Algorithmic trading: How to get started building an algorithmic trading system?

This is an answer to a question on Quora: Algorithmic trading: How to get started building an algorithmic trading system?

Answer by Laurent Bernut:

If investment is a process, then the logical conclusion is automation.
Algorithms are nothing else than the extreme formalisation of an underlying philosophy.

This is the visual expression of a trading edge
Trading edge = Win% *Avg Win% – Loss% *Avg Loss%
It changed my life and the way I approach the markets. Visualise your distribution, always. It will help You clarify your concepts, shed light on your logical flaws, but first let’s start with philosophy and belief elicitation

1. Why is it important to clarify your beliefs ?
We trade our beliefs. More importantly, we trade our subconscious beliefs. “If You don’t know who you are, markets are an expensive place to find out”, Adam Smith
Many people do not take the time to elicit their beliefs and operate on borrowed beliefs. Unanswered questions and faulty logic is the reason why some systematic traders tweak their system around each drawdown. i used to be like that for many years.
Belief elicitation exercises:

  1. The Work by Byron Katie. After i completed a 2 beliefs a day challenge for 100 days, i could explain my style to any grandmother
  2. 5 why ? Ask yourself a question with “why” and dive deeper
  3. “I am”: beliefs about self are best elicited when we start with this sentence stem
  4. Sentence completion exercises: Nathaniel Branden in his work on self-esteem has pioneered this method. For example, “to me a robust trading is…”

Mindsets: expansive and subtractive or masala smoothie Vs band-aid
There are two types of mindset, and we need both at different times:

  1. Expansive to explore concepts, ideas, tricks etc. Expansive mindset is useful in the early stage of strategy design
  2. Subtractive: to simplify and clarify concepts. Complexity is a form of laziness. Instead of adding yet another redundant factor, try subtracting one or two. Work hard until You find an elegant and simple solution

Systematic traders who fail at being subtractive have a smoothie approach. They throw all kinds of stuff into the optimization blender and waterboard data until it confesses. Bad move: complexity is a form of laziness.
Overly subtractive systematic traders have a band aid mentality. They hard-code everything and then good luck patching
“Essentialist traders” understand that it is a dance between periods of exploration and times of hard core simplification. Simple is not easy
It has taken me 3,873 hours, and i accept it may take a lifetime

2. Exit: start with the end in mind Counter-intuitive truth
The only time when you know if a trade was profitable is after exit, right ?
So, focus on the exit logic first.
In my opinion, the main reason why people fail to automate their strategy is that they focus too  much on entry and not enough on exit.
The quality of your exits shapes your P&L distribution, see chart above
Spend enormous time on stop loss as it affects 4 components of your trading system: Win%, Loss%, Avg Loss%, trading frequency
The quality of your system will be determined by the quality of your stop loss,

3. Money is made in the money management module
Equal weight is a form of laziness. The size of your bets will determine   the shape of your returns. Understand when your strategy does not work and reduce size. Conversely, increase size when it works.
I will write more about position sizing on my website, but there are many resources across the internet

3. Last and very least, Entry
After you have watched a full season of “desperate housewives” or “breaking bad”, had some chocolate, walked the dog, fed the fish, called your mom, then it’s time to think about entry.
Read the above formula, stock picking is not a primary component. One may argue that proper stock picking may increase win%. Maybe, but it is worthless if there is neither proper exit policy, nor money management.
In probabilistic terms, after you have fixed exit, entry becomes a sliding scale probability

4. What to focus on when testing
There is no magical moving average, indicator value. When testing your system, focus on three things:

  1. False positives: they erode performance. Find simple (elegant) ways to reduce them, work on the logic
  2. periods when the strategy does not work: no strategy works all the time. Be prepared for that and prepare contingency plans in advance. Tweaking the system during a drawdown is like learning to swim in a storm
  3. Buying power and money management: this is another counter-intuitive fact. Your system may generate ideas but you do not have the buying power to execute. Please, have a look at the chart above

I build all my strategies from the short side first. The best test of robustness for a strategy is the short side:

  1. Thin volume
  2. brutally volatile: this
  3. shorter cycle: stocks go up the elevator and go out the window
  4. Borrow availability:

I started out on WealthLab developer. It has a spectacular position sizing library. This is the only platform that allows portfolio wide backtetsing and optimisation. I test all my concepts on WLD. Highly recommend. It has one drawback, it does not connect position sizer with real live trading.

Amibroker is good too. It has an API that connects to Interactive brokers and a decent poisition sizer.

We program on Metatrader for Forex. Unfortunately, Metatrader has gone down the complexity rabbit hole. there is a vibrant community out there.

MatLab, the expensive weapon of choice for math graduates. No comment…

R: much better cost-performance than Matlab, simply because it is free

Perry Kaufman wrote some good books about TS. There is a vibrant community out there. It is easier than most other platforms

Final advice
If You want to learn to swim, You have to jump in the water. Many novices want to send their billion dollar ideas to some cheap programmers somewhere. It does not work like that. You need to learn the language, the logic.
Brace for a long journey

Algorithmic trading: How to get started building an algorithmic trading system?

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.

Please subscribe and get some files, material and resources. This is all free so take advantage of it.

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!

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