Sharpe ratio: the right mathematical answer to the wrong question

Andrew Swanscott runs the podcast Better System Trader. This is great resource: fantastic interviews with real-life practitioners. I made a statement that attracted a few comments from listeners: Sharpe ratio is the right mathematical answer to the wrong question. Here is the answer as Andrew kindly re-posted on his website.

Sharpe was the right answer

First, let’s start with what Sharpe does well. There are two things it does well:

  1. Cross-asset unified measure: we all know that the most important component in alpha generation is asset allocation. Now, the difficulty is to have a single measure of risk adjusted measure of alpha. This is where Sharpe did the job. It could give a single number across many asset classes: be it fixed income, equities, commodities etc.
  2. Uncertainty: the human brain is hard wired to associate uncertainty with risk. It triggers the amygdala and activates the fight, flight or freeze reflex (see one of my posts about fear and greed). So, Sharpe is a good measure of uncertainty: it quantifies units of uncertainty adjusted performance.

Now, Sharpe ratio, as part of the modern finance package, was invented the same year of the coronation of the Queen of England. It was good, almost revolutionary for its time, since Batista in Cuba was fighting El Che and Fidel. But, like the UN building designed by Brasilian Oscar Niemeyer, it did not age well, and here is why:

Sharpe is not a measure of risk, it is a measure of volatility adjusted performance

Sharpe equates volatility with risk. Risk does not equate volatility and here are a few examples:

  1. Low vol may be extremely risky: LTCM had low vol. In fact, their strategy was to be short gamma. It worked until it did not. Fast forward 2008, vol funds collapsed one after the other. Low vol does not equate risk.  As the great American philosopher Yogi Berra reminded us: “in theory, theory and practice are the same. In practice, they aren’t”. In theory, CDOs and CDs were AAA, low vol high yield products.In practice, they weren’t
  2. CTAs like Ed Seykota, Tom Basso, Bill Dunn, William Eckhardt etc: they have supposedly hopelessly low Sharpe but have clocked >+25% year in year out.

Does it mean that the CTAs have risky strategies ? No, it means they have low semi-volatility adjusted strategies. Semi-vol is just downside volatility.

Volatility means uncertainty, just learn to get comfortable with it

As much as uncertainty is not pleasant and may trigger some reptilian alarms in our brain, we must learn to live with it. It involves mindfulness meditation, strict formalisation of strategies etc. Do not pray for an easy life, pray for the strength to endure a tough one.

Now, what is risk ?

Risk is not a small paragraph at the end of a dissertation called investment thesis. Risk is a number. The only difficulty is to find the adequate formula that goes along. There are two types of strategies: mean reversion or trend following. Please read my posts on the subject.

Risk is not difficult to quantify. It is only difficult to identify. I have come up with the common sense ratio as it recaptures both mean reversion and trend following strategies.

CSR = tail ratio * gain to pain ratio

sense ratio as it recaptures both mean reversion and trend following strategies.

CSR = tail ratio * gain to pain ratio

Please use the trading edge visualiser to find out your personality:



Bottom line, we have associated risk with volatility. We have come up with a measure of volatility adjusted performance and deem it a risk measure. CSR, on the other hand, is a unified risk measure that can be used across asset classes. It measures risk according to strategy type.

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What programs are usually used to back-test trading strategies?

Answer by Laurent Bernut:

Testing a strategy is a journey. For any journey, You need the right equipment. I test everything on WealthLab then program on other platforms. Here is why

The objective of back-testing is to simulate real trading. When You trade real money, You face the following problems:

  1. Position size: You do not trade 1 share per trade. You may have sophisticated position sizing algo. Platforms offering position sizing algos in back-testing phase are few and far between: WealthLab & amiBroker. QuantConnect and Quantopian may but I have not tried
  2. Cash constraint: You strategy may generate signals but if You do not have cash to execute either a Buy Long or a Buy to Cover, it is a moot point. WLD and amiBroker
  3. Portfolio rotation: this is similar to the above point. Test may look great/poor on one security but in real life, you trade a portfolio.
  4. Slippage & commission: always set slippage at max. My settings is -0.5% one-way, that is 1% slippage + commission for a round trip
  5. Volume: only academics and efficient market believers trade on thin markets and banking holidays
  6. Borrow and uptick: structural shorts are a dime a dozen. Profitable structural shorts are the unicorns of short-selling. If You find one, capture it safely and let’s study it

The journey:
The first part of the journey is to get the signal right. This is where most people stop. They identify a signal that they test across a population of stocks one after the other. This may seem fine in theory but in practice You will rapidly come across a concept called Buying Power. If You don’t have the cash, You can’t execute. If You can’t execute, then it’s all academic. This excludes primitve versions of Matlab, MT4 and older versions of TradeStation
Math formula for that level is: Signal Validity = Win%

Second stage is when You start testing at portfolio level
You have realised that signals must be executable to be worth anything.
At this stage, You will test signals. These are where most people stop. This is the province of most sell-side quants using R or Matlab. Tradestation does not go any further. Not sure about Ninja, never tested that one
It assumes all positions are equal weight. This is primitive
math is  Win rate = Win% – Loss %
Bloomberg BTST is a rudimentary version of that

Third stage is when You start working on the real trading edge formula
Trading Edge = Win% *Avg Win% -Loss%* Avg Loss%
This is when You start to realise that size does matter in the markets, particularly when strategy fails to deliver.
At this stage, there are only two platforms which deliver: WealthLab Developer and Amibroker. Both have a comprehensive position sizing library.
Matlab can deliver but You will need to buy an additional expensive module. Matlab is an expensive guillotine, hardly suitable for neurosurgery. I do not recommend it.
WealthLab and amibroker are both C#. They both have canned strategies You can learn from and a vibrant community.

Personally WealthLab changed my life. It permanently changed the way I perceive the markets.

A word about optimisation: the masala smoothie approach
When I started working on the strategy, I, as everyone else, ventured on the side of complexity. I resorted to optimistion to find the best indicator value.

Most people, most quants, love to optimise and re-reoptimise and re-re-optimise their optimisations.
Bad news: it gives the right mathematical answer to the wrong question. Optimisation is the monosodium glutamate MSG of strategies: it gives depth and substance to poor quality food.
Work on the logic, not on the value of some hypothetical Billion Dollar idea.
Do not throw everything in hoping it will stick. Optimisers will waterboard the data until it confesses everything

PS: 100% Free Kick -A@#resource:
Andrew Swanscott interviewed me on Better System Trader.
Interview with Laurent Bernut – Better System Trader
Off line, he suggested I should offer something to his audience. Here is a tool I use daily.

The Trading Edge Visualiser User’s Manual
The way to use it to load your backtest data and look at distribution. I use this tool many times a day during strategy development.

What programs are usually used to back-test trading strategies?

The Trading Edge Visualiser User’s Manual

It is impossible to survive in the markets without an edge. Let alone being able to quantify it. How do You know if You even have an edge ?  If You want to articulate a better strategy, You need to 1) understand where your trading edge comes from, 2) quantify it and then 3) gradually improve it. Sharpe, Sortino or Treynor ratios may provide some comfort, but these are just sterile numbers. The smoothie approach of blending indicators and factors into an optimiser has never led to robust breakthroughs. Our brain remembers stories, associates images and concepts. If You can visualise your trading edge, quantify then You will be able to will be able to a better strategy.
The tool I am about to share simply changed my life. It permanently changed the way I approach markets and strategy development. It enabled me to reclassify strategies in two buckets. It then enabled me to understand the pros and cons of each. From there, it showed me the way to improve my trading edge by gradually nudging the distribution. This file is still very much part of my daily development kit. For example, in the summer of 2015 we realised there were many false positives very close to the break even line. We modified just 2 lines in the program, win rate improved by 5% points. This has enabled us to trade higher periodicity while keeping a high win rate.
The Trading Edge Visualiser tool is free. (You may be asked to reconfirm your mail-address, but it is 100% free). It is designed to be simple and intuitive. It will help You
  1. visualise your dominant trading style: mean reversion or trend following
  2. visualise and quantify your trading edge both in aggregate and at individual security level
  3. materially improve your trading edge: we posted some techniques and tips. Try them and see the results for yourself

The trading edge formula

Whatever You believe your trading edge comes from, it can be expressed in this simple formula
      Trading Edge = Win% * Avg Win – Loss% * Avg Loss
The Trading Edge Visualiser is a visual representation of the trading edge. It shows two distributions: absolute P&L and contribution. Contribution is simply P&L divided by Equity.
Green and Orange bars show Buy and Sell (Long & Short) trades. Blue and mauve bars show AVG win, AVG Loss for BUY & SELL. The middle bar is BUY & SELL Trading Edge.

Visual representations of the trading edge of the  styles

Irrespective of instruments traded, there are only two major types of strategies: mean reversion or trend following.

Mean reversion 

Gain Expectancy - Classic Mean reversion
Mean reversion strategies have a Moby Dick shape like distribution:
  1. the hump of the win rate is above the 50% hit ratio line
  2. The long left tail looks like a fin.



 Trend Following

Gain Expectancy - Classic Trend FollowingTrend following strategies have those characteristics
  1. Low win rates: between 30 to 40%. The peak of the loss rate is below the 50% line
  2. Short left tail
  3. Long right tail

 Step 1: Diagnostic

“If You don’t know who You are, the markets are a very expensive place to find out”, Adam Smith
Both Abebe Bikila, the barefoot Ethiopian marathonian, and Hussain Bolt run fast. Yet, marathonians are not good sprinters and vice versa. Similarly, we all have our own trading personality.
The story we tell ourselves about our style and what our trading history shows are two separate things. It is not uncommon to find “value” guys chasing momentum. Neither is it rare to find “momentum” guys doubling down on “cheap” stocks. The first step is to take an honest look at your dominant style. This tool is as honest as the scale in your bathroom.
Process your trading history on the Trading Edge Visualiser. Compare your distribution with the above major dominant styles: mean reversion or trend following.

Step 2: Understand and measure the risks associated with your dominant style

Risk is not a dissertation in an investment thesis. Risk is a number. The difficulty is to pick the formula will adequately match the risk associated with your style.
Sharpe, Treynor et al do not measure risk. They measure volatility of returns and naively assimilate volatility with risk. They may have some marginal utility for asset allocation purposes, but certainly not when it comes to quantifying risk.
Relevant risk measures:
  1. Mean reversion: The key risk measure for mean reversion strategies is the Tail Ratio. Tail ratios of 0.3 and below present severe risk of blow-ups. For example, some strategies may clock +0.5% every month, but have a sudden -4% drawdown. This would take 8 months to recover, which is probably beyond the patience threshold of many investors.
  2. Trend following: The key risk measure for trend following strategies is the Gain to Pain Ratio: trend following strategies have low win rates. For example, if You allow each loser to dent your capital by -1%, assuming a 40% win rate, winners will have to average +1.5% just to break even.
Common Sense Ratio
“Common sense is not so common these days”, Voltaire, French freedom fighter
One fine Monday morning at 9 am, I had the honour to meet Jack Schwager. I had just finished his book on risk so I was eager to show him my concocted risk measure. He murmured: “hmm, common sense”. A few days later, I showed it to my boss who cared to elaborate: “hmm, it makes good common sense”. Voila: Common Sense Ratio.
     CSR = Tail Ratio * Gain to Pain Ratio
Lose money 1 < CSR < 1 Make money
CSR is a notable improvement on the tail ratio as it will also capture aggregate profit ratio, or the ability to recover from big losses. It will recapture the inherent cyclicality of trend following strategies via mediocre GPR but high tail ratio.

Step 3: Improve your trading edge

Techniques explained below are designed to nudge the shape of your distribution. Your trading edge is the shape of your distribution. Ideally, You want something that looks like this:

  1. High win rate: not only does it feel better, but it compounds fasterGain Expectancy - Alpha Secure
  2. Long right tail: ride your winners and allow your capital to appreciate
  3. No left tail: cut your losers
  4. Symmetrical distribution on the Long & Short side: identical rules on both sides of the book


Mean reversion

The key to success for mean reversion strategies is to increase the tail ratio. This can be accomplished in two ways:

  1. Stop loss: a strategy without a stop loss is like a car without brakes. As a rule of thumb, a stop loss should not be further away then twice the 90th percentile of your profits. Beyond that limit, the period of recovery may be too long to be commercially acceptable
  2. Elongate your right tail: mean reversion strategies do not allow winners to fully mature. This simple technique can allow winners to develop while preserving profits. Instead of closing the entire position, close no more than 2/3 and place a trailing technical stop loss on the remainder. Do not place a valuation stop loss as it will exceed your comfort zone.
 Moral of the story:
  1. Shops do not restock on products they cannot sell; they mark down the inventory and clear it at a discount. Similarly, do not double down on losers, accept your loss and move on.
  2. “Value” investors usually sell their positions to their “momentum” colleagues, only to sigh in despair when prices subsequently double or triple. Next time, sell them a portion of your holdings and enjoy the ride with them. Worse case scenario, if it does not work, your stop loss will take you out and protect your profit.

Trend Following

Profits look big only to the extent that losses are kept small. So, all You have to do is to manage losses and profits will take care of themselves.
  1. Stop Loss is the second most important variable in your trading system, after the most volatile place on the market, that is the grey box between your left and right earlobes. Stop loss has a direct impact on three out of four components of the trading edge: Win rate, Avg Win, Loss rate. Make a habit of placing your stop loss as your enter your orders
  2. Would You allow tenants to stay rent free in a building You own ? Every time You say yes to a free loader, You say no to a good customer, so make a habit of evicting poor performers
  3. Improve your win rate: assuming average loss stays the same, any improvement in the win rate will have a material impact on the trading edge.
Real life example: i am a short seller. The short side is plagued by periodic tidal waves called short squeezes.  The Trading Edge Visualiser taught me that rather fighting them, it made more sense to use them. I wait for the short squeeze to pass and only after that do I enter at a higher level. Then, as the next short squeeze approaches, I reduce size. This clocks a small win, reduces risk and allows to weather squeezes. Once the squeeze is over, there is a fresh high probability entry point.
This habit of scaling-out and scaling-in tilts the P&L distribution to something like the distribution at the beginner of the paragraph. It combines the high win rate of mean reversion strategies but still has long right tail, short left tail.

File user’s manual

 The Trading Edge Visualiser was built using Metatrader 4 OrderLog. It can be applied to any trading history, provided You load data in the fields coloured in blue and reset the pivot table

 Data load

  • Time and Date: the information is organised in chronological order on the Table sheet
  • Ticket No: this assumes that all trades have a unique identifier
  • Symbol: The table sheet calculates the trading edge of each security in a timely manner
  • Type: Buy/Sell, this allows rapid sort
  • Buy/Sell Lots: this field is useful for multiple entries/exits
  • Profit: this is an absolute USD P&L
  • Contribution: this is a simple P&L / Contribution field. There is no currency conversion, benchmarking or modified-Dietz time-series. Relative performance calculation should take place in this field

 Pivot Table settings

  • ROW fields in Tabular Form: In the PivotTableFields: click on Field Settings: in Layout & Print table: Click on Show items in tabular form
  • ROW Fields SubTotals deactivated: In the PivotTableFields: click on Field Settings: SubTotal & Filters table, Subtotals: click None
  • PivotTable Options Totals Columns deactivated: Right-click anywhere in the PivotTable, go to Totals & Filters, uncheck Show grand totals for columns
  • Column Label: Click on Select All to allow automatic refreshing
 Useful tips:
  1. Run this analysis periodically and keep track of your evolution to receive the full benefits
  2. Truncate data: the current file looks at the entire population. Segment your trading history into blocks when your strategy performs, when it does not.
  3. Comment and annotate entries/exits. You will realise that a bit of finesse on exit will go a long way. It is useful to keep track of exits


People who keep track of their weight are 30% more susceptible to reach their weight loss target. The Trading Edge Visualiser tool will help You understand who You really are. It has the potential to transform your trading game, as it continues to do so for me.

It is 100% free, so download and play with it!

The four horsemen of apocalyptic position sizing used by professional investors

4 horsemenDespite picking a fair share of good stocks, it is still tough to generate some consistent serious alpha. Picking the right stocks and exiting them well tells You how often You win. How much You win, however, is a function of how much You bet. Some professional investors pay surprisingly little attention to their bet sizes. Below are four algorithm often practiced by professionals that can

  • Four popular bet sizing algorithms used by professionals that have negative gain expectancy
  • Size does matter in the markets: 1$ or 100 will have a different outcome


When it comes to bet sizing, there are only two sizes: either too much, either too little. As a professional short-seller, position sizing is mission critical. Successful positions shrink. Not only do they contribute less and less, but they also tilt exposures (net & net Beta). To add insult to injury, they become less noticeable. On the other hand, unsuccessful positions balloon. They immediately hurt. So, I have spent years studying the science of bet sizing. I sought to learn from other investment professionals. It eventually dawned upon me that Long biased people rarely ask themselves the same questions. For them, bet sizing does not have the same degree of urgency. Worse even, it became apparent that some position sizing algorithms had outright negative expectancy, or nasty side effects that they were never even aware of.

Aral Sea Ships

Insufficient liquidity

Horseman 1: Liquidity. If You can’t get out, You don’t own stuff. Stuff owns You
Getting into a position is like buying a boat, or a second house. You can do that any day of the week. Now, selling a boat is tough (been there, done that). It may take time to build a position in a stock. Time is an expensive luxury few market participants can afford when they want to liquidate.So, no matter how attractive a story may be, if you can’t exit easily, just don’t enter.

Rule 1: don’t size your positions so that they may go Hotel California on You:
“You can check-out anytime You like, but You can never leave!”, Don Henley, Hotel California




Example of long conviction

Horseman 2: High conviction: feel-good position sizing
Disclaimer: this position sizing is used by the greatest and the worst investors. The classic rationale is: “if You believe in something, then you should go big or go home”. What else is it but a feel good position sizing algorithm ? Risk is not quantified but subjectively assessed. The problem is mental accounting, or the constant emotional revisionism of the situation. Jack Welch said: “what can be measured can be improved”. If You can’t quantify your risk, then don’t expect improvement in consistent alpha generation capability.

The greatest investors also use conviction as a position sizing algorithm. The only difference is that they express conviction in units of risk. They quantify risk first and then put chips on the table according to their perception of the reward. If an idea does not pan out, risk can be parred down.


Horseman 3: Equal size: one-size-fits-all and the volatility roller coaster
This position sizing algorithm will not bring ruin, but it has negative side-effects that may prevent You from achieving your obejctives in terms of performance, attractiveness to investors and quality of life…

Equal weight is a form of laziness:
First, let’s look at the math behind equal weight. All trading systems boil down to their trading edge (Avg Win% * Win% – Avg Loss% *|Loss%|). Since all bets are equal, equal weight implicitly puts emphasis on the signal, and excludes the value of money management. In other words, stock picking has to be consistently above 50% to absorb losses and generate a profit. Unfortunately, no system works all the time. So, equal weight carries cyclicality in performance.

Ignoring volatility at the position sizing level invites volatility in the portfolio
Not all stocks have the same personality. Some are more volatile than others. For example, internet stocks tend to be much more turbulent than utilities. If all positions are sized equally, then the most volatile stocks will drive the volatility of the overall portfolio. Morality, ignoring volatility at the position sizing level will in turn invite volatility in the portfolio.

Horseman 4: Average down, martingale and the certainty of ruin
Rookie gamblers always come up with some elaborate scheme to break the casino. It is usually a variation on the theme of doubling down after each loss. They believe that the losing streak will end and they will recoup their losses. This position sizing algorithm is known as martingale. Let’s look at the math behind this algorithm

1. Adding to a losing position reduces the hit ratio
2. even if You had infinite capital, the most favourable outcome would be break-even. First, do You have infinite capital ? Second, any other outcome before the most favourable one carries an interesting probabilistic property called certainty of ruin
3. Doubling down means adding to losers. Resources have to come from somewhere, probably winning trades. Books written by successful market participants always emphasize “cut losers, ride winners”. Do You know any successful market guru who says “cut your winners, ride your losers ?”

In conclusion, there is a reason casinos have gold, marble, paintings from masters and gamblers declare bankruptcy. Double down on losers and You will go broke. One more thing about probabilities, it’s not about if, it’s about when.

Size does matter in the markets. Not paying enough attention to position sizing has consequences that range from unpleasant volatility to certainty of ruin. Position sizing is not a glamorous topic, but in highly competitive sport, every little bit of edge counts

A powerful two step process to deal with the endowment effect: The game of two thirds, or how to deal with free loaders in your portfolio

Would You allow tenants to stay rent-free ?

Would You allow tenants to stay rent-free ?

If You were the owner of an apartment building, would You allow tenants to stay rent-free forever ? You would probably do everything in your power to either collect or evict free loaders. In the investment realm however, one of the main reasons managers fail to accomplish their goals is that they allow free-loaders to stay rent-free in their portfolios. The difficulty then is how to identify and deal with free loaders.

They don’t really stand out enough on an individual basis. Yet, as an aggregate free-loaders put a drag on performance.

  • Endowment effect: once in the portfolio positions are sticky.
  • How to identify and effectively deal with free loaders
  • The 3 main benefits of the game of two thirds
Once upon a time, i used to place thematic small positions across the portfolio like pawns on a chessboard. They were supposedly hedges for China, precious metals, oil, monetary intervention etc. They were all tiny positions that were supposed to kick in if any of these themes were to gain traction. Six months went by and I could not understand why performance was so pedestrian. Meanwhile, none of those stocks had worked. Then, it dawned upon me that even though they were tiny individual positions, they totaled 10% of the portfolio as an aggregate.
The endowment effect (Thaler 1980)
Endowment effect is the hypothesis that people value more what they own than what they could buy. It is hard for positions to dribble their way into our portfolios, but once in they become sticky. It is difficult to get rid of them, even though they do not contribute. Some managers would hold on to losers just because they do not know what to buy next.
Our lives, our desks, our houses are filled with clutter. Unless we actively create and enforce rules to get rid of it, clutter creeps up on us. Our inner saboteur will always find good reasons to hoard junk. To illustrate its potency, let’s look at a simple example: in your wardrobe, isolate the clothes You have not worn for over a year. Think about all the excuses to keep them, but then ask yourself: “If i did not have it, would i buy it now ?” If not, then bye bye, fashion moves on and so should You.
The game of two thirds: A simple two-step process to deal with free loaders
Free loaders neither detract nor contribute enough to be visible. They don’t stand out enough to be dealt with. Since it is not possible to deal with them on the y-axis (price), the solution is to introduce time x-axis. Rationale is simple, if stocks have been there for some time, but still fail to contribute, then their weight should be reduced.
  1. Calculate portfolio turnover, divide it by three: first 1/3. Add 1/3 turnover to the entry date of each position. For example, a stock entered on January 5th and a turnover of 1 would yield a cut-off date of April 5th
  2. Divide performance in 4 quartiles, concentrate on the third quartile: second 1/3. For all stocks in the third quartile past their anniversary date, cut weight in half
 Special mention for long-term winners
Apple (AAPL) or Softbank (9984:JT) are long-term winners. They sometimes go through extended periods of under-performance. Because there is so much embedded profit, it is difficult to realise that they have not contributed for some time. The idea then is to reset contribution on a rolling basis.
The idea then is to apply the same rules as above on a rolling basis. Instead of cutting positions to half, taking a portion the size of the out-performance from the previous haircut. For example, if Apple went up by 10% from previous haircut, then shave 10% off the current size.
The rationale is
  1. if it starts to underperform, it will be dealt with, and this profit taking will have cushioned the blow. This demonstrates stewardship
  2. If it continues to go nowehere, resources are re-allocated to a potentially more productive asset. If non-performance persists over 2/3 of portfolio turnover, then a more drastic reduction is in order
  3. if outperformance resumes, then it will be dealt with
It is important to periodically reset contribution. When stocks have been in the portfolio for a long time and substantially contributed, we become attached. Failure to reset contribution is one of the reasons why some managers escort their positions on the way up and then all the way back down. It doesn’t show until it is too late.
The three benefits of the game of two thirds

The game of two thirds may appear simplistic. It has however powerful psychological implications. It is a simple, powerful and objective way to short-circuit the endowment effect for three reasons:

  1. Simplicity: math is beyond dispute. Simple rules are elegant, easier to implement and harder to challenge
  2. Stewardship: great investors are not smarter, they have smarter trading habits. Getting rid free loaders builds the habit of dealing with difficult stocks
  3. The quality of our excuses determines the quality of our performance: one of the most frequent excuses is “what do i buy next ?” Constant re-examination of positions forces managers into action.
Once in a portfolio, positions are often sticky. Asking ourselves “would you buy it again today ?” is too subjective to deal with positions that have overstayed their welcome. Our inner saboteur will find good reasons to procrastinate until the next review. Our natural instinct to hoard junk “endowment effect”. The game of two thirds is an elegant way to identify and deal with free loaders.


The game of two halves: an elegant two-step process designed to cut losers, run winners, while maintaining conviction

In every hospital around the world, there is an unwritten rule: surgeons should not operate on their own children. There is no such thing as professional detachment when it comes to your own child. In the investment realm however, market participants are consistently asked to defend their convictions, but also expected to be surgical about their losers. How can someone maintain enough attachment to weather rough times, but stay detached enough to surgically cut when necessary ?

“Cut your losers, run your winners” is the key to survival in the markets, but no-one tells You how to pick the lock. This is especially difficult if You are a fundamentalist (fundamental analyst/manager/investor/trader). First, there is no price mechanism like a stop loss to tell You it’s time to move on. Second, You don’t want to be perceived as lacking conviction. Third, investors want You to manage risk. No wonder 80% of managers find it difficult to outperform every year.
This is the second article in a series of four about exits and affective neurosciences. Our central premise is that the quality of exits will determine the quality of performance. The purpose of this exercise is to help fundamentalists cut their losers, run their winners, while keeping conviction. It is based on the assumption that they are refractory to the idea of a stop loss policy. It is a simple yet powerful method that is guaranteed to mechanically lift performance.
You do not need to be right 51% in order to make money
One of the classic myth is that “You will make money as long as You are right 51% of the time”. Wrong. You will make money only if You have a trading edge:
                     Trading edge = Average Win% * Win% – |Average Loss%| * Loss %
Let’s take an easy example: if average profit is twice as big as average loss, what would be the break-even hit ratio ?
          0  = 2 * X – 1 *(1-X)
          X     = 1/3
with X = Win% and Loss% = 1- Win%
In a system with a 2/1 profit/loss ratio, you only need to be right 1/3 of the time. In other words, stock pickers who identify 3-5 baggers only need to keep losers small to make formidable gains
In reality, the visual representation of a stock picker’s P&L distribution looks very much like the chart below: a few princes make up for a lot of frogs. . Being right 51% of the time through the entire bull/bear cycle is the unicorn of stock picking. Every strategy experiences a drawdown at some point. Stock pickers make money as long as they stay disciplined and keep their losses small.
 Gain Expectancy - Classic Trend Following
 In order to move to the distribution shown below,  one of two things need to happen:
  1. Either reduce the number of frogs: easier said than done, particularly when strategies stop working at some point through the cycle
  2. or, their impact is reduced: reducing drag will mechanically improve profitability
 Gain Expectancy - Alpha Secure
Predicting tomorrow’s winners is much harder than dealing with today’s losses. The game outlined below is an elegant way to deal with losers. Not only does it mechanically improve the trading edge, it also salvages ego and rewires neural pathways from outcome to process orientation.
The game of 2 halves
The objective is to halve the weight of losers once they detract more than half average contribution. Proceeds are then re-allocated to either fresh ideas or winners. This is a simple two-step process:
  1. Divide all positions between contributors and detractors, calculate average contribution: first half
  2. Reduce weight by half (1/2) for all detractors below -1/2*Average contribution: second half
Average contribution: +0.5%          Babylon Ltd weight: 4%  Unrealised P&L: -0.4%     Realised P&L: 0%
After weight reduction                      Babylon Ltd weight: 2%   Unrealised P&L: -0.2%     Realised P&L: -0.2%
Now two things will happen: either Babylon Ltd will perish, or it will rise
  1. If Babylon meets a tragically eponymous fate : it would have to drop another -15%, just to reach minus average contribution, or -0.5%. At this point, it will be either it is a screaming Buy or a dog. Either way, it will be an easier decision to make
  2. If Babylon rises: then unrealised profits will compensate for realised losses. One rule of thumb in order to maintain a positive trading edge, do not add to the position until it crosses previous entry price
The additional 2% freed-up can be re-allocated either to winners or fresh ideas. Adding to winners cements conviction. Adding fresh ideas brings fresh blood to the portfolio. Either way, it is more of a good thing.
Special mention for managers who use an equal weight position sizing: Equal weight position has many drawbacks, but it has one benefit in this case. Instead of using contribution (weight * return), a simple distribution of return is sufficient.
The game of two halves has three deep benefits
  1. Trading edge mechanically improves: this is a simple elegant formulation of the first mantra: “cuts your losers and ride your winners”
  2. Good stewardship: managers are often torn between defending their convictions and dealing with problems. If they cut too frequently, they are perceived as lacking conviction, which negatively impacts investors confidence. By selling a portion of the position, they show peers and investors that they both maintain their conviction and deal with problems
  3. Process versus outcome neural pathways re-wiring: funds reach capacity not when they are too big in size, but when inertia sets in. Dealing with losers forces managers into action. This accomplishes three things:
    1. Managers become dispassionate with their problem children: since dealing with them improves stewardship, the stigma of taking a loss disappears. The game is simple enough to be executed even in the darkest
    2. Increased fluidity: since proceeds are re-invested, managers have a direct incentive to look for fresh ideas, or to their existing ones
    3. Process versus outcome mindset: believing that being right about a stock is a matter of profitability is an outcome process. When ideas are profitable, ego gets validation. When (not if) they are unprofitable, ego feels under attack. This invariably leads to defensive, unprofitable and often destructive behaviors. Dealing with losers in an orderly fashion changes focus from outcome to process. Being right is no longer about the outcome but about doing the right thing.
The game of two halves is a key to unlock the “Cut losers and ride winners” fortress. It is an elegant solution to the oldest problem in fundamental investment. It reconciles the demand for conviction with the need for action. The privilege of its (mathematical) simplicity is that it imposes itself even in the darkest times.
More importantly, it changes the definition of being right. It is not a binary outcome on the profitability of individual ideas., It is the observance of a process that will lead to higher aggregate profitability. In the Jungian archetypes, it no longer triggers the orphan (amygdala in the limbic brain, responsible for fight, fight or freeze), but activates the ruler (pre-frontal cortex or thinking brain). In short, the game of two halves reduces stress and improves profitability.

Regardless of the Asset Class, There Are Only Two Types of Strategies

Finance is one industry where there is no shortage of creativity. There is always a new strategy, investment vehicle, or asset class. This alphabet soup is confusing, particularly when it comes to assessing risk and reward across asset classes. Yet, there is a simple universal way to classify strategies. They fall into two buckets: either mean reversion or trend following. Simply said, the exit policy determines the win rate, which then shapes the return distribution.


  • A powerful visual representation of style/gain expectancy: Call to Action: our commitment is to help people become better traders. if YOU want to visualise your style, opt-in and we will send YOU a portfolio diagnostic tool for free
  • Regardless of the asset class, there are only two types of strategies: mean reversion or trend following
  • Each strategy type has a specific risk profile, which require different risk metrics. Common Sense Ratio recaptures risk for all strategy types (Read this, it is important)
  • How to increase the win rate, gain expectancy and overall profitability depending on strategy type ?

I. The only two types of strategies: mean reversion or trend following

Over the years of patiently testing multiple algorithmic strategies, patterns in the return distribution repeated over and over. It eventually became apparent that strategies fall into two buckets: mean reversion or trend following. Attached are graphical representations of the gain expectancy of mean reversion and trend following strategies. The reason why the same patterns repeat themselves is simple: exit policy.

Market participants tend to treat exit as a single final event. Each trade is a binary event: either it is profitable or not. The accumulation shapes the return distribution. Hit ratio is then determined not by what we enter, but how we exit.

Charts below are return distributions for each strategy type. They are also visual representations of gain expectancy. One image speaks more than a thousand words. This representation changed my life. It permanently altered the way I perceive the markets. The game is about tilting gain expectancy: contain the left tail, moving the peak hit ratio to the right and elongating the right tail. This visual representation is a powerful tool. This is why we want to share it. We are committed to helping people build smarter trading habits. Sign-in to our newsletter (it’s free) and we will send you a portfolio diagnostic tool.

Mean reversion strategies compound small profits

Gain Expectancy - Classic Mean reversion

Death by knock-out: many small profits. a few knock-out blows

Mean reversion strategies compound multiple small profits. They rely on the premise that extremes eventually revert to the mean. They aim ato arbitraging small market inefficiencies. They often have low volatility  consistent performance. They perform well during established markets: bull, bear or sideways. They unfortunately perform poorly during regime changes. They also perform poorly during tail events. The key issue is to contain rare but devastating blow-ups.

 Mean reversion strategies characteristics are (see graphical representation):
  • Moderate to high turnover
  • High win rate: often above 50%. The shorter the duration, the higher the probability of success
  • Consistent small average profits: trades are closed around the mean
  • Low volatility consistent performance
  • Potentially devastating left tail losses: make a little bit of money every day and lose a fortune in one day
  • Long period of recovery after losses:
Examples of mean reversion strategies are
  • Short Gamma: sell OTM options so as to collect pennies in front of a steam roller
  • Pairs trading (non FX): bet on the convergence between two historically correlated securities
  • Value investing: Buy low PBR stocks and “undervalued” assets
  • Counter trend: sell short shooting stars and catch falling knives

Mean reversion strategies post modest but consistent profits. They cater to investors who would look for low volatility returns. Their challenge is the left tail, those infrequent big losses that will take a long time to recoup.

Trend following strategies have a few home-runs 
Gain Expectancy - Classic Trend Following

Death by a thousand cuts: many frogs, a few princes

Trend following strategies rely on the capital appreciation of a few big winners. Whether they follow stories, fundamentals, earnings or price momentum, stock pickers are trend followers. They may fail to appreciate being called trend followers, but their P&L distribution tells a different story.

Trend followers kiss a lot of frogs: they have a low hit ratio, often between 30% and 45%. Performance is cyclical. Styles come in and fall out of favor. Volatility is elevated. Performance can be underwhelming for long periods of time. The key issue is to contain losses during drawdowns.

 Trend following strategies share those common characteristics (see graphical representation):
  • Relatively low turnover
  • Low win rate: 30 to 40%: see chart
  • Big wins and lots of small losses: right tail on chart
  • Relatively higher volatility
  • Pronounced cyclicality: style comes in and goes out of favor
Example of trend following strategies are
  • CTA type systematic trend following,
  • Momentum: earnings momentum, news-flow, price momentum
  • GARP investing: growth at reasonable price
  • Buy & hope
 Trend following strategies post impressive but volatile performance. They can go through long periods of underwhelming performance, which take their toll on the emotional capital of managers. Their main challenge is to keep cumulative losses small. Profits only look big to the extent losses are kept small.
II. How to measure risk for each strategy type
Investors suffer from a “nice guy syndrome”: some young women genuinely say they want to marry a nice guy, but unconsciously react to so-called “bad boys”. Investors genuinely say they want returns, but in reality they do react to drawdowns. More specifically, they are susceptible to drawdowns in three ways:
  1. Magnitude: never lose more than what investors are willing to tolerate
  2. Frequency: lull investors to sleep. Clients will trade performance for low volatility: big money is fixed income, not stocks
  3. Period of recovery: never test the patience of investors.
There are two ways to lose a boxing match: either on points or by knock-out. Mean reversion strategies score until they get knocked out. Trend following strategies lose on points. Risk is not evenly distributed. Therefore, each strategy deserves its own set of risk metrics.
Risk metric for mean reversion strategies: knock-out


Mean reversion strategies have low volatility, consistent performance and high Sharpe ratio. On the surface, they are what investors look for. The problem is mean reverting strategies work well, until they don’t. Big losses are unpredictable. LTCM had a great Sharpe ratio, at least until October 15th, 1987… Risk is in the left tail. The best metric for mean reverting strategies is therefore: tail ratio. Tail ratio measures what happens at the ends of both tails:

Tail ratio  = percentile(returns, 95%) / percentile(returns, 5%)
For example, a ratio of 0.25 means that losses are four times as bad as profits. Turnover then becomes an important variable: the higher the turnover the shorter the period of recovery. The two ways a mean reversion strategy can survive is either by 1) containing the left tail or 2) increasing turnover.
Mean reversion strategies will test investors’ nerves on two things: magnitude of loss and period of recovery. For example,  some strategies such as fundamental pairs trading post constant, reassuring but modest profits like 0.5% a month. Then one day, they post losses of 3 to 5%. They lose in one month the gains of an entire year.
Investors often succumb to the sunk cost fallacy with mean reversion strategies. They believe that big losses are rare and that managers will eventually make them back. This bias ignores probabilities, particularly the theory of runs. It also ignores opportunity costs. The good news is that there is an optimal point below which it makes more sense to redeem than to stick with managers who experienced a severe loss. It is often referred to as optimal stopping. Whilst the formula can be complicated, a simple rule of thumb is to redeem if losses are below 0.4 of turnover.
On the other, trend following strategies have tail ratios ranging from 3 to 10. Winners are much bigger than losers. So, tail ratio is meaningless for trend followers.
Risk metric for trend following strategies: erosion
Trend following strategies have typically low win rates. Risk is therefore not in the tails, but in the aggregates: are a few winners big enough to compensate for the multitude of losers ? Measuring risk then boils down a simple ratio of profits over losses. The risk metric for trend following strategies is therefore:
Gain to Pain Ratio = Sum(profits) / Sum(losses)
Trend following strategies will test investors nerves on frequency of losses and period of recovery. Frequency of losses is another word for volatility. Trend following strategies are volatile, but semi-volatility (downside volatility) is low. They can also post lackluster performance for long periods. For example, mutual funds have built-in cyclicality. Even if they claim to beat the index, mutual funds still lose money during bear markets.
GPR does not apply to mean reversion strategies because blow-ups are unpredictable. GPR can stay high until it is torpedoed by one or two bad losses.
Combined risk metric: Common Sense Ratio
 “Common sense is not so common these days”, Voltaire, French freedom fighter
Managers rarely define themselves as adherents of either mean reversion or trend following. Even so, it still would not be easy to assess robustness. Besides, the more risk metrics we use, the more confusing it becomes. For example, some managers have great performance despite a bad Sharpe ratio, so the question is “which matters more in which context?”
Since both metrics outlined above can be expressed in a simple binary ratio, combining them makes sense. When this ratio was first to colleagues and friends in the HF world, comments sounded like “yep, common sense”, hence its imaginative name: Common Sense Ratio
                                                                     Common Sense Ratio = Tail ratio * Gain to Pain Ratio
                                                               Common Sense Ratio = [percentile(returns, 95%) * Sum(profits) ] / [percentile(returns, 5%) * Sum(losses)]
Above 1: make money, below 1: lose moneyCSR is much more powerful than either metric taken individually.
Example 1: GPR = 1.12, TR = 0.25, turnover = 2, CSR =  0.275
Let’s take a classic mean reversion strategy that generates 10% p.a. (GPR = 1.1). It has a moderate turnover of 1.5. Within a 24 months period, it will post a monthly drop of -4%, with a 95% probability. This is 5 times as big as the average profit, and roughly 4 times as bad as right tail profits (TR = 0.25). Common Sense Ratio is CSR = 0.25 *1.1 = 0.275.
On the surface, it may look like a modestly attractive strategy. In reality, the period of recovery combined with magnitude of loss imply that investors will have to be patient. In very simple terms, the CSR shows that returns are not attractive enough to justify investing in such strategies. Try this with a few low volatility strategies. Risk is not where You think it is.
Example 2: GPR = 0.98, TR = 3, turnover = 0.5, CSR = 2.94
Now, let’s take a strategy that loses 2% over a complete cycle (GPR = 0.98). Best winners are 3 times as big as worst losers (TR = 3). Turnover is low 0.5. You may think, why invest in a vehicle that loses money ? It does not make sense. Yet, You are invested in such vehicles: welcome to the average mutual fund. 80% of mutual funds lose roughly 2% to the benchmark. Every now and then, they outperform with a vengeance. The rest of the time they suck air. Morality: over time, mutual funds are poor investment vehicles if You stay invested through the cycle.
III. How to tilt the win rate, gain expectancy and overall profitability depending on your win rate
Gain Expectancy - Alpha SecureThe question boils down to: is it possible to combine the benefits of both strategy types without having the drawbacks of either one ? How can we generate a return distribution that would look like the one on the chart ? (*)
The whole game of investing is about generating a return distribution that would have the following characteristics:
  1. No left tail: small losses like a trend following strategy
  2. Long right tail: big wins like a trend following strategy
  3. High win rate: above 50% win rate like a mean reversion strategy

This type of strategy combines both short term compounding with long-term capital appreciation.

Investors following a mean reversion typically come in early and leave too early. The key is therefore to elongate the right tail. This is done through allowing a remainder to extend beyond the mean with a trailing stop loss.
  1. Set a stop loss (more on this in an upcoming article)
  2. When the trade means reverts, close half the position
  3. Set a trailing stop loss (not based on valuation) and close the trade once the stop loss is penetrated
Bottom line, markets can stay irrational longer than You think. When it stops making sense for You, it may start making sense for someone else. Ride their tail, but protect your downside.
If You are a trend follower, here is a simple game (game of 2 halves) that will mechanically improve your return distribution. We all know that making money is about cutting the losers and riding the winners. Here is an objective way to do it:
  1. Calculate your average contribution, divide it by 2
  2. Reduce by half every losing position below – half average contribution
  3. re-allocate the proceeds to winners
Bottom line, you have reduced losers and increased winners in a simple way (more in an upcoming article)
The  purpose of this article was to introduce a simple yet powerful way to reframe strategies independent of asset class. This enabled us to look at the merits and drawbacks of each type. We then looked at risk metrics that would best recapture their risk profile. We introduced a unified risk metric Common Sense Ratio that works across all asset classes. Finally, we looked at ways to tilt gain expectancy for each strategy type. Last but not least, if You want to know what your trading style, please subscribe and we will send You a diagnostic tool for free.
Preview of the next article:
The next article will deal with investor psychology. Short sellers have a unique perspective on investors psyche. We never sell short against buyers. We observe people who once held a position and are now processing grief. The next article will be about the psychology of grief adapted to the markets.
  1. Market regime: bear markets have several distinctive phases, with a measurable market signature
  2. You will never read an analyst report the same way again. You will learn to read emotions through language and back it up with numbers
  3. You will be better equipped to bottom fish for stocks
(*) : All above return distributions are derived from the same strategy. It has both mean reversion and trend following components. In order to draw a mean reversion strategy, the trend following component was switched off, and vice versa to build a TF distribution. Both components are normally switched on, third distribution

Is Stock Picking Overrated ?


  • If 80% of managers underperform their benchmark, we probably focus on the wrong thing. How about focusing on gain expectancy instead of stock picking ?
  • Signal module: how often we win (hit ratio) is not a function of what we enter (stock picking) but how we exit.
  • Money is made in the money management module: how much we win is a function of how much we bet (position sizing).
  • Psychology module: Great traders are not smarter, they have smarter trading habits

The finance industry is built on the cult of the stock picker. We have been conditioned to believe that entering the right stocks is the recipe to beat the markets. Year after year, we spare no effort, expenses, technology and time just to find that golden nugget. We never stop and ask ourselves whether it works in the first place. SPIVA gives an unapologetic report on active versus index investing. Every year, about 80% of managers underperform the market by a few percentage points, the equivalent of fees plus transaction costs of one time turnover. There are probably two reasons for this.

Firstly, Charles Ellis explained in his book “winning the losers game” that markets are dominated by institutional investors. The index is therefore the average of highly educated, intelligent, hard working and ferociously competitive people. So, outperforming the index comes down to beating a very high average.

Secondly, if , year after year, we try the same old “better sameness” just a little bit harder and expect different results, only to be humbled each time, we may have been focusing on the wrong thing in the first place. The answer may lie in an equation so simple it is often overlooked: Gain Expectancy.

But first, please understand that our objective is not to throw stones at active management from our modest glass house. We are committed to helping market participants build smarter trading habits. We provide simple yet powerful tools to nudge performance: resources, research, links, Excel files.

Enter Gain expectancy

Gain expectancy is just another fancy word for average profit. All strategies without exception boil down to this formula:

Gain Expectancy = Win rate% * Avg Win% – Loss rate% * |Avg Loss%|

Using this equation, we will examine what we believe to be the four components of any strategy in increasing order of importance: entry, exit, money management and psychology.

Entry Accounts for 5% of performance


Finance is the only sport that hands out medals before the race

Finance is the only competitive sport where we expect medals to be handed out before the race. Market participants focus their energy on picking the right securities, but stock picking is the process that leads to entry. When we focus on stock picking, we just care about getting the best stocks to the starting blocks and assume they will do well thereafter. We overlook critical questions such as 1) what if they do not perform as expected, 2) how big we should be and more importantly 3) will we have the fortitude to stomach the ride ?

Stock picking is not irrelevant, it is overrated. There is no doubt that picking the right stocks increases our chance of success. The treasure hunt of stock picking is the most exciting aspect of the job. Ironically, entry is also the part that has the lowest impact on performance. Looking back at the gain expectancy formula, entry is just an ingredient of the win rate%, not the happy meal. After all, even the best ingredients will not necessarily turn into a succulent meal if there is no recipe. When everyone else is fixated on entry, paying a little more attention to other components may give us a critical edge.

In the coming articles, we will examine different types of entry techniques, common pitfalls and remedies. Everybody likes to buy on weakness and sell short on strength. But sometimes weakness is a symptom of a bigger problem and vice-versa on the short side, strength turns into bullishness.

Exit Accounts For 20% of Performance


It’s not what we pick but how we exit that determines the hir ratio

We all have been shaken out of a position and then watched it rally without participating. The hit ratio is not determined by what we enter, but how we leave. Exit is a binary event: a trade is either profitable or not. The only time when win rate% can be calculated with absolute certainty is after positions are closed. Anytime before that is just paper profit. The quality of our exits determines the shape of our P&L distribution.

Before I embraced the sophistication of simplicity, I used to believe that a certain combination of factors would generate optimal performance. I was looking for a holy grail of some sort. The first epiphany came after a Monte Carlo optimization. One of the combinations made money 19 years out of 20, despite a win rate of 34%. Meanwhile, the highest win rate (67%) lost money 17 out of 20 years. The lesson was clear: “making money in the markets is not about trying to be right. It is about accepting one is wrong and move on”. There is one class of individuals to whom it should come easy: married men.

Would You drive a car without brakes ? Then, would You trust a strategy without a stop loss ? Market participants are often refractory to the idea of a stop loss. It is however the second most important component in any strategy. It has direct impact on 3 out of 4 variables of the gain expectancy: win rate%, loss rate%, Avg loss%. In addition, it has a direct impact on trading frequency and bet sizing. Profits look big only to the extent that losses are kept small.

Entry and exit constitute the signal module. They only determine the win rate. A trading system is like a car. The signal module is the engine. The money management is the transmission and psychology is the driver.

In the coming articles, we will examine the various types of exits and their influence on the P&L distribution.

Money management accounts for 25% of performance

Different Weight simulations SPX - Excel 2015-03-10

Same strategy, different bet sizing algorithms generate different outcomes

Money is made in the money management module. There is rampant confusion in our industry that associates alpha generation capability with high win rate. LTCM used to boast a win rate above 70%. Yet, their demise nearly took down the modern financial system. By contrast, William Eckhardt, the father of the Turtle Traders, claims a modest win rate of 35%. He has however achieved a remarkable annualized performance of 18% over a 36 year career. It is not how often we win but how much we make that ultimately determines our performance.

In a previous job, I used to run my algorithm across various portfolios. The objective was to help other managers better trade their positions 5bps at a time. Compound this over a year and this is the difference between 2nd quartile and top decile performance. The same stocks kept on reappearing in top ten bets. Managers exchange ideas and have access to the same research. There was a low dispersion of holdings, but there was a high disparity of performance. So, the difference that made the difference was obviously not stock picking: everybody owned the same stocks. The primary determinant of performance was bet sizing.

Looking back at the gain expectancy formula, bet sizing is the component that tells how much we make. It helps us achieve our investment objectives. It is also the most important component for market participants engaged in short selling activities.

In the coming articles, we will look at various position size algorithms, risk management tools so as to help You extract more alpha out of your ideas. We will look at specific techniques designed to help You clarify your objectives and achieve your goals.

Psychology Accounts For 50% of Performance

“If You don’t know who You are, this [markets] is an expensive place to find out”, Adam Smith


Great traders are not smarter, they have smarter trading habits

Unfortunately, bull markets have never boosted anybody’s IQ. We simply get overconfident during winning streaks and start gambling away. Then, during the ensuing losing streaks, we get depressed and take too little risk. In any case, we tend to abandon our discipline. Even systematic traders tend to tweak their models during losing streaks.

We have been conditioned to believe that willpower is the key to success. Unfortunately, willpower is a muscle that tires quickly, particularly under stress. For example, we all know that the key to performance is to cut losers and ride winners. So, we promise ourselves that we will reevaluate positions once stories change. Unfortunately, no plan has ever survived its collision with reality. If we leave this process to our willpower, it invariably turns into an internal debate, where our inner saboteur often convinces us to keep losers in the portfolio.  Inertia creeps in and the next thing we know, our portfolio has turned into a toxic waste junkyard. The problem is: every time we say “Yes” to a loser, we say “No” to a potential winner.

Market psychology is comprised of two parts. It is the ability to execute a trading plan through winning and losing streaks alike. It is also the inner game of investing: the inner alignment from deep subconscious beliefs to daily unconscious routines. “Watch your thoughts, they become words. Watch your words, they become actions. Watch your actions, they become habits. Watch your habits, they become character. Watch your character, for it becomes your destiny”, Mohandas Gandhi

There is a simple, but not easy, solution to change our psychological make-up. According to a 2002 research paper by Wendy Wood, we spend between 45% and 60% of waking time in habitual mode.  Interestingly enough, the study was conducted on young undergraduates, a segment of the population where habits are still highly malleable. Imagine how habitual our behavior can be after 10 years on the job. We probably learned for the first 2 years and then pushed the repeat button ever since.

Great traders are not smarter, they have smarter trading habits. They have developed and practiced profitable behaviors that have turned into trading rituals. The beauty of habits is that they bypass conscious decision process. They become effortless and emotionless over time. Under stress, we ditch elaborate plans and fall back to our habits. This is why installing smarter habits is critical: success is a habit and unfortunately, so is failure.

Fortunately, executing stop losses can be as emotionally intense as brushing teeth. This is a gradual process that starts, not with ruthlessly cutting losers, but with keeping a portion in the portfolio…  It starts small but the compounded effects are immense. The difference between sending a golf ball off course or close to the hole is one millimeter when hitting the ball. If smarter trading habits resulted in a gain as small as 0.02% per trade, the compounded effect over 100 trades would put us in the rare company of market gurus.

At ASC, We are committed to help You build healthier trading habits. In the coming articles, we will provide You with research from the fields of finance and medical sciences, resources exercises, links that will help You change your habits.


“We are what we repeatedly do. Excellence, then, is not an act, but a habit.”, Aristotle

If we want different results, then doing something different is probably a good start. Stock picking is not irrelevant, it is overrated. All it takes to nudge gain expectancy (i-e performance) is to redirect a small portion of our focus to the other components of the success formula.

Great traders are not smarter, they have smarter trading habits. We are committed to helping market participants form healthier trading habits. We will provide You with resources, links, exercises and an App on the Bloomberg portal.

Preview of the next article

  • In the next article, we will introduce a powerful visual representation of gain expectancy
  • Using this tool, we will reclassify strategies across all asset classes in two types
  • We will provide You with suitable risk measures risk for either type of strategy
  • We will introduce a new risk measure: Common Sense Ratio
  • We will provide You with a simple technique that dramatically improve your win rate %