were looking for.
Einstein once said “God does not play dice with the universe”. There is cause and effect. It will serve you much better to actually recognize the necessary causes to produce the effect and plan to prepare those causes, ensuring everything is congruent with producing a particular outcome. I’m not saying the alternative of hope, prayer, and thinking doesn’t have a place, but there is no reason to rely entirely on thought alone, when there are proven methods to consistently deliver results on time.

There is a huge industry around “magical thinking” with the “secret”, the “law of beliefs”, and the “law of attraction”. Re-framing your beliefs can be useful. Thinking more positively so that you feel better and are more congruent with your behaviors can be useful… But it is far more useful as the last and one of many components that may not even be necessary at all that will help you achieve your goals, as opposed to just hope and luck.  It is a bit more work, but it actually leads to consistent and reliable results!

When it comes to determining what needs to be done to accomplish them, some goals can be easy. How do you increase earned income? Increase the hours you work or the amount you make per hour!

Some goals can be more challenging. Targeting exact percentage returns via stock trading can be a bit trickier, and take a bit more strategic planning, but they aren’t impossible to measure with the right tools.

But first, let’s show how goal setting might work with setting fitness/diet/exercise goals around a core goal of targeting a particular weight. What does that have to do with financial goals? More than you might suspect!

Your first step is to break the goal down. If you want to weigh 160 pounds and you weight 175 that is a 15 pound loss. That goal is incomplete; you need a time goal as well. You can easily translate that to lose 15 pounds in 10 months. That translates to losing 1.5 pounds per month or maybe .35 pounds a week.

Second, translate that into information that is actionable and use tools to help you. You can run a caloric calculator and determine you need to consume 200 calories less per day at the normal rate of activity, or else you can just plan on burning 200 calories through additional exercise.

Next, come up with a plan in your everyday life or encompassing strategy which incorporates all tactics and procedure to accomplish the task of having 200 calories less in, or 200 more calories out per day on average. Then you form a habit or habits around these strategies and you may need to eliminate old habits in the process.

Finally, your beliefs have to be congruent enough with the goal if they are not already. If you do not think of yourself as someone who deserves to be fit or have subconscious internal beliefs that you are inadequate, THEN it becomes a problem as you then may become overwhelmed with your own success and find a way to do what’s less unknown, and more comfortable which may cause you to fail.

Notice how I prefer to turn “the law of attraction” on its head and make it the LAST component, only after I’ve planned on how I will align all my strategies and habits with the goal.  After all, it may be that your beliefs and thought patterns are already congruent with success and that you just need to take action.

Okay, so what does that have to do with accomplishing financial goals through trading? The process for all goal setting is nearly identical. In the next article, you will see why.

How You Are Losing Money Without Knowing It

If you are trading stocks you are losing money and you don’t even know it. How? Mistakes. How much? I can only tell you how much money the average person is leaving on the table.

It is estimated that mistakes cost traders on average more than 4 times what they risk (in a single trade) PER mistake. (1)
What does that mean? Besides leaving lots of money on the table let’s translate it into facts?
If you are to place 20 trades a month or 240 trades a year and risk 1% per trade, and your profit on average is HALF of what you risk per trade…
then 1% per trade yields an increase of your account size by half a percent per trade.
Without mistakes and $15 per completed trade (buy plus sell costs) and 100k starting amount your gain is about 224.09% per year!.

BUT, what if every 20 trades (a trade a month) you made a mistake? Rather than gain one half of what you risked, you lose 3.5 times what you risked for a net cost of 4 times what you risked.
A 3.5% reduction in your account every 20 trades translates into a gain of “only” 98.19% per year!
In this instance, the gain cost you $125,900.30 with a $100,000 account! You would gain 63.52% more in a year without mistakes.
What if you make 2 mistakes a month or once every 10 trades? Now your 224.09% gain in a mistake free system in a year is down to a mere 20.93%. In this case, your mistakes cost you $203157.50 and you would have made a 167.99% increase in your final amount if you were mistake free and a 63.885% increase if you just cut your mistakes in half.

Nobody is perfect. I am not suggesting you can eliminate mistakes completely, but you sure can reduce them significantly.
If you are trading an account of 10,000, 2 mistakes a month makes the system lose about 15% a year due to compounding costs of commissions compared to gaining over 50% a year if you can reduce them to one average mistake a month.

Awhile ago, I was like you having just come across this realization. At some point, I decided I was going to make it my goal to reduce everything down to a science without compromising the strength of the system.

With a mechanical system that is as easy as hiring someone to trade for you or building a bot, or paying someone to program a bot to trade for you. However, some of the best traders are discretionary. I do not trade a mechanical system, but a discretionary one that allows me the individual skill of identifying setups. This method was mostly taught to me by the “Option Addict”. This guy made a fortune and have many trading members that can attest to that fact buying puts in bear sterns during the infamous collapse. He recently got me and several other traders in TWTR for 1000% gain the first go around, the same year he delivered a handful of trades that netted around 500% But his skills are not limited to options. Day in and day out he can identify a handful of stocks, many of which go on to mke some of the most explosive moves in the market. This guy is the real deal and I have spent at least hundreds of hours learning from him and thousands in my trading career trying to trade like him.

Nevertheless, I was determined to automate as much as possible. The first step was to build a position simulator that could evaluate and simulate a thousand trades and repeat that process thousands of times and complete a “Monte Carlo” simulation to evaluate the expected distribution of results given certain assumptions.

The next process was to build a spreadsheet that classifies stocks in several ways, then use those classifications to evaluate the stock uniquely based upon this criteria. The stock is first scored according to it’s own variables. Then the score is adjusted based upon the overall market and strength of its peers and rotation of capital in each of its multiple categories. Finally, based upon what is moving, the spreadsheet will anticipate based upon what is “next” to be “in phase” in a particular “cycle”, and rewards stocks that have timing ratings according to this.

After all adjustments you can use this list in one of two ways.
1)Automating your selection of generating a list of ideas from which to filter down into a small handful….
OR you can generate a handful of ideas on your own and only take the top few highest rated stocks to be added and watched.
Meanwhile, the spreadsheet can also tell you what sectors, industries, cycles, and other categories are currently “in favor” automatically.

In the process I built a spreadsheet that would automate and simplify the process as much as possible without sacrificing the quality. The process then can be redefined:

1)Identify top setups automatically for manual scanning to narrow the field
2)Hand pick the names I like to get maybe a dozen or two.
3)Objectively reduce the number of names by choosing the top 5-10 (or however many you choose) scored stocks after entering the names back in.
4)Enter in some more details (stop, target) and get a risk reward analysis and use that to further reduce the list or identify what sort of price you will have to get for each stock to be “on par” with the others.
5)set your limit/stop buy orders, or else alerts or watch and wait for the trigger.

The structure of the spreadsheet is done, but some minor tweaks will continue to be done to improve the product.

(1)Van Tharp http://www.vantharp.com/trader-test/mistakes-are-the-downfall-of-most-traders.htm

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.
industry

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.

 

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.

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.

Going Against The Great

baberuth
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:

lnkd1

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.

lnkdtwo

 

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)

How A Portfolio “Kill Switch” Can Change Everything About Long Term Expectations

We all most likely have some sort of psychological breaking point in which we would give up on the system, or in which we should probably re-examine our strategies, and in some cases, in which people go crazy and “break” to the point where they start revenge trading and no longer are actually trading their system. The prior risk management modeling I’ve done using a spreadsheet has not taken into account fees or how one handles draw downs. I have modified the spreadsheet such that there WILL be a fixed fee involved with trading (thus if you have a significant drawdown fees play a much bigger role as a percentage of your account), and I have added the ability to add a “kill switch”. Since I added fees at minimum, it’s necessary to have at least a kill switch of 100% (loss) so you don’t go on calculating $12 fees when your account is less than $12 and continue to go negative. In the meantime I set it up for 1000 hypothetical trades since we now have a more practical model I can simulate it for much longer time periods if need be.

So my hypothesis is people will give up after a drawdown of varying amounts. This drawdown is from the account HIGHS, not from the starting amount. Thus, for some people the end result after 1000 trading periods (whether they participate in all 1000 using the given strategy or not) will actually sometimes be better if they risk less to avoid this breaking point.

PLEASE understand that this simulator only uses one trade at a time as if you only play a single OTM option trade at a time and hold no trades simultaneously. In reality having more relatively uncorrelated trades at a smaller risk % per trade can often improve results at less risk, especially if you have multiple systems such as profitable investing and trading stocks and other asset classes in combination with your options. I will get around to testing partially correlated trades simultaneously and possibly entirely different systems in the future. For now the expectancy is based upon

p1 17%
p2 20%
p3 16%
p4 23%
p5 24%
w1 294%
w2 52%
w3 0%
w4 -64%
w5 -100%

My 2013 OTM option trading results at various risk levels and various kill switches to see a distribution of simulated results. For all accounts I will use a $10,000 starting portfolio and $12 for completed transaction (buy fee plus sell fee) just to use round numbers and so that fees is somewhat significant to the equation.

First let’s start with someone who is very risk averse and cannot handle even a 5% drawdown without feeling incredibly nervous and emotional and plans to quit at a 10%.

I had to think about the results and double check them at first, because I was surprisingly getting a significant skew right with only 1% risk.The mean is greater than the median. (i.e. the “average” is skewed by a smaller number of outliers such that the majority of the people are actually below the “mean”)

The reason was that at 1% with a 10% drawdown, you not only have too little capital at risk to the point where fees will eat away and a large percentage won’t get off the ground, but also that it is extremely common that at some point within the first 200 trades of those 1000 that you will hit the kill switch and very few will make it through all 1000 to produce significant gains which on average will be pretty high even with 1% risk (given that you can last that long). The overall average was still above the starting amount of $10,000, but in most cases, the fear of drawing down killed the traders hope of making money and taking risk. Increasing beyond 1% actually reduced average results because more risk = more volatility and greater probability that you will drawdown before giving your system a chance to compound it’s gains. 10% in theory is around “optimal” in terms of maximizing geometric return, but to someone who is risk adverse, it is probably less than 1%.

I then decided someone who is mildly risk adverse and doesn’t like 10% drawdowns, really starts to go crazy after 15% and ultimately feels compelled to shut down the account after a 20% drawdown. This was interesting because the average ending amount really increased from 1% to 2% but then decreased to from 2% to 5% to the point where you are better off risking 1% than 5% if you can’t handle a 20% drawdown.

I didn’t gather the histograms yet but here are the data points for 1000 monte carlo simulations of 1000 trades under each given condition.

drawdown switch 10p and 20p

30% and 40% drawdown was up next.

drawdown switch 30p and 40p

 

As you increase risk and become more lax on the “kill switch”, the worst case scenario gets worse, but so does the best case scenario. The skew actually becomes less noticable with a larger drawdown switch AND less risk per trade. Over the long run if you LET the trading system work, the results begin to normalize and cluster around the average. However increased risk increases the skew and depending on the drawdown kill switch may not improve even the MEAN average results. With these trades the approximate kelly criterion is 15% of capital but with even the risk aversion to withstand a 30% drawdown from high you are still best risking less than 1/3rd of the kelly.

 

So that brings us back to the kelly criterion graph which is entirely misleading if you are not aware of the other variables including personal aversion to risk, sample size, time period, etc. This graph is with the assumption of infinite time on your side because it was mathematically convenient. However, based upon some understanding of real data in a finite number of time given certain psychological barriers one has to cross among other things you know that for practical application even risking 1/3rd of this amount is incredibly aggressive when using an option based market strategy assuming one single uncorrelated bet at a time. We are also assuming KNOWN information and a fixed edge as opposed to a more uncertain one, both of which favor more caution. On the other hand, I believe I left a lot of money on the table and can improve my system by executing it and managing it more efficiently, and that eventually I will get around to testing how multiple trading systems work on the account. My theory is you can reduce risk and increase return by using complimentary systems (a stable consistent risk adverse system combined with an aggressive one both of which are relatively uncorrelated with the right mixture will compliment each other and both improve return and reduce risk while “normalizing the returns” over a finite number of time)

 

Hopefully this post is enlightening and helps you really analyze and understand risk, and I look forward to advancing my spreadsheet in the future to help me more thoroughly analyze a more multi-dimentional (synergistic) approach to risk.

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.
P1=.153025
P2=.213523
P3=.117438
P4=.263345
P5=.252669

W1=2.930585
W2=.5387795
W3=0.000000
W4=-.638143
W5=-1.00000

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
results

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

resultme

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.

 

1%

1p risk

2%

2p risk

2p risk2

5%

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.

OABOT 4/16 info

Overall, in general the market rewarded Momentum today with Quality not far behind. That suggests we may be transitioning from quality to momentum phase with laggards up next.

OAbot rewards stocks with contracting volatility, it classifies stocks, and it grades stocks differently depending on which risk cycle classification it is in (and other data). It then looks over multiple categories and awards bonuses based upon how well each category is doing. (market cap, classification, industry, sector, above/below $10,etc)
The OAbot is not yet set up to anticipate rotations. It can either rate stocks based on “what’s working right now” (and average scores of individual stocks in categories and looking at relative volume) from a multitude of categories. Or it can give you a good picture of what is working and you can manually find some picks based upon that if you do some manual work… it will all be automated eventually so that “what’s next” also get’s rewarded. The easiest thing to improve it will be to adjust and tweak the existing formulas, the difficulty is in finding the time and setting up the formulas to pull from the necessary areas. So as of right now I have not put the required effort into making the ranking perfect just yet.
Right now, OAbot says that momentum is working in every sector today except financials where quality is still ahead by a narrow margin, and basic materials where it’s neck and neck but momentum still has the lead. So laggards as a whole may be a good spot to look, or momentum names that have not yet moved just yet.

OABOT is in the early stages of development to be able to anticipate, and can at least provide guidelines where manual work can be done to both confirm it and look at the names.

I will look at all the stocks names in each industry+category that OABOT highlights based upon it having enough stocks in each category to draw a conclusion, and based upon anticipating what is working next, after looking at what is working now. This is still very incomplete as the current version only looks at the “larger fractal’ by looking at which categories have been “working”(making extreme moves) including daily, weekly, monthly, quarterly, 6mo and 12mo returns. As such, the number of industries that qualify are always limited.

The following industries are popping up as relevant in risk rotation:
(listed by the stock type you should anticipate)
Laggards
Business Services
Medical Appliances & Equipment
Industrial Metals & Minerals
Semiconductor Equipment & Materials

Momentum
Oil & Gas Drilling & Exploration
Credit Services

Short Squeeze
Independent Oil & Gas

Full list of stocks that are in both the risk cycle and industries that you can use to anticipate according to OABOT:
http://finviz.com/screener.ashx?v=111&t=ABCO,ABMD,ACI,ACRX,ADS,AMDA,ARC,ASM,ASPS,BLOX,BTU,CARB,CASS,CATM,CNSI,COHU,CSII,CVG,CVO,CYBX,CYNO,DLX,DNN,DSS,EDAP,ENOC,ENV,ETRM,EXAC,EXLS,G,GMED,GNMK,GPN,GSM,GSOL,HBM,HOLX,INWK,ISRG,IVC,KLIC,KOOL,LDRH,LMNS,LPSN,LTXC,MCO,MDXG,MIL,MMS,NANO,NAVB,NRP,NSP,NSPR,NUVA,NVMI,NXTM,OFIX,ONE,PFMT,PFSW,PKE,PLAB,PLG,PLM,POWR,PRGX,PVG,PZG,REE,REIS,RIOM,RRD,RTIX,SFE,SMA,SNN,SNPS,SNX,SPNC,SSH,STJ,STXS,SURG,TAHO,TAS,TCK,TER,TGB,TISI,TNET,TRQ,TSRA,UMC,UPI,URG,URRE,URZ,USAT,UTEK,VALE,VASC,VOLC,VRSK,WMGI,ZLTQ,GDP,SFY,CLR,PVA,CRK,UPL,KWK,MILL,EOX,END,COF,SLM,DFS,CACC,CSH,AGM,DLLR,CSE,AXP,NBR,BBEP,VET,VNR,CRT,REXX,MHR,PWE,FXEN

When it comes to anticipating, you also have to come up with a way to anticipate a rotation of industry and/or sector…. That is a tricky task because in certain cases, the market should reward industries which are just slightly off high, other times, you want industries that are “working” right now, and other times you want the beaten down industries that have been neglected the most. I don’t know if I am sophsiticated as a trader just yet myself to really master industry rotation or how to put sector rotation into code so I’m not sure how far I can get on this regard. But I have counterbalanced OAbot answering “what’s working? and how much?” with bonuses applied after answering “What industry/sector has a high average (multiple stocks with good setups)?”

I have a feeling I’m not explaining myself very well because I don’t have the time, but ask if you have questions and I will answer when I can.

 

p.s.LANFORCE (sorry, put here strictly for traffic and page views of lanforce deprived AH members… lol)

Dude, your breadth is bad

 

yellenteethBad Janet Yellen breath leads to bad market breadth.
One of the features I have implemented into OAbot is a sort of market breadth overview to give me a “big picture” idea of what is going on. Here is what it looks like:

breadth

I took the % advancers divided by the amount of significant movers (advancers+decliners) to get the % of stocks of that particular move that are bullish. I made an adjustment since obviously a stock cannot be down 150% over a year so that it was the equivalent down move needed to bring a stock back to where it started. For example if a stock was 100% a 10% up move would bring it to $110. A $10 loss would take it back to $100 or a 9.09% decline. Therefore when I say “10%+ monthly movers” I am really comparing 10% up movers to 9.09% down movers. When I talk about 50%+ movers I am comparing the number of 50% up movers with 33.33% down movers.

You may notice some deteriorating breadth. It started with a big bearish move on the daily on Friday which continued on Monday on the daily and started turning the weekly bearish. Tuesday showed a lack of conviction on the dip buy and today on Wednesday the Weekly breadth is sharply lower and even the monthly is starting to turn. Various measurements via looking at stocks above their moving average have declined. The market had been overbought for some time and the sharp change on the daily is concerning.

If we are to see aggressive conviction buying it will look like this: The extreme moves will not only make a dramatic shift from oversold to become bullish, but it will be lead by the larger of the two numbers will be signalling leadership. In other words, the 4% movers on the day will be even more bullish than the 1% movers on the day. The weekly 15% will be greater than the weekly 5% and the weekly 30% should be greater than the 10%. That is a bullish divergence in breadth that means business. A lot of breadth moves depends upon the context. Afterall, it really is just looking at movement independent of market cap to see what the market is doing. But put in context it can be powerful. But doing so some times is tricky. Overall breadth is still bullish, but it is difficult to tell if this is just a shakeout to the downside before another leg higher and the longer term signals to get aggressively bought and more overbought, or if the market is showing signs of rolling over.
sectorbreadth

Further analysis can be done to the individual sectors to spot oversold areas, or areas that are leading off of significant lows, or just a more in depth litmus for the market. Unfortunately there is not always enough movers of a particular type within a sector to draw any conclusions. a 0% or 100% signal or an error message may just be a small sample size of movers.

The information may be more valuable if one were to track this data and provide a moving average of sorts over time to smooth out the results over the last 3-5 days or even over the last 10 days.

I am a bit confused, mostly because the bull run has been going on for 5 years so it is hard to interpret a shift off of every signal showing aggressive, perhaps some may say overbought conditions to this sell-off that is spilling over from daily to weekly and even monthly data is starting to shift bearish now. Nevertheless, I will let people know how I see it though.

My interpretation is that one of the bullish things about this is that you are seeing individual leadership with Financials and consumer goods which probably wouldn’t happen if this was the start of a monster correlated rush for the exits pre crash or if this was a MAJOR top. It still could be a high/minor top before a substantial orderly correction, OR there could be a few disillusioned investors in these areas (less likely). The market had been severely overbought in terms of many of the longer term breadth signals, particularly the % of stocks within 1% or 5% of their 52week marks. This could be interpreted as most of the severe laggards on the “larger fractal” (higher time frame) have already gone, which may mean we are setting up for an epic short squeeze and euphoric conditions where the trash stocks start making their explosive moves and the market is just trying to shake people out before the move.

While it easily could go into some kind of euphoric, mania stage as many major tops do, and this is unlikely to be a major high or the top, eventually one of these kind of actions will lead to a lot more pain so it is prudent to be cautious. It is usually aggressive selling of everything that happens before significant corrections and bear markets. Even though you have a couple sectors leading for now, that may still change. Conversely it is the aggressive buying of everything that occurs during the early phases of a bull market (2009 as an example).

There are reasons to be cautious until we see some more significant signals. Healthcare which tends to go near tops had been very strong prior to the last couple weeks action particularly fueled by some biotechs really selling off. Utilities have had the best setups, remain strong and have had some days with large relative volume. Also a danger of nearing the tops…. I don’t want to downplay the risks that exist here.

With that being said, a part of me can’t shake the idea I’ve had since the end of last year which is that there will be a correlated selloff or two in early-mid 2014 which will lead to a breakout in late 2014-early 2015. The idea behind that is that the market is attempting to get the masses to miss out on stocks before even the dow begins to enter the secular trend mode and everything with it in a huge rip higher like the 1980s. The pension funds have to rotate into risk to stay solvent, so large and mega cap companies will have to trend higher, and one would think that the smart money would stay ahead of this move and rotate into some large and mid caps as well, sell into the pension fund buying and rotate into smaller and smaller cap stocks over time. Perhaps this is just a big rotation out of the small and mid caps and into the large and mega caps, and to interpret it as a sign of a correction is wrong. Nevertheless, so many stocks suggesting that the market is selling off is still a very cautionary signal that one cannot ignore.

The trouble of course with breadth is that it is often open to interpretation and is only useful when taken in proper context and when framed according to objectives and risk. Getting the context right is only occasionally easy. I will keep an eye on this as it develops. For now I am happy to be hedged, playing fewer positions and looking to raise a bit of cash when I can while rotating into financials.

Some Financials OAbot likes right now.

http://finviz.com/screener.ashx?v=211&t=AON,AJG,LPLA,RJF,HIG,EGBN,CHCO,ABCB,FBNC,CCBG,HST,BEE

 

How to Really Set and Accomplish Your Financial Goals

How to Really Set and Accomplish Your Financial Goals

In the last article I described the detailed process of goal setting and used the example of weight targeting; now we are going to relate the same exact process to financial targeting via stock trading.

In this case, specific goals will not be “inevitable” over a fixed period of time like they essentially are with the weight targeting if we follow the procedures. There is too much uncertainty, variability and chaos involved in trading. However, what you can do it plan for an “exact probability” of achieving a particular goal or more given certain assumptions with the right tool, or an approximate probability given that the assumptions are also approximately accurate.

First, set goal and break it down. You want to return 100% with lower risk. That might translate into a consistent and reliable 6% per month compounded. You might look at trading system expectancy and use 1% of risk per trade and determine how many trades you need to complete. For example, if you earn an increase of 1 times your risk per trade you need 100 trades with 1% risk over the course of the year.

Realistically though, there are some issues that we didn’t have with the weight-loss example. The above measurements are flawed. If you had a 50% drawdown and you need 100% gains to get back to even so if you have a 50% drawdown and 100% loss, the “average” result is 25% gain but the net result is still no change. So you need a tool that turns this into more accurate and actionable information. Additionally, success is not inevitable and therefore, you only have a particular probability of obtaining your result in a year, given your strategy.

I created a tool that would help me simulate results given certain expectations and assumptions and compare results at different levels of risk.

simulator

If the system above produced 200 trades a year I could simply look at the simulated results of thousands of simulations and determine estimated probabilities to measure expectations and risk level.

Based upon the above assumptions, I was able to run 5000 simulations and determine:
1) The number of times out of 5000 (probability) that my portfolio is down at all after 200 simulated trades.
2) The number of times out of 5000 (probability) that my portfolio is up 100% or more after 200 simulated trades. (probability my goal is reached)
3) The number of times out of 5000 (probability) that my portfolio “kill switch” is hit at all after these 200 simulated trades with kill switches of:
a)15% b)20% c)25% d)30% e)35%
4) The probability that goal is met with kill switches of
a)15% b)20% c)25% d)30% e)35%

Using these I am able to monitor actual goals based upon what I consider a realistic set of expectations to balance my personal desire for reward with my desire to avoid risk and avoid drawdowns. There is always a tradeoff. From there, I am able to adjust position sizing to determine what position size most accurately reflects my goals.

Chance you will be up after 200 trades given position sizing of:
1%:87.5% chance (fees eat into results increasing change of a decline)
2%:95% chance
3%:95.4% chance
4%:95.7% chance

Chance you will produce a 100% return after 200 trades given position sizing of:
1%:1.2% chance
2%:46% chance
3%:70% chance
4%:79.4% chance

You may be looking at this like “oh, sweet, I guess I should risk larger position sizes”. You’d probably be wrong.

The greatest danger is in declaring the results “aren’t good” they “don’t work” or “they don’t represent reality”. It is balancing the odds of their being “something wrong” with the system as large drawdowns occur, with your own personal ability to continue to trade the system as well as your own personal ability to withstand large changes in account size. This is why determining the probability that drawdowns occur is valuable.

Let’s look at the probability of a 15% drawdown from highs occurring with this system at different position sizes:
1%: 28.2%
2%: 82.2%
3%: 98.1%
4%: 99.99%
Now let’s relate this to the probability you reach your 100% goal WITH a 15% drawdown in place:
1%: 1.5%
2%: 21.5%
3%: 14.7%
4%: 11.3%

Not letting the system work comes at a cost. But are you going to just let the system work even when your account continues to decline? That also comes with a risk that the initial assumptions about the system were wrong.

But let’s say you have faith/conviction/high confidence in the “system” and understand what you are using has a high degree of leverage. So you are emotionally calm even in the face of a 25% drawdown, what then?

What are your chances of a 25% drawdown over 200 trades given position size, given the assumptions are correct?
1%: 3%
2%: 24.5%
3%: 60.1%
4%: 87%
As a result, what are your odds of a 100% goal being met over 200 trades if you quit after a 25% drawdown?
1%: 1.5%
2%: 40.4%
3%: 45.7%
4%: 34.3%

A 3% position size may still increase your chances of accomplishing your goals at the cost of stress,
If you have the peace of mind of a Zen master, or don’t mind the stress induced ulcers, vomiting, heart attack, etc… Then you may be able to endure 35% drawdowns or greater.
What are the chances of a 35% drawdown occurring with given risk size?
1%: 0.1%
2%: 4.5%
3%: 21.7%
4%: 46.9%
As a result, what are your odds of a 100% goal being met over 200 trades given that you quit after a 35% drawdown?
1%: 1.5%
2%: 44.7%
3%: 65%
4%: 58.7%

Let’s say you create an intelligent robot so that emotions aren’t an issue and for health issues you intentionally are not even going to be aware of what is happening in the system until 1 year from now. Only a 50% drawdown will automatically cause the robot to stop trading. What then?
Chances of 50% drawdown at given risk size:
1%: less than 1%
2%: less than 1%
3%: 2.3%
4%: 10.4%
5%: 23.3%
100% goal met over 200 trades as a result
1%: 1.5%
2%: 46%
3%: 69.6%
4%: 77%
5%: 75%

The previous image is what the relationship between risk and volatility of account is assuming:

1) Unlimited time. (With limited time the results either ARE better than expected or worse. The probability of worse than expected results increases as you increase risk when time is limited. )

2) Certainty in the set of expectations. (The reality is uncertain and therefore it rewards risking less.)

3) That you can weather HUGE volatile swings. Note specifically that the cost in terms of volatility skyrockets as the benefit flattens out and then declines.

4) That fees are never an issue. Fees can make both very aggressive strategies due to volatility and very conservative strategies unprofitable.

5) That your edge remains consistent and results are normally distributed over time. If they are not, it favors more conservative risk.

6) That there is zero correlation between trades as correlation increases, the benefit for additional trades begins to decrease.

7) Probably some other things I haven’t considered.

With a finite time, increased risk skews results so that very few outliers of phenomenal results skew the “mean” results so the mean is greater than the median. The Distribution of Results at 1% is relatively normally distributed but with 3% risk, 5000 simulations of results are distributed like this:

avg

The simulation allows you to better understand the relationship of returns given that these assumptions are different by running simulations with different numbers. You can manage your expectations IF the results vary in either direction to better manage uncertainty.

So how you make financial goals should really contain in it not only a very deep understanding of the type of drawdown you want to endure, the awareness of the cost, but also contain the probability of a particular result with an understanding of the downside and so on. For example, your “goal” should really be considered after running some simulations and broken down to:

1) Probability of achieving goal (you may even consider probability of exceeding it by a particular amount)

2) Probability of breaking even

3) Probability of avoiding certain drawdowns at various levels

4) The Average Expectation of results

For example you might set the following goal:

1) 100% return or more

2) 90% chance of breaking even or better

3) Probability of a drawdown greater than 35% less than 10%, probability of drawdown greater than 20% less than 25%.

4) Probability of obtaining goal greater than 40% with a kill switch of 40%.

Given that goal, and GIVEN the expectancy of the system described, you can determine what position size meets those goals and if not what the tradeoff is (from which you might adjust your goal). You will then find that a 3% position size is too much, a position size of 1% is too small. So you need something between 1 and 3% 2% works but a 1.8% or 2.4% might also work.  It depends on if you’re more willing to take a better average and greater probability at expense of more volatility and greater probability of drawdowns, or prefer increasing the chances of not having a bad result and decrease the volatility and drawdowns. Alternatively, you might actually wish to define a range and be a bit discretionary with position size as long as it falls in the range in exchange for less awareness of what your odds of attaining your goals. Let’s say 1.8% is the smallest position size that will meet all of your goals and 2.4% is the largest position size. You might set a RANGE of position size, and then aim for 1.8% when your confidence is low and/or R/R and/or expectancy is lower than the average for the system, and aim for closer to 2.4% when it is higher. Additionally, you might also decide that there are certain instances of the market where you get a good oversold signal and market breadth has just flipped after a serious decline and you have a larger than usual edge across the board. You may want to position conservatively in all other instances so that you have the capital and ability to take on new aggressive positions when this moment comes aiming for maybe 20% of the year to make up 80% of your gains. There are many ways to break it down, but ultimately the combination of your system, your tolerance to drawdowns and volatility, your desired distribution of results, your need for awareness of expectancy and position size will help you to monitor and achieve your goals with the help of a position sizing simulator.

 

How to Really Set and Accomplish Your Goals

There is a story which says that there once was a man that prayed every day to his God to win the lottery. Years went buy until he was on his death bed having never won the lottery. When he arrived on heaven’s gate he asked God why he never won the lottery, to which he heard a booming voice from his God who replied, “You never bought a ticket”.

Goal setting is not just about “hope” and “prayer” alone. There are certain laws of the universe that exist and “thinking really hard” is NOT going to change them. “thinking really hard” MAY change your beliefs, which may in turn cause you to alter your behavior, form habits, which lead to certain tasks being accomplished in a certain way that may happen to produce the results you were looking for.
Einstein once said “God does not play dice with the universe”. There is cause and effect. It will serve you much better to actually recognize the necessary causes to produce the effect and plan to prepare those causes, ensuring everything is congruent with producing a particular outcome. I’m not saying the alternative of hope, prayer, and thinking doesn’t have a place, but there is no reason to rely entirely on thought alone, when there are proven methods to consistently deliver results on time.

There is a huge industry around “magical thinking” with the “secret”, the “law of beliefs”, and the “law of attraction”. Re-framing your beliefs can be useful. Thinking more positively so that you feel better and are more congruent with your behaviors can be useful… But it is far more useful as the last and one of many components that may not even be necessary at all that will help you achieve your goals, as opposed to just hope and luck.  It is a bit more work, but it actually leads to consistent and reliable results!

When it comes to determining what needs to be done to accomplish them, some goals can be easy. How do you increase earned income? Increase the hours you work or the amount you make per hour!

Some goals can be more challenging. Targeting exact percentage returns via stock trading can be a bit trickier, and take a bit more strategic planning, but they aren’t impossible to measure with the right tools.

But first, let’s show how goal setting might work with setting fitness/diet/exercise goals around a core goal of targeting a particular weight. What does that have to do with financial goals? More than you might suspect!

Your first step is to break the goal down. If you want to weigh 160 pounds and you weight 175 that is a 15 pound loss. That goal is incomplete; you need a time goal as well. You can easily translate that to lose 15 pounds in 10 months. That translates to losing 1.5 pounds per month or maybe .35 pounds a week.

Second, translate that into information that is actionable and use tools to help you. You can run a caloric calculator and determine you need to consume 200 calories less per day at the normal rate of activity, or else you can just plan on burning 200 calories through additional exercise.

Next, come up with a plan in your everyday life or encompassing strategy which incorporates all tactics and procedure to accomplish the task of having 200 calories less in, or 200 more calories out per day on average. Then you form a habit or habits around these strategies and you may need to eliminate old habits in the process.

Finally, your beliefs have to be congruent enough with the goal if they are not already. If you do not think of yourself as someone who deserves to be fit or have subconscious internal beliefs that you are inadequate, THEN it becomes a problem as you then may become overwhelmed with your own success and find a way to do what’s less unknown, and more comfortable which may cause you to fail.

Notice how I prefer to turn “the law of attraction” on its head and make it the LAST component, only after I’ve planned on how I will align all my strategies and habits with the goal.  After all, it may be that your beliefs and thought patterns are already congruent with success and that you just need to take action.

Okay, so what does that have to do with accomplishing financial goals through trading? The process for all goal setting is nearly identical. In the next article, you will see why.

How You Are Losing Money Without Knowing It

If you are trading stocks you are losing money and you don’t even know it. How? Mistakes. How much? I can only tell you how much money the average person is leaving on the table.

It is estimated that mistakes cost traders on average more than 4 times what they risk (in a single trade) PER mistake. (1)
What does that mean? Besides leaving lots of money on the table let’s translate it into facts?
If you are to place 20 trades a month or 240 trades a year and risk 1% per trade, and your profit on average is HALF of what you risk per trade…
then 1% per trade yields an increase of your account size by half a percent per trade.
Without mistakes and $15 per completed trade (buy plus sell costs) and 100k starting amount your gain is about 224.09% per year!.

BUT, what if every 20 trades (a trade a month) you made a mistake? Rather than gain one half of what you risked, you lose 3.5 times what you risked for a net cost of 4 times what you risked.
A 3.5% reduction in your account every 20 trades translates into a gain of “only” 98.19% per year!
In this instance, the gain cost you $125,900.30 with a $100,000 account! You would gain 63.52% more in a year without mistakes.
What if you make 2 mistakes a month or once every 10 trades? Now your 224.09% gain in a mistake free system in a year is down to a mere 20.93%. In this case, your mistakes cost you $203157.50 and you would have made a 167.99% increase in your final amount if you were mistake free and a 63.885% increase if you just cut your mistakes in half.

Nobody is perfect. I am not suggesting you can eliminate mistakes completely, but you sure can reduce them significantly.
If you are trading an account of 10,000, 2 mistakes a month makes the system lose about 15% a year due to compounding costs of commissions compared to gaining over 50% a year if you can reduce them to one average mistake a month.

Awhile ago, I was like you having just come across this realization. At some point, I decided I was going to make it my goal to reduce everything down to a science without compromising the strength of the system.

With a mechanical system that is as easy as hiring someone to trade for you or building a bot, or paying someone to program a bot to trade for you. However, some of the best traders are discretionary. I do not trade a mechanical system, but a discretionary one that allows me the individual skill of identifying setups. This method was mostly taught to me by the “Option Addict”. This guy made a fortune and have many trading members that can attest to that fact buying puts in bear sterns during the infamous collapse. He recently got me and several other traders in TWTR for 1000% gain the first go around, the same year he delivered a handful of trades that netted around 500% But his skills are not limited to options. Day in and day out he can identify a handful of stocks, many of which go on to mke some of the most explosive moves in the market. This guy is the real deal and I have spent at least hundreds of hours learning from him and thousands in my trading career trying to trade like him.

Nevertheless, I was determined to automate as much as possible. The first step was to build a position simulator that could evaluate and simulate a thousand trades and repeat that process thousands of times and complete a “Monte Carlo” simulation to evaluate the expected distribution of results given certain assumptions.

The next process was to build a spreadsheet that classifies stocks in several ways, then use those classifications to evaluate the stock uniquely based upon this criteria. The stock is first scored according to it’s own variables. Then the score is adjusted based upon the overall market and strength of its peers and rotation of capital in each of its multiple categories. Finally, based upon what is moving, the spreadsheet will anticipate based upon what is “next” to be “in phase” in a particular “cycle”, and rewards stocks that have timing ratings according to this.

After all adjustments you can use this list in one of two ways.
1)Automating your selection of generating a list of ideas from which to filter down into a small handful….
OR you can generate a handful of ideas on your own and only take the top few highest rated stocks to be added and watched.
Meanwhile, the spreadsheet can also tell you what sectors, industries, cycles, and other categories are currently “in favor” automatically.

In the process I built a spreadsheet that would automate and simplify the process as much as possible without sacrificing the quality. The process then can be redefined:

1)Identify top setups automatically for manual scanning to narrow the field
2)Hand pick the names I like to get maybe a dozen or two.
3)Objectively reduce the number of names by choosing the top 5-10 (or however many you choose) scored stocks after entering the names back in.
4)Enter in some more details (stop, target) and get a risk reward analysis and use that to further reduce the list or identify what sort of price you will have to get for each stock to be “on par” with the others.
5)set your limit/stop buy orders, or else alerts or watch and wait for the trigger.

The structure of the spreadsheet is done, but some minor tweaks will continue to be done to improve the product.

(1)Van Tharp http://www.vantharp.com/trader-test/mistakes-are-the-downfall-of-most-traders.htm

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.
industry

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.

 

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.

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.

Going Against The Great

baberuth
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:

lnkd1

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.

lnkdtwo

 

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)

How A Portfolio “Kill Switch” Can Change Everything About Long Term Expectations

We all most likely have some sort of psychological breaking point in which we would give up on the system, or in which we should probably re-examine our strategies, and in some cases, in which people go crazy and “break” to the point where they start revenge trading and no longer are actually trading their system. The prior risk management modeling I’ve done using a spreadsheet has not taken into account fees or how one handles draw downs. I have modified the spreadsheet such that there WILL be a fixed fee involved with trading (thus if you have a significant drawdown fees play a much bigger role as a percentage of your account), and I have added the ability to add a “kill switch”. Since I added fees at minimum, it’s necessary to have at least a kill switch of 100% (loss) so you don’t go on calculating $12 fees when your account is less than $12 and continue to go negative. In the meantime I set it up for 1000 hypothetical trades since we now have a more practical model I can simulate it for much longer time periods if need be.

So my hypothesis is people will give up after a drawdown of varying amounts. This drawdown is from the account HIGHS, not from the starting amount. Thus, for some people the end result after 1000 trading periods (whether they participate in all 1000 using the given strategy or not) will actually sometimes be better if they risk less to avoid this breaking point.

PLEASE understand that this simulator only uses one trade at a time as if you only play a single OTM option trade at a time and hold no trades simultaneously. In reality having more relatively uncorrelated trades at a smaller risk % per trade can often improve results at less risk, especially if you have multiple systems such as profitable investing and trading stocks and other asset classes in combination with your options. I will get around to testing partially correlated trades simultaneously and possibly entirely different systems in the future. For now the expectancy is based upon

p1 17%
p2 20%
p3 16%
p4 23%
p5 24%
w1 294%
w2 52%
w3 0%
w4 -64%
w5 -100%

My 2013 OTM option trading results at various risk levels and various kill switches to see a distribution of simulated results. For all accounts I will use a $10,000 starting portfolio and $12 for completed transaction (buy fee plus sell fee) just to use round numbers and so that fees is somewhat significant to the equation.

First let’s start with someone who is very risk averse and cannot handle even a 5% drawdown without feeling incredibly nervous and emotional and plans to quit at a 10%.

I had to think about the results and double check them at first, because I was surprisingly getting a significant skew right with only 1% risk.The mean is greater than the median. (i.e. the “average” is skewed by a smaller number of outliers such that the majority of the people are actually below the “mean”)

The reason was that at 1% with a 10% drawdown, you not only have too little capital at risk to the point where fees will eat away and a large percentage won’t get off the ground, but also that it is extremely common that at some point within the first 200 trades of those 1000 that you will hit the kill switch and very few will make it through all 1000 to produce significant gains which on average will be pretty high even with 1% risk (given that you can last that long). The overall average was still above the starting amount of $10,000, but in most cases, the fear of drawing down killed the traders hope of making money and taking risk. Increasing beyond 1% actually reduced average results because more risk = more volatility and greater probability that you will drawdown before giving your system a chance to compound it’s gains. 10% in theory is around “optimal” in terms of maximizing geometric return, but to someone who is risk adverse, it is probably less than 1%.

I then decided someone who is mildly risk adverse and doesn’t like 10% drawdowns, really starts to go crazy after 15% and ultimately feels compelled to shut down the account after a 20% drawdown. This was interesting because the average ending amount really increased from 1% to 2% but then decreased to from 2% to 5% to the point where you are better off risking 1% than 5% if you can’t handle a 20% drawdown.

I didn’t gather the histograms yet but here are the data points for 1000 monte carlo simulations of 1000 trades under each given condition.

drawdown switch 10p and 20p

30% and 40% drawdown was up next.

drawdown switch 30p and 40p

 

As you increase risk and become more lax on the “kill switch”, the worst case scenario gets worse, but so does the best case scenario. The skew actually becomes less noticable with a larger drawdown switch AND less risk per trade. Over the long run if you LET the trading system work, the results begin to normalize and cluster around the average. However increased risk increases the skew and depending on the drawdown kill switch may not improve even the MEAN average results. With these trades the approximate kelly criterion is 15% of capital but with even the risk aversion to withstand a 30% drawdown from high you are still best risking less than 1/3rd of the kelly.

 

So that brings us back to the kelly criterion graph which is entirely misleading if you are not aware of the other variables including personal aversion to risk, sample size, time period, etc. This graph is with the assumption of infinite time on your side because it was mathematically convenient. However, based upon some understanding of real data in a finite number of time given certain psychological barriers one has to cross among other things you know that for practical application even risking 1/3rd of this amount is incredibly aggressive when using an option based market strategy assuming one single uncorrelated bet at a time. We are also assuming KNOWN information and a fixed edge as opposed to a more uncertain one, both of which favor more caution. On the other hand, I believe I left a lot of money on the table and can improve my system by executing it and managing it more efficiently, and that eventually I will get around to testing how multiple trading systems work on the account. My theory is you can reduce risk and increase return by using complimentary systems (a stable consistent risk adverse system combined with an aggressive one both of which are relatively uncorrelated with the right mixture will compliment each other and both improve return and reduce risk while “normalizing the returns” over a finite number of time)

 

Hopefully this post is enlightening and helps you really analyze and understand risk, and I look forward to advancing my spreadsheet in the future to help me more thoroughly analyze a more multi-dimentional (synergistic) approach to risk.

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.
P1=.153025
P2=.213523
P3=.117438
P4=.263345
P5=.252669

W1=2.930585
W2=.5387795
W3=0.000000
W4=-.638143
W5=-1.00000

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
results

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

resultme

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.

 

1%

1p risk

2%

2p risk

2p risk2

5%

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.