tops 2014 bonds and equities v3
TRANSCRIPT
Targets of Opportunity System (“TOPS”)A Short-term, Long-only Approach to Trading Stocks and Bonds
Clark Collins 2014
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Contents
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2. Contents3. Introduction4. Daily Growth and Drawdowns5. Investment Highlights6. Portfolio Composition and Margin7. Frequency Histogram of Daily Returns8. Worst S&P 500 months vs. TOPS9. Monthly Comparison to S&P 500 and Correlation visualization10. Monthly Scattergram and Regression to the S&P 500 Index11. Stock and Bond Indices and Combinations12. Risk Management and Engineering and Testing Construct13. NO Fit Parameter Selection14. Chart of all reasonable parameter outcomes15. Trade illustrations Monthly16. Trade illustrations Detailed Weekly17. Conclusion18. Resume
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Introduction
Introduction
At present, the global financial markets carry tremendous uncertainty. Potential risk of loss due to “systemic failure” is ashigh as it has ever been, and today’s monetary policies have displaced much of what most of us have learned about financeover decades of general fundamental consensus. Will massive injections of future tax payer debt continue to artificiallysupport equity prices amidst the stark contrasts to a sluggish economy? What do investors do to protect themselves againstthe inevitable systemic risks? This is the same $100,000 question chased by investment professionals for decades.
My interest in constructing this project is an expansion of my long held belief that price action itself is one of very few inputsneeded to make appropriate investment decisions in the capital marketplace. Secondly, regardless of the systemic riskspresent I wanted to show that it is possible to construct an investment vehicle that can perform positively in the short andlong-term and with non-correlation when these price shocks across all asset classes reveal themselves
Using intraday price data , I have compiled a long-only (net of all fees) composite simulation utilizing six well known domesticfutures markets. Three of these individual markets utilize the “Equity-Stock” sector and the other three use U.S. Treasuryinstruments of different durations in the “Fixed Income-Bond” sector. These sectors/markets are some of the most liquidand transparent markets in the world.
Splitting a $ based risk budget equally and utilizing these traditionally non-correlated sectors, my summary shows the “TOPS”signal generations inherent strength which is to strategically enter/invest on measured pullbacks in the “Equity” marketswhile locking in short-term profits in the “Fixed Income” markets which were also previously invested on prior retracementsin its own measured pullback.
The "TOPS" signal generator is NOT built around a mandate that equity positions and bond positions cannot co-exist as onemight ask. The cross blending and alternating between these specific markets and sectors take place quite often as it logicallymakes sense in the short-term to have a sideways market where neither equities or bonds exhibits more directional pricemovement.
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Compounded Net Annualized 16.2%Annualized Standard Deviation 11.2 %Sharpe (2% r.f.) 1.26Annualized Downside Deviation 7.88 %Max Drawdown - 11.69 %Max Days to recovery 230 daysSortino Ratio (2% r.f.) 1.80Compound Annual / Max Draw 1.39
“TOPS” Simulated $1 NET Linear Daily Growth and Associated DrawdownsApril 2008 to January 2014
Investment Highlights
• Opportunistic and tactical. Predetermined trade entry and exit based on windows of time and related price action
• Utilize short-term technical and volatility trading concepts. 100 % automated. No discretion used
• Attempt to capture shorter-term directional movements which coincide with the larger trend or momentum.
• Tax efficient
• Sample Portfolio of six domestic highly liquid futures markets
• Annualized return target of approximately 15%-20% per annum with probable drawdowns no worse than -15%
• Annualized Volatility less than half of the S&P 500 Index
• Scalable and Custom risk/return to investor appetite discretion. Metrics enclosed risk/return chosen by developer
• Fund capacity well over 500 million U.S.D.
• Sharpe Ratio close to 1.5 with relatively short drawdown periods
• Low correlation to traditional domestic U.S. Equity as well as U.S. Bond Indices
A Technical Approach
• Confirmation of trend through short-term technical price highs or lows
• Momentum failure and short term technical breakdown confirms possible evaluation of position
• A Violation or contradiction of “major” technical levels negates potential “LONG” position
• Dynamic trade evaluation would be accomplished each minute and automated trade execution and trade reconciliation
production tied to FIX or API engine
6Portfolio Composition
Monthly and Equally Rebalanced Risk Budget of 6 Futures Markets in Simulation
ES – Mini S&P 500 Index (CME- “Globex”) FV – US Five Year Note (CBOT- “Globex”)
NQ – Mini NASDAQ 100 Index (CME- “Globex) TY – US Ten Year Note (CBOT- “Globex”)
YM - Dow Jones Index (CBOT-”Globex”) US – US 30 Year Bond (CBOT – “Globex”)
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20 Day and 200 Day Moving Averages of Margin to Static $1,000,000 PortfolioAugust 2008 to January of 2014
Average Margin to Equity 20.1 %Max Margin to Equity 33.7 %Non-Trading Days of Total Days 16.2 %
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Average Day 0.05 %Average Winning Day 0.42 %Average Losing Day - 0.44 %
% Winning Days 57.1 %Skew 0.70Kurtosis 7.78Min Day - 3.42 %Max Day 5.58 %
Frequency Histogram of Net Daily % Returns
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"TOPS" NET Daily % Returns with associated 20 day average of those returns20 day Average % Net Return 0.62 %Worst 20 day average Return - 7.94 %
-15.95% -15.64%
-11.72% -11.56%
-9.85% -9.54% -9.15%-8.15%
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S&P 500 TOPS
S&P 500 Index worst 10 Months with corresponding "TOPS" SYSTEM Net PerformanceOver the past six years, the average performance of all negative months for S&P 500 Index was -5.83% while "TOPS"
performance average of all negative months over same time period was a mere -1.99 %
S&P 500 average over worst 10 months -10.43 %"TOPS" same period 0.26 %
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Pearson Correlation 0.16
$1 Monthly Linear Growth Comparison of "TOPS" Program vs the S&P 500 IndexThe S&P 500 index has had some of its best several years in its history with no significant retracement to speak of since 2008. Even in this best
of performing several years for the index (2010 to current), it was half as efficient as the "TOPS" program producing 13.7% annualized return witha Sharpe ratio of 0.96 while "TOPS" simulated a 20.3 % annualized return and a relative Sharpe ratio of 1.98! (2010 to current)
S&P 500 Index Drawdown in excess of -50 % while "TOPS" runs slightly positive over sameperiod!
April 2008 to January 2014
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y = 0.3101xR² = 0.0255
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"TOPS" PROGRAM X AXIS
S&P 500 Index Y AXIS
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S&P 500 0.15
US 30 Yr Bond 0.07
Composite Stocks and Bonds 60/40 0.23
TOPS 1.22
Stock/Bond/TOPS Combination 50/30/20 0.46
Growth Profile of Stock and Bond Asset Classes, related Combinationsand risk adjusted by Sharpe Ratio
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Risk Management and Assumptions
• General information such as net asset value, portfolio composition and risk budgeting are a startingpoint of measuring risk. End of day equal reset of NAV also used on a per million basis per market.This was done to not permit one market to gain or lose too much versus another market which couldsignificantly outperform or underperform.
• Used a $10 per round turn charge per contract as well as $10 per round turn charge for any chargesincurred on spreads from one contract month to the next.
• 1.0 % management fee charged at 1%/12 months. 20% incentive fees charged per month on newportfolio equity highs only. No portfolio risk adjustments were made although this could beaccommodated for managed accounts hoping to produce higher or lower risks.
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* Parameter selection
One of the biggest challenges in financial engineering; when utilizing simulation constructs to measure future outcomes;is how to avoid fitting the data. The importance of this critical error must be avoided at all cost. Transparency of thecode is one way to reassure some, but even in those situations does the institution validate the concept properly, and notjust its risk attributes?
While constructing "TOPS", much thought was given to these important issues. Based on my own experience in thisindustry, I am keenly aware of the reluctance of financial firms to even consider models presented to them that are basedon simulation, regardless of their merit. Although I don't necessarily disagree with this philosophy, in today's world ofalgorithmic trading; a non-fitted quantitative strategy; that utilizes simple risk/reward rules; that is developed, presentedand understood properly; found to be blindly robust across multiple asset classes should bear some merit. Determininga robustness of signal generation is a science all to itself. It can be very time consuming and to be frank, it is still anassumption that the future will be similar to the past. Trading system evaluation expertise and the related processes usedtoday are scarce and most concentrate on the risk attributes of a particular strategy more than the efficacy of the strategyitself.
(continued)
Engineering & Testing Procedure
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Parameter Selection Continued:
In the attempt to address any fitting of data and a possible misrepresentation of results, I decided to use a cross sample ofall possible parameters in a population universe defined only by time. (1 to 7 days) I have already described what thegeneral premise that the signal generator looks for…….so the simplicity of the two parameter efficacy of the "TOPS" signalgenerator can also be measured.
The "TOPS" signal generation method looks for targets of opportunities utilizing two parameters. The first parameter isTime/Price(t). Utilizing short-term price action, I began to look at moving TIME/PRICE windows (t’) beginning with aone-day view out to one-week, stepping every 5 hours. That is the complete population of possible selections. As this is ashorter term trading model, to go out further would begin to correlate us to the longer-term market trend so I avoidedgoing out any further than one-week.
The second parameter is Time/Volatility(v) which I call vertical significance or y-axis relevance. Evaluated by (t’) themoving windows of prices, relevant targets of entry and targets of exit are determined by the scalable parameter ofvolatility. So beginning with half or (0.50 * (v’) ) in an associated moving window of time/price and extrapolating out to(1.0 * (v’) ) on the moving time/price window, I documented and averaged all the outcomes.
The multi-parametric results of stepping wide and utilizing *ALL parameter sets as viable trading options are on the nextpage of this brief document. As you can see, there were fluctuations in profitability by shift but overall the trading rulesshow positive results. *ALL of the statistical metrics exhibited in this document are the average of the entireuniverse of tested parameters. Some parameter pairs performed better than others and some worse as is expected.
There are certainly ways to determine which parameter sets overlapped others and muted the overall upside performancebut for the sake of producing a pure signal generation process undeterred by data fitting, *I have used them all.Bootstrapping techniques and Monte-Carlo simulations would also exhibit similar Risk/Return attributes.
* Certainly ALL is relative. Using all selections in the possible universe would produce many thousands of parameter sets and would theoretically produce very similar results towhat is presented here.
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60-0.90 90-0.50 90-0.60 90-0.70 90-0.80 90-0.90 COMPOSITE
Multi-Parameter Growth Simulations – One day to One week time frameApril 2008 to January 2014
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"ES" - Mini S&P 500 Monthly Scale Trade Examples60 min chart
Kick out of all possible long positionsmust wait for significant rally from new lows tore-evaluate future entry
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"ES" - Mini S&P 500 Detailed Week Trade Examples60 min chart
+ are buys for example chosen parameter- are exits for example chosen parameter
trades are not numbered to match up whichbuy and exit is affiliated
Conclusion
To summarize, the “TOPS” signal generator is a valid and reasonably simple process where neither parameter fitting norany type of learning, walk-forward process is needed to reveal very good results.
This signal generator and basic portfolio was also run over the same six year period on multiple financial instrumentsincluding currencies, metals, commodities, energy and individual stocks with positive and interesting results. Theseadditional tests on other asset classes is always important in my opinion to re-affirm I have NOT fitted a strategy to aspecial circumstance related to just one market.
Trading logic that can exhibit robust positive results across many asset classes is a good indicator that the trading rulesmay have real merit. I also ran these exact trading rules inversely to short markets instead of only buying and exiting.The results were for the most part neutral, but tremendous for those markets which have experienced sustained downcycles in price.
Over the many years of building, reverse engineering and tearing apart trading strategies such as Trend Following,Momentum, Pattern Recognition, Mean Reversion and High Frequency, I can say with confidence that this “TOPS”trading approach is very solid, would be relatively easy to implement and understand and will hopefully be of interest tosomeone looking for a non-correlated strategy with reasonable capacity.
* Further research can be produced as needed for further evaluation.