Better System Trader: Questions from the audience

These are questions from the audience on the Better System Trader podcast with Andrew Swanscott. I am honored and humbled by the interest of listeners. We did not have time to cover all questions, so here are some written answers. If You have questions, please feel free to ask

Trading Psychology

From: Jim

The mind plays tricks on us, even with a successful system, as a system trader, what methods to use for the mental part of the system trading?  So meditation, journaling but how to implement them in one overall plan?

EXCELLENT QUESTION: Part 2 of the book will focus on this

  1. You cannot trust your mind. Michael Gazzanikas 1964 split brain theory. Self-deception: (Daniel Goleman) is a built-in feature. It happens automatically and covers its own tracks and designed to deceive us.
  2. Accountability: simple exercise to test validity of prediction and convince us we are unable to predict.
  3. Reframe from outcome to process: develop a system, account for signals generation and be honest about signal execution
  4. Daily market journal: write what You think markets, thoughts, things that happen, small comments, ideas, formulas. Do the James Altucher method: keep a moleskin with You at all times. Deliberate practice: activates the Default Modal Network (Olivia Fox Cabane)
  5. Write about the thoughts that cross your mind:
    1. dreams and aspirations when making money, why You keep doing that, why You like it. How does it manifest in the body
    2. fears, pains, detail, reflexes (ex: read the press, look for expert opinions): be specific and commit to writing or dictating. Very important
  6. Walk through your fears: meditate and manifest your fears. Seneca was history’s first investment banker. He also happened to be the founder of stoicism school of philosophy. He advocated one day a month of living frugally as a form of inoculation.

Another post on the topic:

From: @trader1970

So far as a Trader what is the biggest fear that you have not been able to overcome?  How do you manage this situation?

  • My father had a hemiplegia (brain stroke) when i was 7. He never regained motor skills and speech ability. We fell into severe poverty. As a result, I have a deep seated fear of becoming handicapped and not being able to feed my family anymore. Personal and vulnerable. Markets related fears I can deal with, I am a short seller, this is a versatile skill
  • How does it manifest itself in trading:
    1. Diversify sources of revenue: we have a real estate business that generates enough to cover our primary needs. That provides peace of mind. My family is safe from harm
    2. Frugal lifestyle
    3. Systematically take less risk: when making sizing arbitrage ask yourself, would You be satisfied with earning a little less than You could or losing a lot more than You should ?

 

Position sizing

From: Bass

Tell us more about risk management, Volatility based Stops and position sizing.

  • It really depends on your customers: Investors are like teenage girls: Teenage girls say they want a nice guy and they fall for bad boys. Investors say they want returns but they react to drawdowns:
    1. Magnitude: never lose than what investors are willing to tolerate
    2. Frequency: never be the last person investors think about before going to sleep
    3. Period of recovery: never test the patience of investors
  • Risk is not a story, risk is a hard number: it manifests itself in individual trade risk per trade (RPT), in aggregates exposures. Example: Long small caps / short futures is synthetically residually Long large caps as the index is primarily composed of large caps
  • Volatility stops: swings +/- 3 ATR. Volatility is as welcome as Kanye West at an award ceremony. Bad news, volatility is like Monsieur Kardashian bad manners: it is here to stay. Your job is to ride it and the way to do so is position sizing. For example, biotech and internet stocks are more volatile than department stores for example. So, size them accordingly.
  • used in position sizing. Rank trades by size (bigger first) so as to go for better volatility signature

 

From: Derek

Hi Laurent,

I have been following your website ASC for quite some time and also your answers on quora. I have something related to an answer you had to a quora question In investments, does more risk really equal more return, in the long haul? Your answer immediately clicked with me and it logically made sense to me. Laurent – you may want to quickly summarize what the answer was before we move on to the next part of the question. I’ll ask you what the answer was.

 Would you please elaborate on your convex position sizing method for a risk per trade and draw down module. This was discussed as an answer on Quora. I understand that as you make money you will allocate a larger risk budget using a convex surface with a max risk budget of -0.30. But i do not understand the reverse side of this, the draw down part.  As we get more draw down we should decrease our risk budget again using a convex surface. It starts at 100 and bottoms out at around 35. I do not understand how that part works.

 Also how did you come up with this method? Can you give a practical example of when you used this both for drawdown and additional risk scenarios?

Thank you very much

Derek

Here is a complete article on the topic. Thank You very much for asking

  1. Long Side: people add risk. Short side; frequent squeezes, start from manageable risk then reduce
  2. Metaphor of accelerator and brakes. Optimum fuel consumption happens when You do not solicit brakes. It clicked while listening to Larry Williams interview on the famous Better System Trader after bringing my daughter to the Hoikuen (crèche in Japanese)
  3. Market Value (MV) = AUM * Risk Per Trade (RPT)
  4. Most position sizing formulas will use one side RPT usually to calculate risk. In my case, this is convex so as we make money take more risk. This is accelerator. You want this to be responsive and nervous so to re-accelerate quickly after drawdown
  5. Meanwhile, when strategy stops working, You need to trade minimum risk. The problem with conventional formulas is that brakes are spongy and re-acceleration slow. You can get whipsawed. Which then erodes emotional capital, which leads to downward spiral. (Feedback loop between emotional and financial capital). By allocating a convex surface, AUM drops dramatically very quickly but then re-accelerates as there are signs of life
  6. Practical example: ETF. At the moment squeeze so drawdown, then surface immediately reacts and I naturally trade smaller. Residual open risk in my latest short entry was -0.12%, down from min risk at -0.25%

Trading Edge

From: Marcia

During your interview in episode 32 you talked about the “Edge” formula, which is, I think, ” (%wins X Average Win) – (%Losses X Average Loss) “? Would you talk more about that and what number you are looking for, or, what insights the number gives YOU?  thank you

Thank You very much. I am writing a book on short selling. Part 1 is about how to build a statistical trading edge. Part 2 is about building a mental trading edge. Part 3 is about constructing a portfolio with a positive trading edge. On the Long side, the market does the heavy lifting. On the short side, the market does not cooperate, so building a trading edge is critical

  • I am looking for positive number. I have never looked for a specific number, thank You for the suggestion
  • Use as asset allocation tool:
    • Plot trading edge by side and strategy
    • Pro-rate trading edge
    • Allocate resources (trading AUM or surface) based on trading edge, with floor and ceiling
  • This is useful for multistrats portfolios where You would systematically allocate resources to the best performing strategy

Shorting strategies

From: Adonis

What are the 3 most successful triggers he uses in going short? Does he use daily or weekly charts?

There were originally several variations on two strategies (mean reversion and trend following). Over time i have managed to merge them into one.

  1. Define trend: lower highs, lower highs
  2. Wait for roll-over: maximum information: volatility, swing high
  3. Enter on next bar

Exits:

  1. stop loss: full
  2. trend reversal (logical time exit): entry qualified on the other side happens within stop loss
  3. partial exit: risk reduction, take profit objective is to break even

Now, the delicate part is not in the signal module. Trading suspension for example is not a signal issue but a position size one. If sizes are too small, then trades are rejected. For example, sometimes currency pairs flip-flops between bull and bear. So, we count entries and add penalty for each full exit. This reduces risk per trade. If the overall equity is ain a drawdown, then position sizes get smaller. If they are too small, they are automatically rejected. This allows us to trade more pairs as some of them stop trading.

 

From: Graham

How do you simulate borrowing costs when testing a shorting strategy?

Everything at General Collateral (GC) +0,15% added to slippage. The question is probably related to hard to source issues or crowded shorts.

Do not short issues with borrow >5%, except on the Long side: squeeze box. Do not squeeze people: it is bad karma

 

From: Nikhil

1)  Majority of ideas for a short strategies seem to fail rigorous testing on larger time frames so one should focus on more active time frames [5min to 2H based data] instead of passive time frames [Daily to Monthly based data]  ?

Assumption: Nikhil may trade breakdowns, because this is a classic symptom or rebound higher than entry which leads to false positives.

Solution is not in better entry signal, but in partial exit and better money management, Trading system has 3 components: exit/entry, money management and mental.

 

2) Can you highlight a basic idea on a short strategies variable for further research for those struggling with constructing a short only strategy ?

JNK Short

Sure, check post on JNK attached. It is a scale-out/scale-in system.

There are 2 certainties in life: death and short squeeze. Use squeezes to your advantage

3) What opportunities do you see in the financial industry going forward for new generation of entrepreneurs (non trading/investing related) coming up ?

At the moment, everyone wants to be in the HF game. I entered the HF game in 2003 when it was still in infancy: a bunch of cowboys blowing stuff up in their kitchen. HF is bound for yet another healthy correction.

I believe the future to be threefold:

  1. Algorithmic assets allocation: fire your financial advisor. If You don’t know why, he probably does. Machines do a better job and they don’t get kickbacks…
  2. Separately managed accounts (SMA): open a brokerage account and let algo do the heavy lifting. Funds running costs are prohibitive. Besides, there is a proliferation of single brain cells parasites called compliance. They are the TSA (US airports officers) of finance: utterly useless at catching problems but extremely annoying
  3. Active management “soft patch”: SPIVA.com. The overwhelming majority of funds underperform the index and they are more expensive than ETFs. There is a gambler’s fallacy going on: ETFs have outperformed active managers so far, but the latter will be better equipped to navigate volatility and downturns. That is gambler’s fallacy: if managers failed to outperform during easy times, why would they even succeed during hard times ?

As for non-investment profession, I honestly don’t know

 

From: Ola

I am using market filters to keep me out of bear markets for my long only strategies for stocks, and I’m cashed up for periods of time. I find this a bit boring. What type of indicators or price action should I look for to create a short strategy to complement the long strategies? I’m looking for something simple and robust to be used on the daily time frame.

Best regards,

Ola

Check JNK trade attached. 1 Define trend, 2, enter on counter-trend move 3 exit partially as rebound comes

 

General trading

From: Bengt

Hello, it is often said that short trading is very difficult to make money off: Do you agree with this? If so, do you think it is a matter of the odds not being on your side or is it too much to handle mentally?

EXCELLENT QUESTION: “This is space, the environment does not cooperate… You solve one problem after another, and if You solve enough problems, You get to come home”, The Martian.

Andrew, Allow me to explain why people fail on the short side: they think from a Long perspective. This is deep shit that no-one has ever explained in statistical and psychological terms. Fascinating theme, I am writing the book on the topic and how to build a sustainable short selling practice

Example: 4 stocks: A,B Long C,D short, all start at 100

Start: Long exposure 200%, Short exposure: 200%, Gross exposure: 400% , Net exposure 0%,

A goes up by 10%, B drops by 5%. C drops by 10% and D goes up by 5%

End: Long exposure 205%, Short exposure: 195%, Gross exposure: 400% , Net exposure +10%,

Bottom line:

  1. On the long side, the market does the heavy lifting for You. There is a bigger bet on something good
  2. On the short side, the market does not cooperate: there is a bigger bet of something that does not work
  3. Net exposure is +10%. The main reason why people fail is that they want to short a throw away the key when they should be working more on the short than the long book. Just to stand still they should keep running: this is a Sherlock Versus the Red Queen effect

 

On the other end of the spectrum: is there an outer limit, odds-wise, for profitable long term trading, or is an 800-day breakout tougher to handle mentally than a 2 day breakout?

Best regards: Bengt

The problem is false positives: You will have many more false positives because of poor trend formation with shorter periodicity. You will deal with being systematically late. A more robust statistical approach is to deal with exits so as to move the needle from “near win” (false positive) to “near miss” (partial win)

 

From: Rob

Please ask for the following:

1) What works better in the forex market – momentum or mean reversion?

Mean reversion works until trend following works. It is a question of periodicity and tolerance for stop loss.

My strategy is a combination of both.

Post about two types of strategies:

2) If you had to start over from the beginning with the knowledge you have now where would you focus on and what would you throw away?

  1. Psychology: clarity about beliefs. 90% of trading is mental, the other half is good math
  2. Trading edge is not a marketing gimmick: it is a number
    1. Money management: example of convexity
    2. Exits: stop loss is the 2nd most important variable

3) You have said in the past to focus on exits and not entries – but how exactly do you do this? Is it a matter of thinking about when you will exit if you are right or wrong?

Never think about right or wrong, it is the wrong mental association that will lead to death. Think about profitable. I am writing something on the psychology of stop loss. This article is potentially the most or second most important post I have ever written.

The best analogy is diet. Diets don’t work. We are all getting fatter and there has never been as much information on diet. Diets fix the wrong thing. The problem is not what we eat. The problem is how we think about we eat. Same goes with stop loss and exit.

This is not a mathematical problem. This is a psychological issue about the meaning we ascribe to closing positions. If we associate stop loss with being wrong, the ego will revolt.

IAU option trade anecdote funny and excellent example to talk about emotional capital and Zibbibo viognier white wine blend from Etna

4) What do you think about fixed fractional position sizing

it is a good basis of any position sizing algorithm. Now, it is a bit simplistic for 2 reasons:

  1. Uniform risk taking through the cycle: think of it as a car. Sometimes it is good to accelerate, sometimes You need to decelerate. Win rate changes through the cycle and so should risk
  2. Dissociation: Long and short sides rarely work well at the same time. Since they have different win rate, they should have different risk numbers

Dissociation by side of the book, strategy using trading edge or win rate. Please check my post on convex position sizing

5) Please talk more about stops. you said in the past your stops have a large impact on your P&L – but how do you calculate your stops. What are the considerations when using a mean reversion vs momentum strategy and type of market forex vs futures.

Sure, happy to explain the equation

Now, for mean reversion strategies, the equation includes another variable: frequency. Let me give You a simple example. If you clock +0.5% per month and then have -6% month, it will take roughly a year to make that back if everything else works. So, a simple idea is to empirically come up with a patience factor. Example: never allow losses to be greater than 4 months of average profit. The difficulty though is correlation. Accidents travel in group.

Another important point on mean reversion, never trade open risk strategies. Example: short naked gamma. I was having dinner with some options portfolio managers friends. Short OTM gamma is still marketed to unsuspecting investors. Those are scams: they show consistent returns until they blow up

From: John D

I trade a long term trend following (trade every 1-3 months) system on stocks indices currencies and commodities. What type of exits would you use on this type of system?

Trailing ATR stop? Time stop? Both?

John D, You are right on all of them

Three stops:

I have developed something called box concept. Once in a trade, there are three possible scenarios:

  1. It does not work and needs to be stopped out. That is a floor or ceiling depending on whether You are Long or Short
  2. It works well and warrants some de-risking: take money off the table and leave a portion for the long right tail
  3. It goes nowhere: this immobilizes resources and needs to be dealt with

The concept is that whatever happens, it will trip one of the mines and will be dealt with. This is how it is done in practice

  1. Isometric staircase stop loss: swing +/– allowance for volatility. Markets do not go up in straight lines. They go up or down, retrace and resume their course. This method allows markets to breathe
  2. Partial trailing stop loss: take some money off the table so as to reduce risk, but leave a residual for the big trend. After taking some money off the table, it makes sense to re-enter and a add a little bit more risk.GBPJPY
  3. Time stop: buying power and trading frequency. Some stocks do not move enough to warrant either a stop loss or a risk reduction. These are the harder ones to spot. The solution is to timestamp them.

About timestamp:

 

Convex position sizing algorithm: something your brain can trade through euphoria and depression

Introduction

There are two position sizes: too little or too much. Too little when it is working and too much when it is not. Of course, our inner idiot compels us to take too little risk when we should be bold and vice versa when we should be prudent.

Position sizing is this critical juncture between financial and emotional capital. Deplete the former and it will take effort to rebuild. It is a complicated problem, but not a complex one. Break the latter and “Game Over”.

On the short side, position sizing is even more critical: failures get bigger and painful, while successes shrink away. Over the years, I have experimented with many position sizing algorithms. Many of them were brilliant, but I would always drift away and abandon each one of them after a while. Then, I realised I looked at the problem from the wrong angle. Convex position sizing is the story of my journey

If You have encountered “fear of pulling the trigger” or if You routinely take too much/too little risk at precisely the wrong time, then this position sizing algorithm might be for You.

 

Part 1: The correct mathematical answer may not be the right one

The first part of the journey was to find out why I consistently drifted from conventional algorithms.

  1. Short selling is not a stock picking contest, it is a position sizing exercise

On the short side, the market does not cooperate:

  1. Volatility is elevated: that rules out systems like equal weight.
  2. Concentrated bets is a bad idea, as their volatility drives the short book and consequently the entire book
  3. Short squeezes are frequent: expect all shorts to rally >10% over 5 trading days
  4. During bear phases, correlation goes to 1. Expect Longs and Shorts to go against You at once
  5. Unprofitable trades balloon rapidly. So, the natural tendency is to be conservative and take small risks.
  6. Unlike the long side, there are no 2-3 baggers. Winners shrink and contribute less. So, there is an opposite tendency to oversize positions.

Bottom line: the short side is less a stock picking contest than a position sizing exercise. Winners get smaller and loser get bigger. The difficulty is to size positions so that they contribute when successful, but do not torpedo performance when unsuccessful.

 

  1. Two types of algorithms and two types of people

There are two types of position sizing algorithms: aggressive or conservative. Risk seeking systems will have You bet beyond your comfort zone, and sometimes lose more than You should. System failure means cumulative losses have permanently damaged your ability to bounce back.

Conservative systems will have You bet small and earn less than You could. Failure means returns are not attractive enough, and/or period of recovery after a big loss is too long.

There are also two types of people when it comes to risk: risk seeking or risk adverse. Risk seeking people have higher tolerance for the volatility that comes with bold choices. If they go too far, they may no longer have the resources to bounce back.

Risk adverse people accept underwhelming returns in exchange for low volatility. Their downfall is they are sometimes conservative to the point of being risk seeking. Failure does not mean they aim too high and miss their target. Failure means they aim too low and succeed.

 

  1. Regime Change, transition and drift

Now, the world is not Manichean. There are times when it is wise to be conservative, settle for a risk adverse system, accept to earn a little less than You could.

There are also times when it pays off to be aggressive, ride a risk seeking system, but potentially lose a lot more than You should.

The problem is that most position sizing algorithms are good at either one or the other. They are not equipped to transition smoothly from equity growth to capital preservation. A core principle is that systems must be followed throughout a cycle in order to achieve predicted results.

 

  1. The correct mathematical answer may not be the right one

The problem with many position sizing algorithms is not to find the optimal size that will achieve desired geometric returns. The difficulty is keeping executing through euphoria and depression. Of course, optimal f is the correct position sizing algorithm. The problem is my inner idiot thinks he knows better.

For example, “fear of pulling the trigger” is simply the inner idiot (often referred to as amygdala) saying those bets are too big. This fear gets reinforced after every loss in the thalamus. It eventually gets to the point where the brain overrides the algorithm, but rationalises decisions. Self-deception is insidious, it covers its own tracks.

I did not abandon any of the position sizing all at once. I just gradually drifted away. I failed because my inner idiot constantly second guessed what the algorithms suggested. Discipline is futile. It’s like diet: everyone puts those kilos back on in the end.

I therefore realised that the only way to makes more sense to build a position sizing algorithm that the brain can embrace and then figure out the math.

 

Part 2: Convex position sizing

  1. Philosophy of the convex position sizing

Convex position sizing algorithm was conceived backward. Math is subservient to the brain. It may not be the optimal mathematical solution, but it is one my inner idiot will have no problem executing during triumph and disaster.

So, I started out with a list of demands

  1. Trade at optimum risk: (accelerator)
    1. Accelerate to maximum risk during run-ups, but
    2. Decelerate quickly as soon as there is a drawdown
  2. Absorb volatility: (brakes)
    1. allocate maximum equity, but
    2. reduce risk drastically during severe drawdowns
    3. Avoid whipsaws due to premature re-acceleration
  3. Reduce risk for each new re-entry: (trend maturity)
  4. Simple input variables (risk appetite)

The best analogy is fuel efficiency. Flooring the accelerator and then slamming the brakes is not a fuel efficient way to drive. These are aggressive systems like Kelly criterion, optimal f and Fixed Ratio Position Sizing (FRPS). Driving like Mrs Daisy is lovely, but not necessarily the most competitive style. These are systems like constant Fixed Fractional Position Sizing (FFPS), equal weight.

Convex position sizing algorithm runs at optimum acceleration. It will take on risk as equity curves rises and reduce as it comes down. It will slam the brakes to avert accidents and then re-accelerate smoothly. Risk Per Trade is the accelerator and Equity would be the brakes.

One of the strengths of the algorithm is smooth transition from risk seeking to risk adverse. The algorithm focuses on drawdowns. As soon as there is a drawdown, risk is reduced. Conventional position sizing algorithms focus on winning streaks and thresholds. They are therefore slow to react.

 

  1. Fixed Fractional Position Sizing revisited

Fixed Fractional Position Sizing algorithm basic formula is:

Market Value = Risk Per Trade / Distance to Stop Loss * Equity

Most formulas focus exclusively on Risk Per Trade (RPT). With the notable exception of Market’s Money, few of them consider Equity (capital allocation or surface). The idea became clear to use both sides, one for acceleration, the other for deceleration.

 

  1. Convex Risk Per Trade

In practice, this is what Risk per trade looks like:image (1)

Risk per trade oscillates between a minimum and maximum. Trends mature, so risk per trade is reduced for each re-entry. Convexity comes from the ratio of min/max risk. In this example, min risk is set at -0.25% and max risk at -1%. The bigger the ratio the steeper the acceleration.

How to calculate min and max risk per trade

  1. Max Risk per Trade: Risk Appetite / [AVG number of positions * (Long Term Loss Rate + 2 STDEV(Loss Rate)]
    1. Risk appetite: is not a mathematical number. It is the drawdown investors are willing to stomach before redeeming. Whatever You think that number is, divide it by 2. This is a clear case where You do not want to be right !!!
    2. Long Term Loss Rate: ideally, this is the win rate through the entire cycle. When there is not enough sample data, default to a conservative 2/3. That means 2 trades out 3 will fail. 51% Win rate is for fairy tales, and Prince charming is not coming
  2. Min Risk per Trade: this is the minimum RPT that would still allow trading during drawdowns
  3. Position count: Trends mature. Risk should therefore be reduced after each entry so as to avoid giving back profit on last entries

Risk appetite is one of the two input variable of the entire posSizer algo. Everything else is calculated.

 

  1. Drawdon module

image (4)This is the equity allocated to each trade. The objective of this component is to absorb small daily volatility. As a drawdown becomes severe, surface is exponentially reduced so as to collapse residual risk. Note the slope of the curve. Small recovery results in rapid increase of the surface.

Trading floor: this is the second input variable. This is a percentage of equity balance that will be allocated if drawdown exceeds tolerance. A good example here is Millennium partners. After a drawdown of 5%, equity is automatically reduced to 50% of initial capital.

When investors say they can stomach a 20% drawdown, what they mean is they will think about redeeming after a 10% drawdown. So, it is wise to cushion the blow with this drawdown module.

 

Part 3: Convex position sizing in action

This posSizer runs on auto-trade Metatrader MT4. We trade closer to 30 currency pairs, leveraged at 100:1 on 15 minutes periodicity. This is probably as aggressive as it can be.

It feels like being in a driverless Formula 1, without a steering wheel, pedals for accelerator and brakes. Yet, thanks to this algo, there is no need to stay glued to a screen all day. This posSizer provides priceless comfort when most needed. It will smoothly handle trouble: reduce risk, collapse it if necessary and then re-accelerate rapidly.

This is what it looks like in practice. Below is a hypothetical equity curve (GS stock price). The real equity curve does not have those big drawdowns, so it is harder to distinguish.image

Blue and pink lines are min and max market values per trade (MVPT). Green lines are market values for each position n1 to n4. Orange line is first entry without the drawdown module.

As equity curve rises, MVPT rises in unison. MVPT reacts rapidly to each drawdown but still remains closer to the upper bound until a more pronounced drawdown happens. Risk is reduced for each new tranche.

The drawdown module kicks in during severe drawdowns. This is the difference between the orange and green dotted line. MVPT goes down even further than minimum risk. There are times when even small positions seem too big. This ensures trades go through but at bare minimum risk. This reduces concentration, which in turn sets the stage for a rebound.

One of the problems of FFPS is premature re-acceleration after a drawdown. This leads to whipsaw in sideways markets. This is again a potential reason to drift from suggested positions. After a severe drawdown, the orange line rises faster, while the dotted line adjust re-acceleration to the speed of recovery. For example, the first drop below min risk was followed by a prompt recovery. The second one was more gradual.

 

Conclusion:

Under extreme stress, every degree of freedom, every bit left to interpretation has the potential for costly human error.

Position sizing often overlook the most important component in any trading system: our inner idiot. This algorithm reconciles math and affective neurosciences. It helps us “meet with Triumph and Disaster, and treat those two impostors just the same”, extract from Rudyard Kipling, “If”

In investments, does more risk really equal more return, in the long haul?

Answer by Laurent Bernut:

This morning Palermo time, Andrew Swanscott from Better System Trader podcast interviewed me. The above question came up.

The answer is Yes and it is No, the real question is when. Best analogy is driving, will driving faster get You to destination: Yes when on the highway but No when in downtown.
I define my identity as a professional short-seller. As such, I have a different relationship with risk than most people. There is an interesting paradox in short-selling:
  1. If You are wrong, your position balloons and hurts immediately
  2. If You are right, it helps less and less
So, the whole game is of short selling is about position sizing and risk management:
How can positions be sized so that they would contribute but not hurt ?
This is probably one of the tallest order in fund management.

Between Charybdis and Scylla: Open Vs closed risk dichotomy

People perceive risk as either static, as in constant or completely random.

The perilous trip of the ship of Ulysses between Scylla and Charybdis.

The perilous trip of the ship of Ulysses between Scylla and Charybdis.

Well, it is somewhere in between and it depends on how You trade risk in the first place.

It reflects back on the concept of open versus closed risk. Open risk is the tropism of mean reversion strategy. Everything hums fine until the big iceberg. Closed risk means risk is capped.
Your view of the world will shape your risk profile. If You run an open risk model then because of its inherent unpredictability You are condemned to run it at low risk ad perpetuitam.
If You run a closed risk, then You can accelerate and decelerate within the bounds of your risk tolerance.

Accelerator and brakes

This is one of the most profound discoveries I made in 2015. there are two types of people when it comes to sizing a bet: those who take risks and sometimes get hurt along the way and the risk adverse crowd who will consistently take minimal risk.
I think this relates to the essence of the question: Can I build a system that preserves capital when strategy does not work but takes risk when it  does.
I think I can answer this one with good wisdom. Please read this post:
The thought behind the math was this: is there a middle ground between pedestrians and F-1 racers ? I think I found the formula. Please read the above post. We have used it and it does wonders, beyond what i theoretically expected in fact:
  1. When good times roll over, risk per trade is extremely responsive: brings risk to minimum right away.
  2. Concentration decreases: smaller risk per trade means smaller positions, means lower concentration , more positions, diversification
  3. But because surface does not change dramatically, position sizes are fairly reasonable. They do not swing from 0.15% to 15%
When the sh*** hits the fan, everything goes into Guantanamo, but it can still trade and thereby reboot itself.
  1. The skew of the convexity means that every marginal cgains translates into buying power restoration
  2. That posSizer is the best sleeping pill i know
Conclusion
I am sorry if i came across as boasting this position sizing algorithm. The point was that nothing is static. The answer You are looking for is in your position sizing algorithm.
Subscribe to my website to get free material, resources. Subscribers have free resources, files, code. Moreover, your feedback keeps me going on Quora
In the end, ask yourself this question every time You think about sizing a position: can I live with earning a little less than I could or lose a lot more than I should ?

In investments, does more risk really equal more return, in the long haul?

What is the difference between stock trading and gambling in a casino?

MonteCarloAnswer by Laurent Bernut:

I’ll give You the same answer I gave two CIOs of Fidelity. The common point between professional poker players, star fund managers and street hookers is that they go to work: it is not meant to be fun.
Excellent question. Beyond taxes and manufactured negative gain expectancy, there is much market participants could learn from professional gamblers:
  1. Gambler’s serenity prayer: grant me the serenity to accept folding a losing hand, the courage to take calculated risk and the wisdom to know the difference
  2. Cut losses and run winners: in poker, money is made by folding a lot and be aggressive a few times. Successful fund managers spend their time cutting losses. The paradox is that the way to win the war is to accept losing small battles
  3. Position sizing: Black jack is a game where You play against the house. It is manufactured to have You lose. Yet, Edwin Thorpe, whose track record towers Warren Buffet’s, beat the dealer. His method forced casinos to adapt. His secret sauce was position sizing, a fraction of Kelly criterion
  4. Position sizing algorithms: Gambling is a far more mature industry than investing in the sense that a lot of position sizing algorithms used in finance come from game theory. Martingale, reverse-martingale, drawdown/run-up of bankroll, Kelly Criterion
  5. Gambling is boring: hookers, poker players and star managers go to work. It is not meant to be fun. They leave their emotions at the door. Treat gambling and markets as a job so that You can fleece the emotional players
  6. Gamblers have a system: gamblers are not smarter, they have smarter gambling habits. Adherence to a system takes discipline. Reinforced discipline is called habit
  7. Gambling as trading is not a zero sum game: one of the most common myths about the market is the zero sum game. Slippage, commissions erode however slightly the account. Take every trade as if You put a chip on the table
  8. Quantified risk: the notion of calculated risk has unfortunately been perverted by those who do not understand it. Risk is not an abstract dissertation at the end of an investment thesis. Risk is a hard cold probabilistic number
  9. Odds and win rates: one of the fallacies of market participants is the belief they need above 50% win rate to be successful. 2 things here: 1. trading edge or gain expectancy shows that low win rate can be compensated by big payouts. 2, Distributions of P&L of most traders (excluding mean reversion and market making) show aggregate win rates over the cycle of 30-45%. Winners compensate for losers. The important lesson here is that traders walk into a trade expecting it to win, when they should be mentally prepared  for a loss. Pre-packaging grief (see my post: The view from the short-side: how we process emotions and the market signature of the 5 stages of grief Kubler-Ross by Laurent Bernut on Alpha Secure ) . This means that throughout the cycle, styles come and go. Making money means knowing when your style is out of favour and betting small and then when in fvaour take risks. Back to the serenity prayer
Conclusion
Investors usually look down on gamblers. Yet, there is much to learn from gamblers. How come a few of them become successful despite built-in unfavorable odds ?
Beginners in both markets and gambling believe they are on to something when they double down after each loss. They believe that their luck is about to turn, so they use martingale (it comes from the French for winning streak). They just forget two things: dice have no memory so each run is independent from the previous one. More importantly, the maximum expected value is break-even. This means that any outcome other than the best one carries an interesting probabilistic property called “certainty of ruin”.
In other words, there is a reason why casinos have gold, marble columns, master paintings and rookie gamblers go broke…

What is the difference between stock trading and gambling in a casino?

Has anybody gotten rich through automated trading?

Happy New Year from Alpha Secure Capital. This was an answer to a question on Quora. It has been read by more than 16,000 people.

Now, I am a digital nomad investor: Viet Nam, Singapore, Tokyo, KL, Venezia, Palermo, Reikjavik. Rents get paid in our sleep, balance gets bigger by 1-3% every week. Dream life, hey (*) ? Well, it came at great sacrifices.

Autotrade sub 30 mn is the tallest order in the trading industry. On the one hand, there are HFT shops, with whom there is no point competing. They already do a wondeful job at killing each other not so softly. On the other hand, point and click prop shops ecking penny after penny. Then, there are Delta one and deriv desks arbitraging small corners away. All those guys have the money, the resources, the access, the info, the programmers You will never have. You are outgunned, outnumbered and let’s face it: outside. Now, let the race begin.

It took me 15 years to mature the concepts, 3,694 hours to code, 3 2/3 years to run  and a lifetime to refine them. This has consumed my life, my waking hours, my sleep. Ever woke up breathless and feverishly write equations ? I nearly burned the house not once, but twice, because i forgot that there was something on the stove, while i was wrestling with some C#. Once, my wife came yelling at me for not taking care of our screaming baby. I just did not hear our daughter crying… on my lap. Well, code would not compile…

Sisyphus stones
Then, there is the sheer frustration of never being enough. Then there are bugs. One rule of thumb, never add, always subtract, always come to simplicity when solving bugs. Then, there are “100 year flood”, perfectly rhyming with the late “100 nights of solitude”. Then, there are platform issues. They are not meant to do scale-out/scale-in and adaptive position sizing. Then, there are those small issues that You will have to face one after the other.  There will be times where You wander and meander like Ulysses, “what if this, what if that ?” But there also those immensely gratifying days when You wake up with light and equations flowing through like when I found my personal holy grail of position sizing

After the Daedalus of development, one day the end will be in sight; it will be there, almost, just a few modules away. But then, there are those shortcuts You took 10 iterations ago that will come back and bite You. They stand between You and the finish line. And You know that tackling them means overhauling the entire architecture.
This is the realm of frustration. The last mile is always the hardest. Please remember this though: autotrade is like watch-making. Until the last cog fits in the right place, your clock will always be off, so don’t give up, never give up.

Then, You run your own money, face drawdowns, go back to fix the last few bugs. Then, You run it on small amounts. The best moments are not when You make your previous monthly salary in a week while kitesurfing or going wine tasting. The most beautiful moments are when You make those few hundred dollars week after week and when You finally know it is viable. It feels like watching a flower blossom. This is the best sleep You will have in your lifetime, well at least for 3 months …

Here are the lessons I learned. A viable trading system is built backward:

  1. Focus on the short side: the short side is notoriously harder. If Your system works on the short side, it will work on the Long side. Any 3 star Michelin chef can flip burgers. Now how many Burger king employees can do 3 star meals ?
  2. Focus on the exit first: a race is never won until the finish line is crossed. Some of your positions are marathonians, some are sprinters. You never know until You see them on the field.
  3. Stop loss: it is the only variable that has a direct influence on 3 out of 4 variables of your trading hedge
  4. Money management is key: how to preserve capital when your system won’t work and how to take calculated risk when it does ? This is where the heavy mathematical artillery should be concentrated, not on the entry. Think about it: everyone owns Apple. The difference that makes the difference is how big You are
  5. Simplicity: complexity is a form of laziness. If your solution is still complex, it means You have not worked hard enough to find a simple one. There is no exception to this truth
  6. Symmetry: once the short side delivers, translate it to the long side. You will have unambiguous signals, unified risk management
  7. Watch Star Trek and the original Kardashians, they were not as villains as the newer ones, breaking bad, desperate house wives etc
  8. Then, last and very least, but first take the dogs out. And then finally, sorry don’t forget to water the plants first. And then finally, oops have You called your mother yet ? And then finally, take the trash out and after a good night of sleep, You may think about entry. Entry is at the very bottom pile of the priority list of an autotrade strategy, long after labeling priorities on multiple positions

In the end, You will realise that the goal was never about money. It was first about the freedom from a paycheck and the long term uncertainty of retirement. Rich and wealthy are not synonymous. Rich should be the experiences You accumulate over your life. Now, we live out of our suitcases, frugally as usual, but what a life! Speaking of which, time for a Prosecco with our neighbours, our landlord the architect and his buddy the last Gondola maker in Venezia

(*) Now, the highlights of our week is to hunt for consecutive stop losses. We have excess capacity. We have suffered a great deal coming up with our strategy on MT4. Most modules had to be built from the ground up. We  genuinely want to spare this Sisyphean ordeal to aspiring autotraders.
So, we will choose 2 or 3 people and help them build their strategy.
I can help anyone formalise their own strategy through a thorough guided discovery process. This is not pleasant.
Then on the MT4 coding side, the person I work with is a senior programmer for the US Department of Defense (be nice to him or he will bring democracy to your computer…). I can code alright, but his stuff is military grade… Reach out if You are interested, or if You like what You read

Has anybody gotten rich through automated trading?

How complex are the algorithms used by financial institutions such as Goldman Sachs and other hedge funds in their proprietary trading so…

This is an answer to a question on Quora. It got re-posted and shared across Quora users.

Answer by Laurent Bernut:

Complexity is a form of laziness
Complexity is fragile: it works until it does not

There are two types of algos: low-latency and systematic algos.

Low-latency is the realm of HFT. Those algos can be quite intense. Read dark pools or flash boys. There are now algos gaming other algos. It is a bit like the “sperm war” in the “red queen”, a book on evolutionary psychology.

At the other end of the spectrum is systematic trading. It stems of the belief that if investment is a process, then it should be automated. Those algos are conceptually easy to understand. They are not however always easy to program.

At the end of the day, algos are a reflection of the philosophy, beliefs of those who design and code them. Those who have not mastered their craft will gladly put lipstick on a pig, by adding complexity to flawed concepts.
Those who have worked a bit harder will simplify. Simplicity is not easy

Sorry for the philosophical answer

How complex are the algorithms used by financial institutions such as Goldman Sachs and other hedge funds in their proprietary trading so…

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:

Platforms
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

Tradestation
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?

People talk about an edge in trading yet never want to discuss what their edge is. Can you give an example of an edge that no longer work…

Answer by Laurent Bernut:

Edge can be summed up in a formula that applies to any instrument (see below). Like risk management, everyone like to talk about it, but few people actually can distinguish between sustainable, stylistic, and systemic edge

I. Examples
Let’s start with examples:
Chinese IPOs: Institutional and some retail investors participate in IPOs. Classic trade is to sell half to 100% of the allocation at the Open of the first day and book the disount. Chinese IPOs used to go up 100% in 3-4 days.
That “edge: is clearly gone now that they open 50% below and that “investors”/flippers have to keep their holdings for a certain period.
More on Chinese IPOs later when we talk about specific versus general

Stock split: there was a strange loophole in Japan, where stock split were announced 2 weeks before effective dates. So, the trade was to go Long prior, reverse the trade and go short at the close of the day before the split went ex-date.

Korean USD trading
In Korea, foreign participants had to trade in USD, while locals were allowed to trade in KRW. And then You wonder why some markets never seem to go anywhere ? Borrow availability was vastly different.

A shares B shares in China
Foreigners were not allowed to trade A shares.

Prop trading:
This is probably one of the most egregious “edges”. Prop traders were carrying their facilitation business and encouraged to hold their own book…

II Types of edge
Systemic edges:
Locals in China and Korea were legally able to milk foreigners. This had nothing to do with talent. Arguably, it might have encouraged complacency. This is systemic edge. Another example of systemic edge is Ali Baba. There is no real competition from other shopping malls.

Those edge exist as long as regulatory inefficiencies are there to protect them. They are fragile, fickle but can last for some time.
They sometimes can be double edged. In their panic moves to contain the fall, Chinese authorities have asked some brokers like CITIC to engage in price stabilisation activities.

Stylistic/cyclical edge:
Some people know how to trade GARP (growth at reasonable price). Some know how to trade value. Others know how to ride Beta.

Their edge last as long as markets reward their style. Unfortunately, investors find it difficult to distinguish between skill and style. In other words, are managers good (or bad) because of what the markets reward (or punishes), or are they good because they adapted to what the markets reward.
There has been a lot research on the topic. Quants sometimes refer to this as luck versus skill.
A simple, down to earth trick to find out is: how do those managers fare when market style changes ? Does their performance suffer much more than the markets or can they contain losses ?

Sustainable edge
The rarest form of edge and the least talked about is sustainable edge. The reason why few people talk about it is because either deep down they know they do not have anything special, or they believe their outer game constitute sufficient sustainable edge.
For example, biotech analysts clocking 2-3 baggers one after another was great until a presidential candidate stopped being supportive of the industry. Unfortunately, no bull market has ever boosted anybody’s IQ.

III. So, how do we measure trading edge ? Where do we find examples  and can it be replicated ?

A. Trading edge formula
Every trading strategy, investment method, track record that has, is and will be boils down to this formula:
Trading edge = Win% *Avg Win% – Loss% * Avg Loss%
This is also known as gain expectancy. This is just another fancy way to say average profit.
Guys studying finance have never heard of that one. They have heard a lot about efficient markets from guys with zero beginning of real track record…

Robustness of the edge can be tested via a t-stat. Van Tharp has popularized the formula by rebranding it into SQN: System Quality Number

t-stat = SQRT(trades) * Trading Edge / STDEV(Loss)

This t-stat recaptures frequency and deviation from the mean. In other words, it is OK to have a spectacular trading edge but it is quite useless if it trades every Friday 13th full moon (next one in 2017 and the after in 2023)

B. Examples of sustainable trading edges
In order to constitute a valid trading edge for any specific “investment obedience”, trading edge must be transmissible across the “trading tribe”

This immediately eliminates discretionary fundamental investing. Everyone in the fundamental analysis business likes to make reference to Warren Buffet. Yet, very few have a comparable performance.
I do not hate fundamental analysis, quite the opposite in fact. A good analogy is rock n’ roll. ELVIS and Ted Nugent are both rockers. Yet, no-one would ever dare putting The KING and that trigger happy human evolutionary challenged specimen in the same basket.

I wish I could say Value investing has some sustainable edge. Unfortunately, like fundamental analysis, the word has been perverted and used by everyone with a marketing pamphlet.
There is evidence of sustainable edge in systematic value investing.

Systematic trend following CTAs have a systematic edge. Their style has generated alpha over decades. Trading edge is transmissible: newer guys seem to fare well. (for disclosure purposes, I am not a CTA)

C. Can it be replicated ?
This is probably the most important question. No-one really cares about their neighbor  trading edge. What they really want to know is: how can i develop my own trading edge ?

This is where the conversation takes on a different perspective. There are only two types of edge: specific and general.

Systemic, stylistic/cyclical edges are specific. They focus on a particular edge over the rest of the competition.
“Stock pickers” focus on getting this or that particular stock “right”. This is extremely specific.
General edge on the other hand is about making sure the above formula has a positive sign before the number.

The reason why CTAs have a sustainable replicable edge is not because they pick better stocks. They are not trying to be right on their view about lean hogs or German bunds. They trade the same asset class across various markets. That asset class is called risk. It has a number attached to it.

They can maintain a sustainable edge not because they try to be specifically right, but because they choose to remain generally right.

In other words, this is outcome versus process thinking.
Process can be formalised and quantified. As the great motivational speaker Jack Welch used to say: everything that can be quantified can be improved.

D. How to improve your edge ?
“He who controls the past controls the future”, 1984 George Orwell

Very few market participants have the courage to keep an honest record of their past trades. We are our trading history.
For example, my trading history is part of my every day position sizing. How i size my positions today is conditioned by how well past trades have performed. If they perform poorly, then I should trade smaller so as to preserve both financial and emotional capital.

1 Know your style
The first is to build a P&L distribution of your trades and find your style. There are only two types. Please look at my blog about mean reversion and trend following. Know and understand your style.

2. Know when your style stops working
It is great to be the best figure skater, but August tends to be a bit dull.
The simplest way to improve your edge is to limit drawdowns when your style goes out of favor.
Remember this very simple truth: profits look big only to the extent that losses are kept small. hardly rocket surgery, isn’t it ?

3. Restore normal risk when your style is in favor
This one goes against mainstream that encourages You to take risk when it works. The problem with this is how much is enough ?
Kahneman says we suffer from chronic overconfidence. That means we take too much risk and then get humbled.
So, my suggestion is not to take on more risk when it works. My suggestion is to take less risk when it does not.

Conclusion
Long answer, but prosecco was good this evening

People talk about an edge in trading yet never want to discuss what their edge is. Can you give an example of an edge that no longer work…

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.

http://bettersystemtrader.com/032-laurent-bernut/. 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:

TradingEdgeVisualiser

Conclusion

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.

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?