A Trader Journal

A big part of trading is about making decisions with incomplete information in an ambiguous environment and then managing those decisions towards the best possible favorable outcome. I don't mean just trade entries, exits, money management or trailing stops etc. The above applies to all other aspects of trading like market selection, trading research, setups to explore or employ, retrospective analysis etc.

Given the prevalence of decision making in trading/investing and our great ability to sabotage our decision making with psychological biases, I think having few structured techniques in our toolbox for good decision making is highly useful.
 
The purpose of this post is to put forth the idea of utilizing structured decision making techniques available in other fields in trading and to present an overview of 5 simple and effective decision making techniques.

Update: The post is updated with annotated charts. Also few changes to the content of the post.
 
The core idea of this post is to analyze fundamentals just like price action and see if that provides any additional value. Recently I added this functionality to my software for fun but now I think it might be worthwhile to investigate further for longer term trades.  

So which fundamental metrics do we use? Following are two fundamental metrics this post uses -

• Rolling (4 Quarters) Earnings Per Share
• Rolling (4 Quarters) Price/Sales Ratio.

Following are some observations from the below annotated charts -

• When EPS trend is down, the price action is either down or side ways. So if EPS trend is down (like in AMZN) but price trend is up, something funny could be going on like the stock running on stories/perception.
• At bottoms, the EPS trend seems to act as leading indicator. Near tops, the price is the leading indicator.
• Inclusion of EPS trend in price action chart provides a better picture of the context.
• Caveat - I have not yet verified quantitatively the above assertions are valid.

AMZN chart is interesting i.e., Rolling EPS trend is down (since ~ 2011 Q1) but the price trend is sideways to up. In AMZN past history (and other charts included in this post) there were no instances where EPS downtrend is down and price has sustained uptrend.
 
MSFT chart is also interesting i.e., its Rolling EPS trend was doing pretty well from 2006-2012 but its price trend is sideways. Market not recognizing its value? I can imagine reaction from TA folks to this question :-).
 
Note - Readers proficient in fundamental analysis are welcome to suggest other metrics for future posts. The requirement is I should be able to construct the metric from the data available in the Annual/Quarterly reports.
 
Question - What would be a good metric(s) to analyze markets (like SP500) from fundamental perspective? Any suggestions are welcome.


Wish you all good health and good trading!

Disclaimer: The above is not a recommendation. Please do your own due diligence. Also I change my trading opinions often as new information/insights roll in.

Often interesting ideas pop up when we look at same data but with different lenses. Many readers of the blog probably might be aware of Low Volatility Anomaly in markets. If you are not familiar with it and interested on that topic, then do a Google search for "Low Volatility Anomaly". You will find many articles, academic journal papers and explanations on that anomaly.

The low-volatility anomaly basically says portfolios of low-volatility stocks have produced higher risk-adjusted returns than portfolios with high-volatility stocks in most markets studied. Now often most of these low-volatility anomaly studies take one of the following two approaches -

Ranking-Based Approach:
In rankings based approach, the market or target segment (like large cap, small cap, emerging etc) is divided into deciles/quintiles based on a volatility measure. The division is such that securities in the lowest decile/quintile will be of low volatility. The portfolio is then invested in these low volatility deciles (or weighted heavier) and re-balanced monthly.

Minimum Variance Approach:
Another scheme is constructing minimum variance portfolios with the understanding minimum variance portfolios will have lowest risk. Then a weighting algorithm is used to determine the weights and limits for the selected securities & sectors belonging to that minimum variance portfolio. Then the portfolio is re-balanced monthly.

While the idea is good, I am not sure either of the above approaches are practical for individual traders unless one has large account and time. Also my personal preference when it comes to academic papers on trading is to generally pick the concept, understand the authors viewpoints, discard rest and figure my own way to incorporate those concepts for profitable outcome.

Capturing Volatility Premium:
IMO often good ideas come from simple rearrangement of concepts picked in various contexts over time. Applying that here, what do we know when it comes to volatility and these approaches - 

• Volatility in markets is mean reverting i.e., low volatility begets high volatility and vice-versa.
• A big part of low volatility portfolio returns is due to periodic re-balancing of the portfolio.

Most would probably know above. Now combining the above two, it seems to me basically low volatility portfolio profits are more to do with volatility harvesting then the actual volatility level. In other words, following low-volatility anomaly, one buys low volatility stocks for portfolio and then sell those stocks when their volatility is high. The latter happens indirectly because of the periodic re-balancing of the portfolio.

If that is true, then why not simply pick few broad market (liquid) ETFs,  buy when their volatility is low and sell when their volatility is high? 

Let's put above hypothesis to test. The broad market ETFs chosen for the test are - Emerging Markets (EEM, Europe (EFA), Asia & Pacific (EPP), US Small cap (IJH) and US Mid cap (MDY).

Some Notes:

• Average True Range is used to measure volatility here. There are other ways to measure volatility. The choice of ATR as volatility measure is mostly a matter of convenience.
• The test results are frictionless i.e., no slippage and commissions. 
• The test is done on weekly charts. Duration: 2000 - Current. 
• The portfolio is weighted equally across the 5 major markets. 

Results:
Following annotated images provides various performance stats. One can glean several insights both at individual market level as well as at portfolio level. Some highlights:

In the below image, notice the horizontal areas in the equity curve (black line) and the behavior of benchmark (red line) in those periods. The system goes into sidelines or has position only for a short time when the volatility is high in the benchmark. That is what we want.

The following image provides various  performance stats and ratios both for individual markets and for portfolio. The pie-chart provides the color notation. Notice anything of interest in "Annualized Sharpe", "Sortino Ratio" and "Rolling Correlation" bar plots?


The below scatter plot shows where individual markets and equal weighted portfolio  fall in annualized Risk-Return spectrum.

The last image provides detailed performance stats, calendar returns and draw downs etc. Looks like US Small cap has better returns of all whereas on risk-adjusted basis, the account seems to do better.

Now one idea doesn't make a system. The purpose of the test is basically to check for myself whether the hypothesis (i.e., buying and selling based on volatility level and price action )has legs and worth investigating further. The results are better than I expected for first round. The hypothesis seems to be worth investigating further. Thoughts?

I have not seen any low volatility anomaly studies on net that approach it this way. If you know of any studies/articles that discuss low volatility anomaly using approaches (besides ranking into deciles or using minimum variance) then please let me know.

Side Note: Like other tests on the blog, formulation of test rules, back-testing, analysis and visualizations are done using a proprietary software I developed over time. The software was built using R language and C#.
 

Each week I come across lot of interesting research and analysis through WallStreetCurrents.  So posting a list of articles I found interesting and informative.

Trading/Investing:

1. Volatility based Asset Allocation - The title is bit misleading. It is not about position sizing based on asset volatility but rather it is about using VIX as one of the filters to determine selection of Assets. Clear explanation and lots of performance stats.
2. Four Big Themes current going on - A summary of 4 big macro stories currently going on
3. How to set profit targets and control losses - Interesting article from Futures Mag. Talks about walk forward optimization and using MAE, MFE etc for determining profit targets and losses.
4. HP's Deal from Hell - Pretty insightful article from AswathDamodaran.
5. How much profit will Amazon eventually make?  - Interesting viewpoints from short perspective.
6. This trend is very worrisome for Apple - Good analysis on Apple mobile market share and viewpoints.

Technology:

1. Augmented Light Bulb Turns a Desk into Touch Screen - Pretty interesting idea.
2. Why Amazon thinks big data was made for the cloud
3. The cleverest business model in Online Education - The article talks about a startup called Duolingo that taps into crowd to make learning a language free.
4. Beyond Lithium Ion - ARPA E Places Bets on Novel Energy Storage - The article has also link to list of projects submitted to ARPA and grants they received.
5. Why Google's Ingress game is a data gold mine
6. MIT Researchers create tiny shape shifting robots - A interesting idea with 3 min video.

Other:

1. 5 statistics problems that will change the way you see world - Interesting problems especially the 4th and 5th one.
2. Weighing the Week Ahead - Summary of various stuff happening in markets.
3. New generation investors betting on Americas housing market
4. How does drawing improve children's mood?

Now I don't agree with all views. On other hand, I find it useful to read especially views that contradict mine. Please let me know in comments/mail if you found this post useful. If there is enough interest, I will post weekly WSC reads in future.

Wish you all good health and good trading!

This study is NOT about Sector Rotation. Continuing our series on playing the macro themes, this research is about utilizing the "Risk On-Risk Off" (instead of business cycle) for timing the Sector investments and to tactically switch between Sectors and Long Term treasuries. I have not seen any studies on the net which approach this way when it comes to playing Sectors. 

Note: For interested readers, this study lends well for applying Sector Rotation concepts as an additional filter. If you do, please drop me an email with your observations.

Coming back, this system basically involves three parts - 

1. Strategic selection of Sectors to cash on Macro theme
2. Tactical Switching between Sectors and Long term Treasury Bonds.
3. Timing the Sector Switches

For this study, the universe of available sectors to invest is S&P sector SPDRs. That doesn't mean one cannot use industry groups or a more granular sector groupings. I chose S&P Sector SPDRs primarily because of their longer price history.

Strategic Selection of Sectors - 

All sectors are NOT equally sensitive to interest rates. Same when it comes to inflationary conditions and future expectations about rates. So why not focus on those sectors that are particularly sensitive? So for this study, the shortlisted sectors are

• Housing ............................... IYR
• Energy  ................................ XLE
• Basic Materials ................... XLB
• Industrial ............................ XLI
• Discretionary Spending ...... XLY

Now I have not done any quantitative study to actually measure the sectors sensitiveness. I chose Sectors mostly on conceptual basis. For example, low/decreasing interest rates is good for housing. On other hand, materials prices will have upward pressure in that environment.

Tactical Switching between Sectors & Treasuries -

The study uses TLT as a proxy for Long Term Treasuries. Similarly this research uses  "Equal Weighting" scheme for allocating account capital. 

Note: Interested readers might also want to explore variable weighting scheme (like volatility based weighting) for account allocation to see if that improves results further.

The switching rules between sectors and treasuries are fairly simple. Following are the rules:

• Divide the account into 5 equal parts as per our Equal Weighting scheme. 
• Allocate each part (i.e., 20% of the account) to one of the above selected sectors.
• When timing rule to switch into a sector is triggered (rules given in next section), then invest the allocated part into that sector. 
• When timing rule tells us to switch to treasuries from a sector, then move the invested amount from that sector to the treasuries. 

Timing the Sector Switches -

 For timing the sector switches, this research uses both "absolute momentum" and "relative momentum". I think we covered in one of the prior posts why it is better to consider both types of momentum. So no point in going over that again. Please drop me a comment or mail if you have any question.

Following are Timing rules to switch between a given Sector & Long term Treasuries. The rules are evaluated over the weekend: 

(Switch to Sector) 

• Rule:1 -- Sector current week close is greater than the close 13 weeks ago AND the sector returns (percent gain) over last 13 weeks is greater than treasuries return  over the same 13 weeks. 
•  Rule:2 -- If above rule is met, then close the Treasuries position and switch to cash in coming week. After that switch from cash to that sector in following week.
• Note: One can theoretically switch position from Treasuries to Sector on same day but practically that is not likely. So this system assumes, there is a 1 week delay in between. Also that allows one to use discretion for getting better entries and exits. The test assumes all entry and exit prices are @ Monday Open price.

(Switch to Treasuries) 

• Rule:1 -- TLT current week close is greater than the close 13 weeks ago AND the TLT returns (percent gain) over last 13 weeks is greater than matched sector return  over the same 13 weeks. 
•  Rule:2 -- If above rule is met, then close the Sector position and switch to cash in coming week. After that switch from cash to TLT in following week.

Some Notes - 

• Usual caveats...Results are frictionless i.e., no slippage or commissions. Calculations are based on closed equity. 
• Duration: Jan 2002 - Current. (~ 11 years). Time Frame: Weekly.
• Account Initial Capital - $100k  
• Benchmark - SP500 Index.

Results -
Following annotated images provide various performance stats.  If the images don't convey information well then please let me know your suggestions/improvements.

My key takeaways from the results are -

• The concept of utilizing macro theme for sector switching and timing shows promise.
• The images provide performance stats at both account & individual sector level. Forensics on the latter provide some pretty interesting stats. See Housing, Discretionary and Material Sectors switches. I can guess logically housing sector switch out performance but have to think bit more about Discretionary and Material sectors out performance. Any comments?
• Low correlation of the account (as well as individual sector switches) when compared to the benchmark i.e., SP500 index. Makes it a good candidate for strategy diversification.
• Low drawdown. Makes it a good candidate to apply "Risk Parity" approach. 

Any Thoughts? Comments? Suggestions?



Disclaimer - 
The above study (or for that matter any thing on this blog) is NOT a recommendation. The study is not for live trading. It will need additional improvements and lot more testing before any consideration for live trading.

Just like markets, each trading strategy creates footprints for the discerning trade/investor to see and capitalize on it. The premise of this post is simple - Can we analyze and take advantage of our trading strategy footprints to improve the Returns, Sharpe and other performance metrics while reducing the draw downs?

The concept is not that complicated but generally many don't consider it. That included myself. I was using something similar but not same as what is covered in this post though  - a topic for a future post.

First we need a strategy before we can improve upon its performance. Any one of the numerous studies posted on this blog will fit the bill. But it is more fun doing a new strategy. So below is a strategy with rules to capitalize on a old concept - Turn around Tuesday

I think by now most people might have heard of "Risk on - Risk off" terms in media. Thought it would be interesting to explore this macro theme from various angles. This will take more than one post. What I have currently in mind is to explore topics like

• Risk switching to enhance popular portfolios like 60-40, Tobias, permanent folio.... 
• Risk switching to enhance market timing for short term technical trading. 
• Risk switching in Intra-Asset class instead of inter-asset class (like stocks & bonds). 
• Risk switching with Forex etc.

Following is an analysis on inflation regime shifts and what it means for asset allocation. It is a bit long article. Below has some fragments I picked from the article. Also a couple of graphs from the article (with my annotations) on asset classes performance in  different inflation regimes.  If you are interested in reading further, the link to source article is at the end of the post.

Over the past thirty years, inflation in the U.S. has averaged just below 3% per year. For many investors, we fear this extended period of price stability has created a complacency about the impact inflation can have on the returns of different asset classes.

But the events that have unfolded since the credit crisis of 2008 should challenge this attitude. The crisis sowed the seeds for the possibility of rising inflation. Central banks have increasingly engaged in unconventional monetary policy, and debt levels among developed market governments have ballooned. Monetization of government debt through inflation could be a logical result.

Further, we believe asset prices are much more sensitive to inflation outcomes relative to expectations than actual inflation levels – i.e., investors can react strongly when outcomes differ from expectations. Historically, inflation regime shifts have occurred with little warning. And once a growth spark ignites the inflation gasoline left everywhere by central banks (most recently the Fed with QE3), it may be too late to hedge the effects of inflation.

Therefore, now may be the time for investors who are concerned about inflationary risks to focus on increasing their exposure to asset classes that tend to provide a positive beta to changes in inflation.

While stocks and bonds have generally performed poorly during periods of high and rising inflation, a number of other asset classes have performed relatively well – including commodities, foreign currencies, gold and TIPS.  


 
Currently there is little in the way on inflation pressures with core inflation running in line with its average of the last 20 years. While it is hard to say with certainty when inflation will move higher, we can identify some of the potential catalysts. 

• A commodity supply shock, such as the closure of the Straits of Hormuz or widespread regional unrest in the Middle East, is one near-term catalyst that could move inflation materially higher. Recall that in the inflationary episode of the 1970s, it was the Arab Oil Embargo in 1973 that caused inflation to double from 5% to 10%. 
• Increased demand and decreased level of unemployment. As this happens, the Fed will be faced with making a tradeoff between the two components of their dual mandate, price stability and full employment. It is in making this tradeoff during the coming economic recovery that we see the catalyst for inflation. The Fed may err on the side of seeking greater employment and a stronger recovery, believing that temporarily higher inflation can be reversed.  
• Central banks globally have been engaged in a series of unconventional policy measures and competitive currency devaluation.   

Source Article: Inflation Regime Shifts

One of my daily morning rituals is to pour myself a nice hot cup of tea, sit in warm morning sun rays and flip through WallStreetCurrents headlines. In recent months, I pretty much stopped looking at other sources besides WSC. For me WallStreetCurrents kind of became a fast and efficient way to keep tabs on markets, viewpoints and more important a continual source of new trade/research ideas. 

Anyway, so I was flipping through WSC  and when I came to Quant Currents, the first headline that caught my attention was "Dual Momentum". Basically it is a post from Gary Antonacci about his new paper - "Risk Premia Harvesting Through Dual Momentum".

I became a fan of Gary Antonacci work when I read his prior paper "Risk Premia Harvesting Through Momentum".  I think the Risk Premia papers methodology will be more robust when compared to some of the other popular TAA and AAA papers.

One problem in general with popular papers on TAA and AAA is the reliance on volatility as a proxy for risk. To me, Volatility is NOT same as Risk. Volatility is just an up and down movement and is a good source of profits. Similarly I find volatility targeting though good, the performance differences seems to me is less to do with targeting and more to do with volatility harvesting. I feel there are other ways to do volatility harvesting while treating risk in absolute terms like draw down etc, % capital etc.  How many customers decide to stay/leave a fund based on sharpe, volatility etc compared to absolute metrics like % of their capital loss or gain?
 
Coming back, following is an abstract of Gary Antonacci new paper. I will post my analysis and thoughts on the paper methodology in coming days. If you cannot wait,  the link to full paper is at the end of the abstract.


Momentum is the premier market anomaly. It is nearly universal in its applicability. Rather than focus on momentum applied to particular assets or asset classes, this paper explores momentum with respect to what makes it most effective. We find absolute momentum to be more effective than relative momentum, but that combining the two gives the best results. We also explore the factor most rewarded by momentum - extreme past returns, i.e., price volatility. We identify high volatility through the risk premiums in foreign/U.S. equities, high yield/credit bonds, equity/mortgage REITs, and gold/Treasury bonds. Using modules of asset pairs as building blocks lets us isolate volatility related risk factors and benefit from cross-asset portfolio diversification while using a combination of relative and absolute momentum to capture risk premium profits.

Link: Risk Premia Harvesting Through Dual Momentum

One of the first things traders learn (often hard way) is there is no absolute right and wrong approaches when it comes to profiting in markets. Please feel free to let me know your views. We learn more when our view differ. So the more our views differ the better.

Wish you all good health and good trading!

We hear often about high correlations in stock market but not much about ETFs as a driver of high correlations. One would probably come across more media/blog bytes on Risk On-Risk Off etc than ETFs impact on correlations. 

ETFs had $1.2 trillion in assets under management in early 2012 and is one of the fastest growing segments. So it is likely that ETFs continue to accumulate more assets under management and along with that increased impact on underlying asset prices as well. 


When shifts happen, some adapt while others fight it . I think one way to check whether a methodology is fighting or floating with this ETF tide is to check for things like - (a) are the new opportunities sparse/decreasing relative to past? (b) does the methodology require lot more complexity to accomplish same thing that in past was simple? and (c) are the profits more harder to come by relative to past? If answer is Yes then I would imagine the   methodology and trader are fighting the tide. Please feel free to disagree/comment.
 
Recently I came across an interesting paper on ETFs and Asset return correlations. Following are some highlights from the paper. 

Why ETF's drive the asset correlations?

• ETFs have a greater potential to affect asset correlations than mutual funds for several reasons. First, traditional mutual funds have some leeway on where to invest their money, and must typically keep some cash on hand for redemption. ETFs, on the other hand, are created in units which must contain the appropriate portfolio. Each time a unit is created or destroyed, the stocks in that ETF portfolio potentially trade together.
• The second reason that ETFs can drive correlations is the arbitrage that they make possible between the price of the ETF and the price of the underlying basket of shares. Arbitrageurs are likely to favor ETFs because, unlike mutual funds, they are easy to short and quick to trade. Now when the basket of shares is bought or sold together for arbitrage purposes, this places demand on all of the stocks together, which in turn increases correlations.
• ETFs, by making it easier to trade stocks with similar characteristics for investors, they acerbate co-movement among stocks that share similar characteristics. By similar characteristics, I mean like size based (small cap, large cap...) or style based etc.

Findings:

• The paper finds that the more an ETF owns the market capitalization of stocks in its portfolio, the more the stocks in that ETF portfolio tend to move together in the subsequent month. 
• An ETF's turnover is another strong determining factor in driving the correlated movement of stocks that make up its portfolio.
• Another finding from the paper is the more a stocks market cap is owned by ETF's, the more that stock co-moves with the market in the subsequent month.
•  Similarly the weighted average turnover of ETF's that owns the stock is a strong factor in driving the co-moves of the stock with the market.

I would imagine some would agree with above views and others won't. We learn more when our views differ. So please feel free to let me know your views. I have not yet figured on how to create a quant test as well as capitalize on these findings. My one gripe is the paper could have chosen better metrics for results and also presented in a more reader friendly manner.

If you are interested in reading the full paper, following is the link to the academic paper - ETFs and Asset Return Correlations

Wish you all good health and good trading!

I came across this viewpoint which was scheduled to appear in Winter 2013 issue of Journal of Portfolio Management. You can find the link to article at the end of the post.

The core idea of the article is to see portfolio risk as a seamless continuous curve composed of 4 distinct regimes. These risk regimes are identified by the potential size of the losses a portfolio can incur. For example, one way to define these 4 risk regimes is (0% to -5%), (-5% to -15%), (-15% to -35%) and (-35% to above).  If we approach our portfolio risk management this way then a logical next step is to adjust/use appropriate risk management strategies as the portfolio risk transitions from one regime to next regime. 

For example, for smallest market fluctuations (i.e., 0% to -5%), one approach to manage portfolio risk is by doing dynamic balancing like volatility targeting i.e., regularly balance the asset (stocks, bonds, cash...) allocation in portfolio such that total portfolio volatility is within a predetermined target.

Four modes of risk management

Now to protect portfolio against losses in 2nd regime (say -5% to -15% losses), one effective approach seems to be finding alternative assets to re-align portfolio exposure. What I understood is basically expand the portfolio to have more diversification in terms of strategies and alternative exposures. Feel free to correct if I got it wrong.

To protect portfolio against even deeper losses i.e., 3rd regime (say -15% to -35%), an effective approach is to explicitly hedge the tail risk via option-like strategies. That makes sense as basically by using option like strategies, one is out-sourcing the risk. 

Also it makes sense to use in this regime and not in earlier regimes as portfolio incurs cost in implementing this approach.  Note: The risk management strategies to handle earlier regimes also has costs like whipsaws/giving up on gains for additional diversification in regime-2 or jump costs when doing re-balancing in regime-1.

One challenge is losses don't really care about our regimes definition and can seamless move from one regime to another making all above approaches to manage risk useless. Another big challenge is future is unknown. The reason I found this article interesting is it provides a framework to consider portfolio risk and to craft ahead a plan on how one can go about managing the portfolio risk and surprises. Your thoughts?

Link: Four Modes of Practical Risk Management

Markets are always changing. Some key questions every trader has to answer irrespective of their approach is - how is my trading strategy doing currently? Is the market currently conducive to the strategy for pressing the edge or to reduce the exposure? Under what environments  my method will shine and in which environments will it run into rough seas? Is the edge gone permanently or is it just a temporary draw down?

Interestingly we don't hear much in trading literature/blogs about this aspect and techniques one can use. If you have heard, then please let me know. I like to read. 

Anyway, in my opinion, a strategy performance is dependent broadly on three factors:

1. Suitability of current market environment to the method.
2. Suitability of the money management algorithm being used to the method.
3. Suitability of the trader (nature/personality) to the method.

This post focus is on first item i.e., getting an idea on whether the current market environment is conducive to the strategy and in what environments will it do well. Similarly when to pull the plug on the system and shelve it or revisit its core logic. As an example, I am using the strategy we covered in prior post. If interested, you can find more details of the system here and here.

In my opinion, having a simple and objective process/rules for doing this analysis makes a big difference both to the account and to trader's health. Often the aspects of trading that causes stress are those areas that are not simple and crisply defined to follow repeatedly.

I am sure there are an alphabetical soup of quant/statistical tests (i.e., A - Z tests) one can perform to determine how the system is doing currently. Similarly another alphabetical soup of adaptive approaches to side step this problem. I could be wrong but IMO the problem with adaptive approaches is they will satisfy intellect more than the account, adds lot of complexity to strategy and then somehow magically hit the one case we forgot to consider. Do you know of any adaptive strategies that withstood last decade and performed well?

The approach I use is fairly simple, effective and objective. I don't know of any quant tests that can do better than the approach I use currently. Doesn't mean there are no better approaches out there nor this approach is the best. I am sure there are and look forward to investigate. I welcome readers to share their thoughts and techniques.

I think there is wealth of information one can gain from a simple performance summary chart. To get better mileage for you and for me, I recommend readers to take a deep look at the 1st image for couple minutes and note down what comes to their mind about the system character. Then look at the 2nd image which is heavily annotated with my observations.  Then please let me know where our observations differ or things I overlooked/mistaken. If enough readers do, it will be beneficial to all.

Profiting from emotions price action strategy performance

Note: Read the annotations in the order they are numbered. These will set the path to the final question i.e., how is the trading strategy doing currently? is the edge still there? when can one press the edge for this system? and finally what are the the red flags to watch for that will let me know the system edge is in danger. 

Profiting from emotions price action strategy performance with annotations

Please feel free to share your thoughts, any techniques you found useful and also any inconsistencies in my analysis. We learn most when our views and ideas differ. I hope readers got some useful takeaway from the post.

Wish you all good health and good trading!

If your platform is not Ninjatrader then probably you can skip this post. All the quant stuff on this blog are done using Ninjatrader & R. For last few days I was trying to get Ninjatrader software handle following scenario. I feel the scenario is fairly common and the solution will be useful others. So posting it on the blog.

Scenario:
I have couple strategies that works off daily and weekly charts. Now I would like to use the strategies for live trading. But I am NOT comfortable with software placing orders automatically. Also I would like to use some level of discretion. So my desired workflow is:

• Each day wait for US markets to close for the day. Then start Ninjatrader and connect to an EOD data feed (Example: Yahoo/Kinetic etc).
• Select my strategy and run on a pre-defined watch list in Strategy Analyzer.
• The strategy executes on the watchlist and generates list of orders (buy/sell/short/cover) with details. I either take a printout (or save in spreadsheet) to trade manually the following day/week.
• Repeat the above step for other production strategies & watch lists.

Last post covers a simple price action strategy to profit from crowd emotions and some stats on it by market regimes. You can find the post here. It is easy to either skip that or move on to something else after quick read because it is too simple.

This simple strategy had beaten buy-n-hold by a wide margin overall in last 17 years. CAGR of 10% is good especially given the short time the strategy spends in the market. IMO time is one of the safest risk control a strategy can have. You can see for yourself the results, consistency and other ratios etc in the following two images along with my annotations.

Strategy - Equity Curve, Weekly Returns, Drawdowns

Strategy - Performance Analytics

Currently the max drawdown of this method is 20%.  That is too high for my comfort. One of my reasons for sharing this method on the blog is to hear your thoughts and suggestions on ways one can reduce the draw down of this strategy. Any suggestions?

Wish you all good health and good trading!

Many people approach the market with assumption that only ideas that are complex or arcane can provide an edge in the markets. Unfortunately most trading books and vendors promote this assumption to sell their own services. Would you buy/subscribe otherwise? Another culprit is the notion that the more complex and intellectual a method or concept is, the better it is. Typical justification, otherwise everyone would have figured it. Another reason - intellectual addiction.
 
Sometimes simple things can provide an edge. Partly because they tap into simple basic emotions we all have as humans. For example, remember the last time market (SP500) had a strong sell off and closed near its lows. If you are visual, think of a large big red bar. What was your thought pattern and reaction? What sense did you get from financial media and popular blogs?

My guess is in both cases, it is a negative emotion followed by intellectual/logical reasons to justify market sell off and continuation of it. We know majority people are bad at timing. So how about taking other side of the crowd? This is where the price action research of this post comes in.The purpose of the study is to see what are the market returns when we follow the crowd vs when we takes opposite side to the crowd. 

Using SP500 (SPY) market as an example, one simple way to define crowd behavior for the day is based on the market close with respect to its high, low and close of the day. There are multiple ways to identify but for this test how about we define crowd behavior as follows -

• The location of the market close with respect to its high of the day provides the emotional temperature of the crowd for the day. The farther the close from high of the day, the stronger the emotional temperature of the crowd.
• The location of the market close with respect to its open provides the emotional sign of the the crowd for the day i.e., down day = negative emotion, up day = positive emotion.
• To quantify above, let's divide the day's range (i.e., high - low) into 4 quartiles to identify in which quartile the market closed for the day. Following image provides a pictorial description of what I mean.

Definitions:

• Bull Market - Market is above 200 day moving average. 
• Bear Market - Market is below 200 day moving average.  
• Graphs Annotation - In below graph, on X-axis, label 25 means 0%-25%. Similarly label 50 (25%-50%) , label 75 (50%-75%) and label 100 (75%-100%).

Close_Test:

• Compute the quartile for today's SPY market close i.e., (0-25)  or (25-50) or (50-75) or (75-100)
• Buy market @ open the next day.
• Sell market @ open the following day.
• Calculate the performance stats.

Note: Another interesting test would be to buy @ close today near end of the day (instead of buying @ open the following day). This can help one to get in on overnight action. I assume most of this blog readers are likely end of day traders. So for the test, I am using buying @ open the next day.

Bull_Market_Test:

• SPY market close is above its 200 day moving average.
• Compute the quartile for today's SPY market close i.e., (0-25)  or (25-50) or (50-75) or (75-100)
• Buy market @ open the next day.
• Sell market @ open the following day
• Calculate the performance stats. 

Bear_Market_Test:

• SPY market close is below its 200 day moving average.
• Compute the quartile for today's SPY market close i.e., (0-25)  or (25-50) or (50-75) or (75-100)
• Buy market @ open the next day.
• Sell market @ open the following day
• Calculate the performance stats. 

Misc:

• Test Duration: 1996 t0 Current
• Friction less results i.e., no commissions, no slippage.
• Long only trades.

Fading vs Following Crowd Emotions

Results Analysis:
My observation is in general it takes lot more time to write blog and generate plots compared to time it takes to create a test or analyze market. I already spent lot of time on this post to provide something worthwhile and results are fairly clear cut. Look at the annotations on the graph - 

• Specifically the results of 75%-100% quartile for all three tests. 
• Results of this top quartile under bull and bear market regimes.

Note:
The above is not a system nor it is a recommendation. Just a research into of one of the market characteristics. Feel free to let me know your conclusions from results. Also feel free to agree/disagree. 

Wish you all good health and good trading!

"There is just one life for each of us........ Be yourself!"

This study is about bull and bear markets (S&P 500) performance by VIX regime. For the test, I used SPY etf as the proxy for S&P 500. 

Definitions:

• Bull Market Phase - Market is above 200 day simple moving average. 
• Bear Market  Phase - Market is below 200 day simple moving average. 
• VIX Regimes:  0-15 (low volatility), 15-30, 30-45, 45-60 (high volatility)

Bull Market Test

• Market is in Bull Market phase.
• Go long when market transitions from previous VIX regime to new regime.
• Exit long when market transitions from current VIX regime to next regime.

Bear Market Test

• Market is in Bear Market phase.
• Go short when market transitions from previous VIX regime to new regime.
• Exit short when market transitions from current VIX regime to next regime.

Misc

• Test Duration: 1996 t0 Current
•  Friction less results i.e., no commissions, no slippage.
• Long only trades.

Results Analysis - Bull Markets: 

1. Profitable in all 3 VIX regimes. The Trades category provides an idea of how many times the market entered into a particular volatility range.    
2. The Win% is highest in VIX range 30-45. But the number of time market entered into that VIX range is relatively less.  
3. Average Win/Loss Ratio and Average trade returns are highest in VIX range 0-15. I wonder if it is because of low volatility anomaly in markets?

Results Analysis - Bear Markets:  

1. As expected, results show bear markets are more volatile than bull markets. Unlike bull markets, bear markets entered VIX 45-60 range multiple times. 
2. Long trades are profitable in the volatility regime 30-45. Not sure Why?  
3. Another is high average win/loss ratio in volatility regime 0-15. Why? 

 Feel free to let me know if your conclusions from results is different from above. Also I am curious to hear your thoughts on above 3 questions.
 
Note: The above is not a system nor it is a recommendation. Just a study of one of the market characteristics.

Today while scanning through WallStreetCurrents site, I came across a post on new Volatility ETF (VIXH). What caught my attention in that ETF prospectus was its rules based on VIX levels for buying VIX options. Thought will check out how those VIX rules would fare if I apply it on SPY.

Now rather than blindly buying SPY at each VIX level, thought I will combine with another study I am checking currently i.e., week of the day effect on SPY. (Note: If any readers are interested in pure VIX level based entries test then please let me know. I will do in one of the future posts).

Test:

• Divide VIX range into 4 levels : 0-15, 15-30, 30-45, 45-60. (Note: My levels are slightly different from ETF but that shouldn't make much difference).
• Buy @ market next day open and sell after 2 days. Note: Only one position at a time. Next position is opened after the current position is closed. I think this condition is more realistic.
• Finally tabulate the performance metrics categorized by VIX level and Week of the day.
• Test Duration - 1995 to 2012 Current. Caveats - Results are frictionless i.e., no slippage & no commission.

SPY ETF - Week of the day profile by VIX regime

Results:
Some takeaways

• Poor performance of longs when VIX level is above 45.
• Low performance of longs when VIX level is below 15. But draw downs are also low. So may be risk parity approach to increase the returns.
• The sweet spot seems to be to go long on SPY only when VIX level is between 15-45. 

In the ETF prospectus, rules related to VIX level are as follows:

• VIX futures less than or equal to 15, no VIX calls are purchased
• VIX futures above 15 and less than or equal to 30, 1% of portfolio in VIX calls
• VIX futures above 30 and less than or equal to 50, 0.50% of portfolio in VIX calls
• VIX futures above 50, no VIX calls are purchased

Your thoughts?