From wikipedia some formulas that I cannot use (formula illiterate)
cheers
David
I’ve got an interesting addition to this list which many of you will scratch your head and say - “what is this guy thinking”!!
Moneyball - by Michael Lewis. Every since I read that book, I started thinking more and more about what drives a stock price up (Value creation on the same thought process as “Runs Created”).
Has anyone read Data Driven Investing - Professional Edition by Bill Matson? Any comments?
http://www.mechanical-investing.com/data-driven-investing.html
Recently reading some outstanding books on how to systematically develop and test trading systems:
1. Thomas Stridsman:
(a) Trading Systems that work: Very good book, with a whole chapter devoted to how to measure goodness of a model and then few chapters with example technical trading systems and followed by systematic analysis. Especially liked the idea that predicability of a system is more important than its profit potential. (In other words, strive first for systems with smaller standard deviation of returns, then look for max return). This reinforced the view I once read somewhere: Novices chase returns and risk eats them while professionals carefully look at risk and returns take care of themselves. The book systematically looks at the key parameters that can tell us the goodness of a system.
(b) Tradings systems and Money Management: is a followup book with more detailed analysis but the first one is very good.
(c) Stridsman’s 10 rules from Global Investors rules book capture many of these ideas in concise prose.
10 rules
2. Weissman - Mechanical Trading systems: has an excellent chapter on how to avoid overoptimization and deal with outliers etc. Has very good coverage of pschological issues in why people tend to stray away from their trading systems recommendations.
3. Tushar Chande - Beyond Technical Analysis Second Edition
Outstanding book, I would say the best primer on Trading systems and the most concise and mature and data-driven approach. Surprisingly, the first edition is good too, but does not have good recommendations on Amazon. If you had time to read just one book, this is the one. (I am not in any way related to Tushar 
This is definitely one book that will teach you how to think about testing your system.
4. O’shaughnessey: Invest like the best (is from 1994) but very fascinating introduction to how he went about reverse engineering the portfolio parameters of successful mutual funds. His latest third edition of “What works on wall street” gives some very good insights about market cap and about single factor simulations. I am sharing this because this is closer to the fundamental-ranking and yanking approach and reading it can provoke independent thinking.
OK, when rubber meets the road, what new insights did I get from these books, you ask.
Before I get to that, what are your answers to the following questions?
(a) Given two sims, which is better, one with more turnover, or one with less?
(b) Is a sim with 55% win rate good enough to make money?
Is a sim with 80% win rate prefered to one with 70%?
(c) My sim has a rule Mktcap > 100 and MktCap < 500. Should I use it? Why is this giving better returns than without the rule?
(d) Is a 5 stock sim better than a 10 stock sim? or vice versa?
and
now for the toughest and probably the most important question of all:
(e) How will you know if your model has stopped working? How can you distinguish whether what is happening is another drawdown or whether it is a breakdown because at least some market rules it exploits have changed?
My takeaways:
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Dont just look at annual return, look also at std deviation of returns. Very unusually high returns can also be caused by curve fitting
(tuning the system ie changing its rules until it manages to pick the past winners). Do not tune rules to get rid of one loser here, one loser there. When you change a rule, it should improve most of the performance most of the time. Often, deleting a rule, accomplishes this. Which leads us to next one. -
Fewer the rules the better. Every rule, every filter on the market and stocks, reduces the degrees of freedom in your system. For instance if your model works well only for small caps and is below market for large caps, then probably your model is fragile and likely to stop working for small caps, unless you can clearly explain to me why it works for small caps and why it can never stop working.
This also leads to other idea, rules that make sense are better than rules that use numbers like Rank > 99.25. Also, test each of your rule with a ± 10 percent increment, it should still work well, even though it may not be the best. For instance if your ranking systems makes you 120% annual return when Rank > 99, For Rank > 98 and Rank < 99 it should make at least 80% and for Rank > 97 & Rank < 96 it should make at least 60%. Why? You can never guarantee that a real 96-rank stock might be misranked as a 99 or vice-versa.
Buy Rules limit the universe of your choice, your catchment area, so for some reason, if that area dries up, you are left out in the open. It is like a lion deciding, I am going to eat only rabbits. All-weather sims are better than market specific sims, because long-term consistent profits is the key to growing and preserving the gains.
At least understand your restrictive buy rules and make sure you have only very few of them, and with good reason. Sell Rules, I think, can be more and might be the key to consistent long-term profitable systems.
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More the trades in the sim, the better you can trust it, as far as a mechanical system is concerned. If a sim makes 500 trades in the last five years, it means it has picked up 250 stocks during different market cycles. The % winners, average gain expected, worst lost, drawdown and almost every statistic is more reliable and repeatable with this sim, than
a sim that has rebalanced only once a year. (Turnover 100%).
In general, of course.
This has been mentioned many times in this forum by Brian, Denny and others: The more trades a model makes, more reliable it is. In other words, like casinos, systems need enough trades to get the statistical edge.
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Good models should work comparably well in all market cycles (every quarter, say). For instance, a system that does 200% return in 2003 and 150% in 2004 but 25% in 2002 is not that great.
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Predictability is more important than profitability. In other words, first strive for a system that gives good returns consistently rather than great returns once in a while.
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Same rule leads at the individual trade level. Make sure your returns are not due to few big winners, but due to lots of small profits and the elimination of big losses. Stridsman says it well: Strive for mediocrity. One of the forum members wrote this once very well: You cant count on these rare winners happening again, so remove them from simulation. In fact, thinking along the lines led me to cutting your winners short to make sure your system does not depend on such big winners to become profitable.
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I am looking a lot more into the trade statistics now. Especially win rate, average holding period, av gain and av loss
and gain/day and loss per day and experimenting with using these to set the sell rules. Actually, I am comparing these with Rank sell rules. I am experimenting with NOT using Rank to sell. -
Closely look at historical draw downs. Even if your sim had only a 22% drawdown back in 2002, try to walk through that closely week by week. Can you see, for instance, that it took 7 months for the portfolio to reach back to where it was? Do you think you would faithfully trade that sim week after week during those 7 months?
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The most important thing is to be able to detect that your model is losing its edge. This is also probably the most difficult thing. Though after a lot of thought, it seems possible. The way, it seems to me, is to look at things like, how often do you get 4 consequitive negative weeks? What is the probability that you will get 4 days of 3% dropping in a month? How often can you expect 5% drawdowns? 10%? 20%? In your sim, are the worst drawdowns early in its life or in recent times? If you have meaningful study of these, and assuming that your sim is a tamed and well behaved machine churning out small profits and small losses at regular intervals, then you can learn to ignore ‘expected drawdowns’ and be alert when the behavior of the sim is deviant. (Finally, it is all about mean-variance and standard deviation, My Dear Watson!)
Without such a study, you will not have the reasoned faith to follow your portfolio during its casino-runs, which you interpret to be coaster rides.
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Bottom line: Mechanical investing is more profitable than discretionary investing, provided you
(a) absolutely convince yourself through testing that the system is very reliable and
(b) follow every signal without exceptions, without trying to improve upon the returns of your model. The most hurting tendency is to selling winners earlier than the model says, hoping to catch them on a decline, but then not doing it and seeing that they keep moving up. Sims and successful discretionary traders make money by having on an average three kinds of trades - big wins, small wins and small losses.
Even though one should not trust sims that make 60% of their money from the big wins, I do think at least say, 30% of the money should come from top winning trades. A few of them are needed to wipe away the losses and give an extra edge. By selling them early, this opportunity is lost, leading to only having small wins and small losses. (The other even more harmful and the most prevalent tendency of letting losers stay for long to become big losses (anything more than 2% of overall starting capital per trade), I assume, most of us have overcome. If not, send me a Selfaddressed envelope (so I can share my experiences).
Consistency in strategy (no matter whether it comes because of you have what Buffett calls temperament or because of following a trading system) and making many many small bets and playing for the long run are the keys to making money. The exact opposite behaviors of chasing new tips, strategies and systems, making too large bets that either way shake up emotions leading to overtrading and oversize bets and dropping in and out of the game when one feels like it are the keys to losing money which all new-comers are blessed with and most traders and investors may not overcome in one life.
My appreciation for portfolio123 has tripled - (you can see how I have started thinking in quantitative terms 
- after reading all the books, since this is one platform that can (a) help you get the statistical edge using FUNDAMENTAL analysis and (b) help you realize that potential with a trading system simulation and (c) test your hearts out with real data.
I apologize for having said so much of my personal reflection in this thread, and realize that for many of you reading this, this must be child’s learning (‘Welcome, Ravi, Who said the markets are easy?’). Still I wanted to share for those who might benefit.
Ravi
One other insight was that money management (deciding how much dollars to buy each stock or how much to bet at one time on one thing) is much more important than (a) stock selection (b) entry price (c) sell rules. In other words, what we need from the ranking system is a small but very reliable statistical edge.
Even though portfolio123 makes money management very easy, it makes the implementation easy, not the design. For example, 3 stock systems and 4 stock systems can give huge returns for short periods but will decay sooner than later or they swing too much drawdowns that we stop following them.
Money management includes several other things, such as starting capital. For instance, a sim, which averages 70% annual returns when started with 100,000 portfolio, drops to 40% when started with a 10000 portfolio. Why? In the first year or two, commissions steal away most of the returns! In fact, if you start with $2000, this sim may never see the light of the day! Power of compounding working against us!
This actually led me to experiment for the first time with a fixed amount investment. For instance, given a 20000 dollars capital instead of specifying 5 stock sim (by choosing percentage 20%), choose fixed amount and specify $5000. First year the sim buys 4 stocks and as it makes more and more money, in five years, it makes enough money to buy 30 stocks spreading the risk over many stocks, limiting drawdowns.
Bottomline: While we must look for the holy grail ranking system (which means that its top rankers immediately turn profitable and grow the fastest in a short time and top rankers never ever lose money), there might not be such a measuring stick that works forever.
Instead, we might want to equally strive for a frictionless or lossless money managing strategy (which is deciding how many stocks for what amount and how to cut losses and how to let profits run and when to cut profits short) - as good as possible so that even with a suboptimal ranking system (as measured by % winners), the net results are fairly good and more importantly very very consistent. In order to do this a lot of experimentation has to go into % allocation, number of stocks in the portfolio and the sell rules.
What I am trying to emphasize is that this money management should be independent of the ranking, but rather, adjust itself to the results of the sim. For instance, a sim could watch its losers and winners over time and adjust its stop losses dynamically. It can see how much of its return came from the top 5 winners and adjust profit taking to ensure it does not get overheated. Over-optimization and curve-fitting is bad, but who said adjusting to live markets is bad? There goes my biggie feature request to Marco and team. It could be a feature where stops, position sizes, cash reserve (money allocation and sell rules) are all specified in terms of performance parameters measured over the last 50 trades, so the sim and the portfolio use those variables instead of hard numbers.
I am sure in the vast world of trading, people would have experimented with such ideas.
Ravi
Ravi,
That was one of the best posts I’ve seen on this board. It should go in the knowledge base. Good work, my friend.
Brian
Thank you. I have benefited a lot more from this forum and hope to give at least as much as I learn.
I have gone back and re-edited my posts to clarify thoughts and add some more ideas.
Ravi
Thanks to Ravi and others for keeping this thread going, which I started over a year ago. Ravi, I particularly appreciated the references and pointers that are specific to systems development.
Coming off of my worst month in a long time (on the heels of a great 1.5 years – so don’t worry), I am even more convinced than ever that money management, position sizing and risk management are my personal holy grail. For those that feel similarly or even wonder if they need to look at these things more closely, I highly recommend Trading Risk by Kenneth Grant.
Perhaps more importantly, I have started keeping track of what I have read in the past two years or so in a more methodical fashion and have come up with the list below. These book recommendations are books I have recently read and are loosely grouped into categories, with similarities in each group. Each book has two ratings – based on a 10 point scale – my (highly subjective) assessment of value for a beginning trader followed by my assessment for an experienced trader.
A relative newbie should probably proceed from the top group down as far as they can continue to get value, but, personally I’d make the top 11 required reading at the outset and suggest that among the others, you don’t wait too long to read Mamis, Bulkowski and Grant, in particular.
Also, expect to reread many of these books as your knowledge base grows and you are able to pull different nuggets out of them.
Finally, you will find some bias here as it relates to my being an active trader (in addition to a LT investor) who focuses largely on position trading and has been using more T&A over the past few years.
Introduction and Context
Market Wizards: Interviews with Top Traders (Jack Schwager) – 10/8
The New Market Wizards: Conversations with America’s Top Traders (Jack Schwager) – 10/7
Stock Market Wizards: Interviews with America’s Top Stock Traders (Jack Schwager) – 10/7
Anecdotal / Historical
How I Made 2,000,000 in the Stock Market (Nicholas Darvas) – 9/5
Reminiscences of a Stock Market Operator (Edwin Lefevre) – 9/9
High Level, How To: The Short List
Fooled By Randomness (Nassim Taleb) – 9/8
Trade Your Way to Financial Freedom (Van Tharp) – 9/9
Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications (John Murphy) – 9/6
Japanese Candlestick Charting (Steve Nison) – 8/8
The Successful Investor: What 80 Million People Need to Know to Invest Profitably and Avoid Big Losses (William O’Neil) – 8/6
High Probability Trading (Marcel Link) – 9/9
High Level, How To: The Second Tier
Trader Vic–Methods of a Wall Street Master (Victor Sperandeo) – 8/8
How I Trade for a Living (Gary Smith) – 7/6
Trend Following: How Great Traders Make Millions in Up or Down Markets (Michael Covel) – 6/5
The Education of a Speculator (Victor Niederhoffer) – 7/7
Practical Speculation (Victor Niederhoffer) – 6/6
Detailed Individual Approaches
Fire Your Stock Analyst: Analyzing Stocks On Your Own (Harry Domash) – 8/5 …OR…
…Screening the Market (Marc Gerstein) – 8/5
How to Take Money from Wall Street: Learn to Profit in Bull and Bear Markets (Tony Oz) – 8/7
Trade Like a Hedge Fund: 20 Successful Uncorrelated Strategies & Techniques to Winning Profits (James Altucher) – 7/5
The Logical Trader (Mark Fisher) – 7/7
Too Often Overlooked: Managing Exiting Positions
When to Sell (Justin Mamis) – 9/9
It’s When You Sell That Counts (Donald Cassidy) – 8/8
Risk Control and Money Management
Trading Risk: Enhanced Profitability through Risk Control (Kenneth Grant) – 8/9
Value Investing Bible
The Intelligent Investor (Benjamin Graham) – 8/8
Thought Starters
The (Mis)Behavior of Markets (Benoit Mandelbrot) – 7/7
Devil Take the Hindmost: A History of Financial Speculation (Edward Chancellor) – 7/7
The Alchemy of Finance (George Soros) – 6/7
Reference and Miscellaneous
Encyclopedia of Chart Patterns (Thomas Bulkowski) – 7/8
Stock Trader’s Almanac 2006 (Yale & Jeffrey Hirsch) – 7/7
Choices, Values, and Frames (Daniel Kahneman & Amos Tversky) – 9/9
Fortune’s Formula (William Poundstone) – 7/7
The Vital Few vs. The Trivial Many (George Muzea) – 7/6
Options
McMillan on Options (Lawrence McMillan) – 7/8
Option Volatility & Pricing: Advanced Trading Strategies and Techniques (Sheldon Natenberg) – 5/9
Of Marginal Value…
A Mathematician Plays the Stock Market (John Paulos) – 5/3
The Master Swing Trader: Tools and Techniques to Profit from Outstanding Short-Term Trading Opportunities (Alan Farley) – 4/5
Swing Trading (Jon Markham) – 5/4
Wow! Outstanding list, thank you!
Brian, I agree. Unfortunately, Ravi’s post is buried in a discussion of best books, where it may be difficult for people to stumble across.
A year ago I suggested that the forum software should allow members to recommend posts, as is done on many other discussion boards. Then users could sort a listing of posts by number of recommendations. The cream would rise to the top without the need for Marco’s intervention. See this feature request
Way of the Turtle: The Secret Methods that Turned Ordinary People into Legendary Traders
http://www.amazon.com/Way-Turtle-Methods-Ordinary-Legendary/dp/007148664X
Interesting book by and about Curtis Faith, apparently one of the more successful of the Turtles. Forward by Van Tharp.
The trading system the Turtles were taught was trend following (eg of indexes and commodities and currencies, …) but I found it also helpful for thinking about P123 due to discussion about adjustable position sizing, importance of following system even for massive drawdowns, diversification, and Average True Range
It is my understanding that the trend following Turtle system never worked on stock index futures.
Steve
Just One Thing - Twelve of the World’s Best Investors Real the ONE Strategy You Can’t Overlook, by John Mauldin, editor
http://www.amazon.com/Just-One-Thing-Investors-Strategy/dp/0471738735
Each chapter is written by an experienced investor, many with 30+ hard-won years of experience. Much of the writing is heart-felt - they share what is most dear to them. I suspect that many P123 users would also be able to write equally powerful chapters.
I was struck by Dennis Gartman’s contributions. For him personally, his advice was "Never, every under any circumstance, should one add to a losing position … not EVER! " He compared that rule to the Real Estate Rule of "Location, Location, Location and felt it underlies survivability in the investment business long-term.
This is important for instance to the real-life question of what is best approach to scale in more real money into P123 ports. P123 SIMS currently must take postion of being either neutral on this matter or optionally simulate buying more of stocks that have fallen at Rebalance, but are still of sufficient rank. While I realize such a test would not include the important strengths of ranking that P123 offers, I am wondering if anyone is aware of academic studies or has ever backtested elsewhere to compare the approaches: (1) Add more to winning position, (2) Add more to losing position eg “Buy on Dip” and, (3) Stick with position until replace with new stock (P123 approach)
Thank you.
It is my understanding that the trend following Turtle system never worked on stock index futures.
Right, actually a lot of traders who tried to make the move from commodities to stocks complained that the movement of stock prices is highly random, and doesn’t trend very much.
I read a book “how markets really work” recently with some data in it that shows counter trend trading is a much better idea, at least in the short term.
For a full treatment of the subject, you may want to download "A Quantitative Approach to Tactical Asset Allocation "
ie Long term trendfollowing on stock indexes works, but probably not in the manner you would expect.
Hi, Budonk. The link in your last post isn’t working. Truncated at http.
Here is the correct link for A Quantitative Approach to Tactical Asset Allocation
FWIW, the author, Mebane Faber, also has an excellent blog: World Beta
“Your Next Great Stock” by Jack Hough who writes Smartmoney stock screen column. This is an introductory book to screening and is very nice complement to AAII in terms of providing perspective and helping to learn new strategies and test them out here at P123.
Kurt
Came across this daily free Small Cap news reader.
Which may be of interest to some in this community.