When running multiple strategies, should position size be changed based on the drawdown of individual positions, systems or the system as a whole?

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This question was originally posted on Quora. Something really cool dawned upon my drunken stupor lately and i modified a chapter in the book. By group of systems, let’s assume you refer to strategies.

One-size-fits-all position size algo is a form of laziness

Most people have a one-size-fits-all position sizing algorithm across all their strategies for the long and short side, through drawdown and run-ups. That is as efficient driving the same car in the same gear uphill, downhill, downtown or on the highway.

Two possible explanations for that:

  1. hmm, never really thought of that
  2. you gotta soldier on through the skinny days to reap the fat days.

Money is made in the money management module

How you bet determines how much you make. Over a small data sample, stock picking might make a difference. Over a complete cycle where style goes in and out of favor, position sizing is the main driver of performance.

Now, let’s reframe the question: Should position sizing depend on the equity curve, the strategy, the side (Long/short) and/or the individual security traded?The answer is all of the above.

The position size that you are about to take should reflect:

  1. how well you are doing at the moment in general: The idea is to take more risk in run-ups and less during drawdowns
  2. the strategy you are about to trade: Different strategies warrant different position sizing algos or at least different values for the variables. Example: high win rate mean reversion and low win rate trend following etc
  3. the side you are trading on: long or short, they have different win rates and expected pay-offs
  4. the instrument: let’s say you got 3 false positives in a row, you might want to take less risk. This is an individual penalty ledger. Conversely, if you ladder in or scale up, you might want to depreciate risk for any additional position

Putting everything together

This gives a pyramid like this

Now, the first layer is the risk adjusted account balance. I will not go into details on how you calculate this, but in practice either your equity curve or your account balance would do.

Drawdown module

Then, You want to stick a drawdown module, that would reduce the capital allocated based on your current balance versus the peak. Simply said, if You are in a -5% drawdown, you might want to allocate only 50% of the capital you would normally allocate. This drawdown module has a shape like this:

For reference, the black line is what Millennium Partners does to managers: they reduce the book by half after a -5% loss. The green line is a conventional arithmetic proportional reduction. The red is my own take on this concept. Formula is proprietary

Strategy and side stats:

This means you need to keep a record of the stats for each position sizing algo you use for each strategy on each side. This is still at the portfolio level. Example: let’s say currently trend following has 40% win rate on the long side and 20% on the short side. Mean reversion has 60% Long and 30% short etc.

This tells you which strategy works on which side etc.

Now, let’s move down to the individual instrument level

Penalty ledger and game theory

Now, this is where things get really interesting. Some market participants like to use game theory for stock selection. I believe game theory is better suited for position sizing.

The difference is: using game theory for entries is a binary choice: either in or not in. There is no learning there because you do not keep stats on the choices you did not make. Unless you are a creepy stalker, you do not keep tabs on the women you did not marry…

At the security level, you have stats over how well each position has performed. Reward those that did well and punish those that did not is a simple elegant heuristic. This is where game theory really works well. The algo we use is a child playground game, worked for centuries. It has consistently defeated sophisticated game theory algos in iterative contests. You want to know which algo it is, go pick up your kids a bit earlier today.

Pyramid depreciation

Adding tranches to an existing position is called scaling in/laddering/pyramiding. This is a staple for trend followers. Now, trends are born, grow, mature and eventually die, a bit like believers in the Efficient Market Hypothesis. So, you need to depreciate risk as you add new tranches. A simple way to do this is:

depreciation rate = 1/(1+n),  with n=0 before you enter your first position

Now, multiplication has this wonderful property called transitivity, that allows us to blend everything together in a condensed formula that looks like this:

Size = Capital * drawdown module * penalty ledger * depreciation * f(strategy stats, side stats)

Trade rejection, asset allocation, and regime change

And the best for last. Once you grind your variables through the above formula, out comes a position size. then, you want to have a trade rejection hurdle. Below x% or x(MV) position size, trade is rejected.

What it says is simple. One of the ingredients in your basket is rotten:

It could be either: 1) you are in a drawdown, 2) the strategy that is out of favour now, 3) the side is not working, 4) the instrument itself has a frustratingly low win rate, 5) You have already reloaded a few times. Whatever the reason, this is not a fat pitch, so you want to keep your powder dry.

This has implications on asset allocation and regime change. Let’s say you run a multi-strat book. Your amount of capital is finite. So every position has to compete for cash.

By systematically weeding out the ones with low score, you end up privileging the strategy that works the best under the current regime. This means you would take a lot of mean reversion trades in a sideways market, while the trending algo would suck air. Then, as Ms. market feels like trending, your trending positions would lift the overall trending strategy stats and get a better allocation. Now let’s say the trend would be down, your long trending stats would deteriorate and the algo would be naturally more selective.

Conclusion

Position sizing is a vastly under-appreciated topic, despite being the primary driver of long-term returns: it is not what you pick that makes the difference, it is how big you size them. Position sizing can achieve much more than just delivering returns.

Some of the thorniest issues for multi-strat managers are asset allocation and regime change. Well, your position sizing algo can do the heavy lifting for you to allocate between strategies and navigate regime change.

For more on the topic of position sizing, check this blogpost on Quora: Simplified-Convex-Position-Sizing-Something-Your-brain-can-trade-Part-II

 

 

Will traders still be able to compete with algorithms in 5 years?

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Algos have already taken the lion’s share of trading volume across all markets. 80% of Tokyo Stock Exchange daily volume is done via algos. If investment is a process then automation is the logical conclusion

First, ETFs will displace discretionary participants, not algos

If every person contributing to their 401K asked themselves this simple question: “do i want to retire on numbers, or on stories?”, then the assets under management of the active management industry would melt twice as fast as the arctic polar cap.

Algos are not about to change the way discretionary participants work anytime soon. ETFs will. Investors have gradually woken up to the fact that the only competitive advantage provided by rows of analysts and company visits has been a marketing argument, not a trading edge.

Evolution does not take prisoners

Active managers who want to survive have to up their game. That means better risk control, better stop-loss policy, better bet sizing, better execution, better emotion control. In a nutshell, that means better process. If investing is a process, then automation is the logical conclusion. Discretionary investors will gravitate towards algorithms to remain competitive

Even if they choose not to automate their systems, discretionary traders should formalise their process to the point of being computer coded. This sheds light on the blind spots in their process.

5k Views · 62 Upvotes · Answer requested by Roman Gabay

Upvotes62

Are forex signals useful in making profits?

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This question originally appeared on Quora

 

This great question has done a remarkable job at baiting scam-artists. Whoever answered “Yes”, followed by a link to some website is a scam-artist

The one-eyed scam-artist leading the blind

Everyone wants to gain this mysterious esoteric called trading edge. Well, trading edge is not a story, it is a number and here is its formula:

Gain expectancy = Win% * Avg Win% – Loss% * Avg Loss%

Tattoo it where you can be reminded throughout the day (Tramp stamp on your better-half is probably off limit). There are two modules to any trading strategy:

  1. Signals: exits first, last and very very very least entries
  2. Money management: position sizing

The scam artists who answered yes, referred to hereafter as Yes-men will provide dummy statistics about entries but conveniently forget everything thereafter. Entry is easy. You can buy a boat, a sports car, a vacation home any day of the week. Good luck reselling it. Bad entry can be salvaged, bad exits can’t.

Then comes position sizing. Signals conveniently omit position sizing for a good reason. It is a function of win rate and stop loss, both of which are either omitted or optimistically manufactured…

Implicit adoption of a trading system

Now, who does not like to wake up, receive a bunch of signals, pick one or two, send the trades and watch the money roll in? I certainly would love to, except it does not work like that.

Even if signals are thorough enough to encompass all the necessary elements from entry to exit including risk, it still omits the most important: following someone’s signal is adopting her belief system, formalised in a trading system. Example: as a professional short seller, i am naturally risk-adverse (prudent position sizing) but disciplined (take the trade, follow the system). This philosophical tenet transpires in my algorithms. This may not jive with someone who likes big high conviction bets.

Even supposedly objective systems such as deMark have several subjective degrees of freedom. The best example comes from the Market Wizards series. Those wizards have completely different strategies that sometimes seem to contradict each other. Yet, they are profitable because they have a system that suits their personality. When You follow someone else’s system, remember that you half-heartedly embrace someone else’s personality without fully understanding it.

How to spot a scam artist?

First, those who answered Yes and attached a link are scam artists. Coming out was not so tough after all, was it?

Second, here are classic red flags:

  1. No real money live track record = SCAM: Myfxbook provides live track record, so no excuse
  2. Entries only: no exit, no stop loss, no risk, no stats means SCAM
  3. Rosy stats: 70%+ win rate means SCAM. We all want to follow a system that is right 100% but the reality is the best traders LT average win rate is around is largely below 50%
  4. easy money: no you will not turn $500 into $2,000,000 in 2 years. Veteran pros with decades of experience dream of achieving 20% per annum. Scam artists who promise you can achieve 100% returns= SCAM

Conclusion

Basic rule of thumb: we are in finance, assume everyone is a scam artist until proven wrong. Good news, You can cross a few names off the list

How do I get better at trading?

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How do I get better at trading? by Laurent Bernut

Answer by Laurent Bernut:

“Hope is a mistake”, Mad Max, post apocalyptic road poet.

3 declinations of the same principles: process

A. Trading edge is not a pretty story , trading edge is a number

Every strategy ever traded boils down to this formula:

Gain expectancy = Win% *AvgWin% – Loss%*AvgLoss%

Your survival only depends on how You can tilt the distribution. Regardless of the asset class, there are only two styles: mean reversion and trend following.

  • Open mindset: there is a nugget in everyone’s story
  1. Read the classics: Lefevre, Schwager, Covel, Van Tharp, Loeb
  2. Podcasts: Michael Covel, Andrew Swanscott, Barry Ritholtz
  3. Investment newsletters are to investment what mangas are to literature, Unsubscribe, no exception
  • Stock picking is vastly overrated
  1. Plain vanilla fundamentals is not enough: 3/4 of professional managers underperform; over 3/4 of them claim to be fundamental stock pickers
  2. 90% of market participants focus on stock picking and entry. 90% of market participants fail. Causality and correlation: unsubscribe from all newsletters
  3. never enter w/o an exit policy: Once in a position, there is 100% chance You will exit. W/o exit plan, 90% chance the market has sth nasty in store for You
  4. Money is made in the money management module. The single largest performance discriminant is bet size: process and math

B. Portfolio management process

  • Risk is not a story in China, Risk is a number

Risk is not a story. Risk is not a high Sharpe ratio or low VAR. Risk is how much You can afford to lose per trade and cumulatively. Whatever You think your risk budget is, divide it by two. By the time You have lost half your budget, You will be a different person, gripped in cortisol and CRH, paralysed by fear.

  • Write strict investment guidelines: risk, exposures, objectives

“People live up to what they write down”, Robert Cialdini. Formalise your process in writing. Execute. Simplify. Running a portfolio w/o strict guidelines is like building a house w/o a plan

C. 90% of trading is mental, the other half is solid math

Above all else, any trading system is worthless w/o the right mindset: process over outcome

Examples of outcome vs process:

  1. Stop loss override: ego over process
  2. Close a position too early clear trading plan: outcome vs process mindset
  3. Too big/small bets: euphoria/depression over process
  4. Focus on performance instead of plan execution: outcome over process
  5. Mood swings depending on performance: outcome vs process

Being right is not being profitable (outcome). Being right is following the plan (process)

Conclusion:

This is an “avant-gout” of the book to come. On the short side, the market does not cooperate. Open and process mindsets are the two keys to survival

How do I get better at trading?

#Quora: How do normal day traders manage to profit 200-300% annually and hedge funds are able to return only 20-40%?

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How do normal day traders manage to profit 200-300% annually and hedge funds are able to return o… by Laurent Bernut

Answer by Laurent Bernut:

Day traders have one person to answer to: themselves, HF dudes don’t. Sticky Vs Fast money. It is not that they can generate good returns, it is that their clients will not allow them enough wiggle room. So, they are stuck in the month to month tyranny of positive returns. Size-Matters

HF is a fixed cost business

Keeping the lights on at any HF will cost You between half to one million USD per year. There are cheaper arrangements, but those funds are not institutional grade: pension guys will not even look at them. That is all before the principals can extract a dime out of it.

Now, contrary to popular beliefs, HF is a fixed cost business. You have bills to pay and for that You need to attract investors and raise your AUM. Performance does not pay the bills. Performance attract investors who pay the bills. Guys who waltz in with 10M thinking their performance will take care of the costs, stay at 10 forever

HFs are expensive underachievers.

In the mind of an investor, why would You invest with a HF when You can get cheaper better elsewhere? Yeah, yeah, yeah, the low correlation to the markets, downside protection, asset class diversification, i hear You, but who cares: 8 years of bull markets tend to dull people’s perception of risk and frankly. Every time the market had a hiccup, those wise dudes tumbled hard anyways. That is a fact, but that also has to do with the nature of the people they cater to.

Sticky Vs Fast money

HFs are stuck in a rut where they struggle to attract sticky, pension type money. Calpers pulled out of the HF game and many other endowments, pensions follow suite.

It takes roughly half to one million USD to keep the lights on. So, they market to fast money schmocks who put pressure on them. If You don’t perform 2–3 months in a row, or if You lose me 5%, i will cut You off. HF wise dudes don’t like those nervous investors, but they have no choice. Those Shylocks are the guys they have to perform cosmetic surgery with, as in lock their mouths to those guys’ money-makers, in order to one day reach institutional size.

What happens when You are not allowed to lose money? You don’t take risk. When you are up on the month, You take money off the table. When You are down, You cut risk. You never allow positions to fully develop.

At the heart of it are three things:

  1. Mismatch between assets and liabilities: HFs fund their LT strategies with short term funding. This cannot work. This is really the heart of the problem, from which everything flows
  2. Poor portfolio management skills: I started my career building portfolio management systems. Think of this as flying on instruments.If You don’t have good instruments to land at night on foggy days, game over. When i take a look at my HF buddies portfolio management systems, of course they struggle. I have seen only 2 which were investment grade in 15 years. Bottom line, these guys fly blind, no wonder they crash
  3. Short selling incompetence: selling futures as a hedge is for tourists. Most guys who come from the Long institutions think they are good at short selling. 2 years in, they give up on short selling. You don’t learn MMA by signing up for the UFC octagon, You train at the gym first

Institutional HFs

SAC/Point 72, Millennium, Balyasny are successful have a different philosophy. Investments are leveraged and managers have a tight stop loss. As a result, they generate 15–18% p.a. for their clients. Now as a manager, at -5% your AUM is cut in half. At -7%, stop loss. There is a direct disincentive for managers to take extra risk. Very few managers have the patience and discipline to clock in boring returns month in month out.

Do individual investors roll up into HF managers

Some do in the CTA world. This is how Paul Tudor Jones started. The game has changed though. It used to be easy. Ken Griffith started trading bonds in his dorm. Now, according to him, he would never be able to pull this off.

My thoughts on this. I used to want to start up a HF. We were quite advanced and then the cost of the whole thing hit me. I would be bankrupt before we would have enough assets under management (AUM) to be deemed investment grade. So, i rewired my thinking. I promised myself i would do whatever it takes never to need investors. Welcome to MT4 Forex Autotrade: trading 24/5 leveraged 100:1. It has been a long arduous road, but now we do not need investors. Autotrading puts wine (my kind of food) on the table

How do normal day traders manage to profit 200-300% annually and hedge funds are able to return only 20-40%?

#Quora: How should I manage a client’s portfolio if he wants a 8-10% return and no negative years worse than -5%, and has a starting amount of $2…

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How should I manage a client’s portfolio if he wants a 8-10% return and no negative years worse t… by Laurent Bernut

Answer by Laurent Bernut:

Experience has taught me that people like this are a plague. They are not risk adverse. They are conservative to the point of being risk seeking: by refusing to accept moderate waves of volatility, they invite left tails tsunami. If You cannot afford to turn his money away however, here are the formulas

A. Psychology of conservative people

If You can’t stand losing, then You shouldn’t play. When they say they are willing to accept modest returns as long as You don’t lose much, what they mean is they do not want to lose at all.

Conservative is not synonymous with risk adverse. They are opposite in fact. Risk adverse means You have articulated and pieter_bruegel_the_elder_-_the_parable_of_the_blind_leading_the_blind_detail_-_wga3512quantified your risk appetite. Conservative means You are afraid of taking any risk. You are ready to discount your ambitions to the point they will be met with certainty. Kodak, Nokia were risk adverse…

It also means they are afraid and think everything is risky. It is your responsibility to educate them on risk. Do not step into dissertation mode about China, the Fed, Venusians landing in Central Park and Yoyogi park in Tokyo (i will sacrifice myself and volunteer if those sexy Venusians want to perform tests on my body). Risk is not a story, risk is a number.

Secondly, if You deliver, they are likely to demand more over time: 8–10% turns to 10–12% etc. Two reasons for this: you will be put in competition with other managers who promise they can deliver better with the same risk. Since your returns are underwhelming, You will be in constant competition. Secondly, and this is more insidious, they become overconfident. Since they believed everything was risky but now are making money, and they still don’t understand risk, they turn euphoric, literally drunk on testosterone and dopamine. They are laughing their way to the bank until the day you start losing again.

B. Market’s money

The strategy is to start small with minimum risk and increase gradually as you generate performance. Then, before year end, you reduce risk so as to start the new year afresh.

Many people do the gradual increase well, but forget about the decrease. Investors think in calendar years.

Example: first quarter, you generated 2%. You can now increase risk by x% of your gains (10–33%). So instead of risking 0.10% per trade, you would risk, 0.12% and so on and so forth.

Comes November, You are currently risking 0.20% per trade. Now, it is time to de-risk down gradually down to 0.10% so as to start January with a low risk, low concentration portfolio, ready for the new year. Remember: in the investors mind, January is the beginning of a new year, not the continuation of last year’s market.

C. Risk:“how much is enough?”, Steven Seagal, obese mythomaniac

You will often read that you should not risk more than 1–2% of your capital per trade. This does not mean position size, but equity at equity at risk. In your case, if you apply that rule over 5 stocks, one bad month and game over for good. So, get a better bad idea …

What is the maximum risk You can afford on each trade? This is one of the thorniest questions in financeI have pondered that question for years, until one day i came up with a simple elegant solution. Input variables

  1. Drawdown tolerance: If Your investor redeems, game over. he said he would tolerate -5% max drawdown. So, in order to be safe, you should probably calibrate your risk to a fraction of this. If You calibrate at 100%, he will redeem and this is one time where being right is bad, very bad. Besides, you need to rebound from drawdown, so let’s say somewhere between 50%-66.67%, say 2/3
  2. Avg number of positions: over 1 turnover cycle, what is your average number of positions? let’s say: 50
  3. Loss rate: over 1 turnover cycle, what is your average loss rate. In case You don’t know use 60% as loss rate (Yes, it means you lose more often than win, and it is called prudence)

Equipped with this:

Max risk per trade = Drawdown tolerance / (Loss rate * Avg #positions)

= 5% * 2/3 / [50* 60%]

Max risk per trade = 0.11%

Now, that was the max risk per trade. Let’s move to the min risk per trade. This is a fraction of that: usually 40%. So, your min risk per trade would be around 0.05%

Add trading has a cost: 0.036% blended avg, between DMA and high touch at Credit Suisse for example (as a good friend complained again this morning while we were naked in the gym shower !?!).

Now, You probably start to understand why i mean that those customers are toxic. When You go through a drawdown and you will have rough periods, You will simply not be able to dig yourself out.

Conclusion

Once in early 2013, i was cruising at a hedge fund party nursing some nasty Chardonnay and some dude who just launched was explaining his strategy:

-“fundamentals pairs trading”, he proudly said

-“So, You must be Long Toyota and Short Mazda, right? Mazda has gone up 400% and Toyota 30%. That must be a painful trade? ”, i asked

-”Nah, positions are small anyways, so no it does not hurt”, he confidently replied

-”Well, if they are too small to hurt, do You think they are big enough to contribute?”, i candidly asked

And he did the unthinkable rudest thing someone can do in Japan. He gave me back my business card and walked away

How should I manage a client’s portfolio if he wants a 8-10% return and no negative years worse than -5%, and has a starting amount of $2…

#Quora: Do most quantitative trading strategies have limited capacity?

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Wasting water leaks into overfilled glass photo against white

Answer by Laurent Bernut:

The best answer to that question comes from my ex-boss, mentor and more importantly dude friend: “You are at capacity when inertia sets in”

This means that when managers become reluctant to take a trade, this is when they reach capacity. It might be at 100M or at 2B. It is after all subjective. The same can be said about algorithmic strategies.

Algorithmic strategies are more scalable than humans. They can be deployed across larger universes and shorter periodicities. So, diminishing returns kick in later. Market impacts happens and returns come down eventually.

There are three reasons:

  1. Volume market impact: some strategies arbitrage inefficiencies. So, trading naturally correct them. They have built in capacity constraint
  2. Competition: market participants copy each other. Pie does not grow, it gets fragmented
  3. Conceptual shortcomings: that is the hardest problem to solve. Problems are often solved at a different level than they were created. There are four ways it can be solved
    1. go wider: expand your coverage universe
    2. go bigger: accept market impact as a necessary cost of doing business. This means expand limit orders, but it also means refine signals so as mitigate slippage
    3. go deeper: elicit trading: bait other market participants to take the other side so as to create volume. This is the new old thing. Remember “Reminiscence of a stock operator” when the veteran trader tests the market by observing how fast his orders were filled. HFT have perfected that craft.
    4. go different: money management is the new new old thing. Getting in is a choice, getting out is a necessity. Trades do not have to be all-in and all-out. Scaling in and out mitigate capacity issues

Do most quantitative trading strategies have limited capacity?

#Quora: How does one address the issue of regime shift in algorithmic trading?

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How does one address the issue of regime shift in algorithmic trading? by Laurent Bernut

Answer by Laurent Bernut:change-of-season

Fascinating topic that has kept me awake for years. It is a thorny issue. As the patent clerk Einstein used to say: answer are not at the same level as questions. Answer is not at the signal (entry/exit) level. These are position sizing and order management issues.

Definition of regime change

Everybody has a savant definition, so i might as well come up with a simple practical metaphor. imagine You drive at 150 km/h on the highway and then all of sudden, this 4 lanes turns into a country road. If You do not react quick, you will go tree-hugging.

There are three market types: bull, bear and more importantly sideways. Each type can be subdivided into quiet or choppy. So, we have six sextants. Regime change is when market moves from one box to another.

It can be either a new volatility regime, or a move from bear to sideways, sideways to bull or vice versa. Markets rarely move from bull to bear. There is some battle between good and evil.

Why it matters?

Many strategies are designed to do well in a particular environment. They make a lot of money that they end up giving back when regime changes. Examples:

  1. Short Gamma OTM: sell OTM options and collect premium. Pick pennies in front of a steam roller. 2008–2009, August 2015, CHF depeg, Brexit etc. 1 day those options will go in the money and game over
  2. Dual momentum: trade the shorter time frame but determine regime on longer time frame. When market hits a sideways period, market participants get clobbered on false breakouts like poor cute little baby seals
  3. Mean reversion: works wonders in sideways markets until it does not. An extreme version of that is Short Gamma
  4. Value to growth and vice versa: that is probably the most lagging one. By the time, my value colleagues had thrown the towel and loaded up on growthy stocks, market usually gave signs of fatigue…
  5. Fundamentals pairs trading: relationships are stable until market rotation. For instance, speculative stocks rally hard in the early stage of a bull market, while quality trails. Example: Oct-Dec 2012, Mazda went up +400% while Toyota rallied +30%

The vast majority of market participants are trend followers, whether it is news flow, earning momentum, technical analysis. Trending bulls they do great, trending bears, they can survive. Sideways is where they lose their shirts on false positives.

The issue often boils down to how to enter a sideways regime without losing your shirt

The value of backtests

I agree that backtests will help you identify when your strategy does not work. This is actually the real value of backtest. This is when you modify the strategy and adapt the position sizing to weather unsavoury regimes. Then trade this version, not the ideal fair weather strategy.

Reason is simple: everybody’s got a plan until they get punched in the teeth. So don’t lower your guard, ever.

Asset allocation across multi-strats

Some market participants like to develop specific strategies for specific markets: sideways volatile, awesome for pairs trading, great for options strangle/straddle short. Breakouts are good for trending markets etc.

How do You switch from one to the next ? Fixed asset allocation is as clunky, as primitive and as MPT as it gets. There is something far more elegant and simple:

  1. Calculate trading edge for each sub-strategy: long Trend Following, pairs etc
  2. pro-rate trading edge for each strategy
  3. Allocate by pro-rata
  4. Allocate a residual minimum even to the negative trading edge strategies

This is a simple way to put money where it works best

Regime change for single strategy

When not to trade

Van Tharp believes that there is no strategy that can do well or at least to weather all market conditions. I believe there is more nuance. Is the objective to make money across all market regimes? For example, sideways quiet are preludes to explosive movements. In order to make any money, you need to trade big and if you are on the wrong side when things kick off, game over

We trade a single unified (mean reversion within trend following) strategy. What follows is our journey through solving the regime change issue. We found that the three best ways to manage regime changes are

  1. Stop Loss:
  2. position sizing:
  3. order management:

Stop Loss

Regime changes are usually accompanied with rise in volatility. Volatility is not risk, volatility is uncertainty #de-friendSharpeRatio

We used to have a complex stop loss rule. Now we have a uniform elegant stop loss. It just lags current position. It is fashionably late, hence its name: French Stop Loss.

The idea there is to give enough breathing room to the markets to absorb changes without giving back too much profit.

Our solution is to consider stop loss as a fail safe and allow the market to switch direction bull to bear and vice versa within the confine of a stop loss.

The result is we end up switching from one regime to the next more fluidly. We have reduced the number of stop losses by 2/3, which in turn has materially increased our expectancy.

Stop losses are costly. They are the maximum you can lose out of any position.So, unless you have a kick ass win rate or extremely long right tail, you want to reduce their frequency and allow the market to transition.

Position sizing

The first thing You need to understand is that You cannot predict/anticipate a regime forecast. You will find out after the facts. This has some immediate consequences on position sizing: always cap your portfolio risk

Many position sizing algorithms use equity in layers or staircase. We base our calculation of peak equity. So, drawdowns however small have an immediate impact on position sizing. This slams the break much faster than any other method i know.

The other side of the equation is risk per trade wich oscillates between min and max risk. When regime becomes favourable again, it accelerates rapidly.

The whole premise is early response to change in regime through position sizing

Another feature is trend maturity. Betting the same -1% at the beginning and at the end of a trend is asking for a serious kick in the money maker. Trends are born, grow, mature, get old and eventually die one day, just like believers in Efficient Market Theory. So, risk less as you pyramid.

Order management and hibernation

Along with the position sizing comes a vastly underrated feature in most order management: trade rejection.

Secondly, we use a position size threshold. When markets get too volatile and we experience some drawdown, stop losses dilate. As a result, position sizes get smaller. When sizes are too small, trades get rejected.

Order management is vastly under-utilised in most systems i have seen. It is often binary, all-in/all-out, or one way scale in or scale out.

In our system, taking profit off the table is a not a function of chart, technical analysis but driven by risk management. Until price goes a certain distance, exits are not triggered. If exits are not triggered, entries cannot be triggered either. Since volatility goes up and position sizes get smaller, the system drops into hibernation.

We have factual evidence that hibernation during an unfavourable regime is a powerful mechanism to weather regime change. It involves a lot more sophistication than muting indicators, slope flattening etc. It encompasses position sizing, order management, stop loss and to a lesser degree signals.

Conclusion

Multi-strat asset allocation based on pro-rated trading edge is the easy way to go.

Single strat across multiple market regimes means acceleration/deceleration but also hibernation. This is not an easy problem. There is one thought that kept me going through the frustration of figuring this out: “Building a system is like watch making. Time will always be off until the final cog fits in”

How does one address the issue of regime shift in algorithmic trading?

#Quora: How can Renaissance Technologies make so much money from financial markets by hiring scientists/mathematicians with no domain knowledge o…

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How can Renaissance Technologies make so much money from financial markets by hiring scientists/m… by Laurent Bernut

Answer by Laurent Bernut:

I have never worked at Renaissance, so please take my answer with a grain of salt, but here is a first hand story that could shed some light.

Red OctoberOn June 22nd in NYC, my colleague, who is also ex-US Department of Defense consultant and myself, met with one of the foremost US experts on sonar detection (good luck finding him on Facebook, LinkedIn). He is a physicist with multiple PHDs, geeky funny. His expertise is signal processing. He is the real “Hunt for Red October”.

It was one of the most refreshing experiences ever. He explained his world. I explained mine. Cotes de Provence Rose, beer and wild berry Zinfandel helping, we tumbled down the rabbit hole talking even about epistemology, the philosophy behind math.

His world, signal processing, bears uncanny resemblances with ours. We explored Bayesian probabilistic determinism, which models (Gauss, Poisson etc) to apply to distributions, the cost of false positives (think trading edge), arbitrage between time and action with sparse data (confirmation). We spoke the same language. We were talking real problems: how do distinguish signal from the noise ? How fast ? What is the cost of being wrong ? What is the cost of being right ? Which statistical law applies to randomness ?

We entered a massive time distortion. We started around 2 pm and a couple of bottles down the road, but then after what seemed like 5 minutes, we were hungry. It was 10 pm. We could have gone on forever (*)

Compare this with glorified journalists, otherwise referred to as fundamental analysts.

  • “This is fairly valued”… life is unfair darling, so do you really think markets are fair ?
  • “On a sum of the parts valuation”… Frank N. Stein zombie valuation
  • “Fundamentals are strong”… Make fundamentals great again…
  • “Long term story is still intact”… Some HF reality TV celeb says that about Valeant by the way…
  • “On a DCF basis, our target price is +10% above current market valuation” … stop tinkering the terminal value to rationalise your subjective views
  • “i think there is 80% chance that” … bad arithmetic meets emotional roller coaster
  • “top quality management” … was also said about Enron, Bear Sterns, Kodak, GM, Chrysler, Valeant

Too much B/S bingo, too much theory,

Bottom line: “In theory, theory and practice are the same. In practice, they are not”. Yogi Berra, Yankee philosopher

Physicists approach the markets as a statistical problem. This is practical.

MBAs have too much untested theories in their head. It is costly and time consuming to unlearn all that junk.

 

(*) There is no way i could ever afford someone of that caliber; he charges something the size of Liberia’s national deficit per hour. But, he wants to send his granddaughter to Mars and he thinks our algo could be the right fuel, so we invited him to have fun with us. Maybe good guys do not always finish last…

How can Renaissance Technologies make so much money from financial markets by hiring scientists/mathematicians with no domain knowledge o…

#Quora: What’s the biggest mistake that stock market investors make?

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My answer to What’s the biggest mistake that stock market investors make?

Answer by Laurent Bernut:

Short answer: Absence of exit plan. The only soldiers who go to combat w/o an exit plan are Kamikaze. They don’t need one because they expect to die. Unfortunately so do a lot of market participants.082115-Tradingsecrets-01

The apparent simplicity of the answer hides sophisticated concepts that can be broken down in two part: statistical and psychological trading edge

I Statistical trading edge

A Stock picking is overrated

Most market participants believe that stock picking is the alpha and omega of alpha generation. Stock picking is merely everything that precedes entry. It excludes bet size and exit. Unfortunately, stock picking has little influence on the statistical trading edge

Trading edge = Gain Expectancy = Win% *AVG Win% – Loss% *AVg Loss%

1 Stop Loss is the most important variable in a trading system

Stop Loss has direct impact on 3 out of 4 components: Win%, Loss% (1-Win%) and Avg Loss%. Furthermore it has impact on trading frequency: the tighter the stop loss the higher the frequency and vice versa for Buy and Hope

Oops, this just proved that exit is the most important factor.

Please read further about the psychology of stop loss, even if you do not agree

2 Don’t call the race before the finish line

As can be seen, entry has some influence on Win% and Loss%, but nearly not as much as exit. The only time the amount of money that has been made/lost is known with irrefutable certainty is after exit, when open risk is closed

The corollary is that poor entry can be salvaged, poor exit can’t. If You do not have a proper exit plan, You will fail to appreciate the exit plan the market has in store for you

3 Money is made in the money management module

Secondly, Stock picking excludes bet sizing. Bet sizing is the most important determinant of performance

Here is an interesting story to prove this point. When I was at Fidelity, I was running my algo across all managers’ portfolios. My unbridled ambition was to help them trade better 0.05% at a time (multiply this 0.05% by 20 and you are in the rarefied atmosphere of outperformers).

It soon dawned upon my thickness that the same stocks were featured in in my esteemed colleagues’ portfolios. Nothing surprising there, everyone has access to the same research and there is healthy cross pollination. What was surprising was that despite low dispersion of holdings, there was high disparity of performance and volatility: despite owning the same stocks, some people were making a killing, while others were getting killed.

Conclusion: the difference that made the difference is not stock picking, it is bet sizing

4 Blow-ups and feed free-loaders

Watch your winners, but watch your losers more closely. Interesting story: i once had the opportunity to analyse multiple portfolios over many years across many managers on a trade by trade basis. The most important findings were:

  1. Winners look big to the extent that winners are kept small: If You exclude the worst three detractors, everyone would have outperformed the benchmark every year: 100% outperformance 100% of the time (before costs)
  2. Free loaders are performance killers: winners and losers are visible, so they get dealt with. The problem is to identify positions that are neither one nor the others, free loaders. They do not contribute enough to compensate for blow-ups and do not lose enough to be visible. They mobilise resources that should be deployed on productive assets. The psychological consequences is called capacity: you are at capacity when inertia sets in, when you do not want to take a trade because you think you are too big

The two issues that cause managers to underperform are directly related to Absence of EXIT policy

II Mental edge: 90% of trading is mental, the other half is good math

Optimism peaks before entry. After that, emotions kick in. The ultimate proof of this is divorce statistics…

1 Pre-mortem

Few market participants give bet sizing the importance it deserves. It is often either conviction (feel good) based or equal weight. Once they are in a position, things change. Or, more accurately, their perception change: they have either too much or too little of a stock.

The concept of pre-mortem is finally finding its way in decision making: Nobel prixe winner Daniel Kahneman, Dan Gilbert (positive psychology Penn U), Hal Hershfield (future self). The idea is to visualise the worst possible outcome and plan accordingly.

If for every trade You are about to take, you visualise a stop loss, something interesting will happen: you will size positions smaller. That is a natural reaction.

In other words, the most important question about fund management is: could i live with earning a little less than i could or could i afford to lose a lot more than i should ?

Again, the only way to grasp this is to mentally think about exit first

2 Kubler Ross: market participants grieve their way to selling

I am a professional short seller. On the short side, you make money by selling along people who liquidate their long position. You lose money by selling short crowded shorts…

There are many psychological charts about euphoria to despondency. They are all nice, adorable and lovely, but they are written from people whose perspective is to go Long. They do not understand the psychology of selling. There is one model that grasp the emotions we go through; the psychology of grief popularised by Elizabeth Kubler-Ross

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

Market participants grieve their way into liquidating losers and are too complacent when leaving winners.

3 The psychology of stop loss

Even if You do not agree with what you have read so far, You must read the post below.

All diets w/o exception have failed: statistics show that we are all getting fat year after year. Diets address the wrong problem. They talk about what we eat, not about how we relate to what we eat. Diets are a psychological issue, not a physiological one.

Stop Losses are exactly the same. They are an identity issue, not a statistical one. The ego associates being profitable with being right. So, losing money is an attack on the ego. We rationalise, change our beliefs (Festinger and ognitive dissonace, Gazzanikas and split brain theory) rather than kick out losers.

Read this post to learn how to reframe your beliefs and execute stop losses like you brush your teeth.

The psychology of stop loss: how You can be 100% right despite 60% failed trades by Laurent Bernut on Alpha Secure

Conclusion:

Making money in the market is about having an edge. There are two types of edge: statistical and mental. The only ways to tilt your trading edge is to start with the end in mind: think about how you are going to exit before you enter

What’s the biggest mistake that stock market investors make?