Why 7% is the Difference between Failure and Success in Trading - Psychology and Money Management | futures io social day trading
futures io futures trading


Why 7% is the Difference between Failure and Success in Trading
Updated: Views / Replies:19,222 / 107
Created: by Anagami Attachments:7

Welcome to futures io.

(If you already have an account, login at the top of the page)

futures io is the largest futures trading community on the planet, with over 90,000 members. At futures io, our goal has always been and always will be to create a friendly, positive, forward-thinking community where members can openly share and discuss everything the world of trading has to offer. The community is one of the friendliest you will find on any subject, with members going out of their way to help others. Some of the primary differences between futures io and other trading sites revolve around the standards of our community. Those standards include a code of conduct for our members, as well as extremely high standards that govern which partners we do business with, and which products or services we recommend to our members.

At futures io, our focus is on quality education. No hype, gimmicks, or secret sauce. The truth is: trading is hard. To succeed, you need to surround yourself with the right support system, educational content, and trading mentors Ė all of which you can find on futures io, utilizing our social trading environment.

With futures io, you can find honest trading reviews on brokers, trading rooms, indicator packages, trading strategies, and much more. Our trading review process is highly moderated to ensure that only genuine users are allowed, so you donít need to worry about fake reviews.

We are fundamentally different than most other trading sites:
  • We are here to help. Just let us know what you need.
  • We work extremely hard to keep things positive in our community.
  • We do not tolerate rude behavior, trolling, or vendors advertising in posts.
  • We firmly believe in and encourage sharing. The holy grail is within you, we can help you find it.
  • We expect our members to participate and become a part of the community. Help yourself by helping others.

You'll need to register in order to view the content of the threads and start contributing to our community.  It's free and simple.

-- Big Mike, Site Administrator

Reply
 7  
 
Thread Tools Search this Thread
 

Why 7% is the Difference between Failure and Success in Trading

  #81 (permalink)
Fiddler
Nashville, TN
 
Futures Experience: Intermediate
Platform: NinjaTrader
Broker/Data: IB
Favorite Futures: NQ ES
 
Posts: 468 since Feb 2011
Thanks: 323 given, 542 received

I have always like this thread, and the recent discussion got me thinking about it again. Looking for some reasonable, if not statistically sound way to apply the metrics to generate results for non-Bernoulli distributions by combining Monte-Carlo with the Risk Adjusted Optimal F template.

Would it not make sense to find out the statistics of the low performance on a Monte-Carlo and then run those numbers in the Risk adjusted optimal F?

While writing this post I have spent too much time implementing my idea to see if it was worthwhile...lol.

I used the results of the bot that I have been running this year as my starting point.

Take the win loss ratio from the Low PnL on the Monte-Carlo simulator, then apply that to the Risk Adjusted Optimal F template. With my example, the accuracy went from 63% to 48.5%. Granted you have only taken care of one variable, accuracy, with the win/loss ratio still potentially in flux.

W/L changes are much more subjective. I adjusted average win down by the same percentage as average loss, basically reducing my W/L ratio at current accuracy until the profit reached the Low PnL on the Monte-Carlo simulator. Again with my example, W/L ratio went from 1.65 to 0.95.

I then have 2 points that I can use for sensitivity analysis. One if I have adjustments in my system accuracy, the other for W/L ratio changes.

I made 2 copies of the Optimal F sheet. Put data tables below where I could feed in "Low Accuracy" and current W/L ratio to the model for one sheet and current accuracy and "Low W/L ratio" on the other. I can then evaluate impacts to accuracy given the "Low W/L" and impacts to the W/L assuming "Low Accuracy". Basically, conducting a sensitivity analysis on each variable given the "worst" situation for the other variable.

To me this seemed a decent way to check system risk. In the end, my position sizing on the bot match my analysis results. @Fat Tails is welcome to tell me how statistically unsound this is, so that I know how much statistical risk I have remaining.

Reply With Quote
The following 2 users say Thank You to Luger for this post:
 
  #82 (permalink)
Elite Member
Georgia, US
 
Futures Experience: None
Platform: Various
Favorite Futures: Various
 
josh's Avatar
 
Posts: 4,897 since Jan 2011
Thanks: 5,143 given, 11,242 received

First of all I want to thank @Anagami for creating this thread and for @Fat Tails for adding so much context and understanding to it.

In trying to take the content from the world of theory down to "how do I apply this?" I thought about some considerations I have, and would like to discuss them here.

Do all of the simulations here all assume a mechanical approach, where the stop and target are always fixed? My assumption is yes but I may be incorrect.

My issue with the mathematical content in this thread, besides the fact that my limited brain power prohibits me from fully grasping it after only one brief read, is that it assumes that we are trading a "system" and that we have a definite signal, and that we take every one, and that we are trading a system that implies the market is essentially the same at all times and days (at least that's what I think it assumes).

But as a day trader trading ES, with the trades I normally take, a 1:1 would absolutely kill me. I would have to modify my approach drastically to obtain the optimal F that FT is touting. My trades are based around the structure of the typical ES day, and taking the trade off simply at 1R when I know that it will yield more profit seems a bit unusual to me. In other words, I would be trading a system, and not the market.

I apologize for taking this from a great mathematical discussion to the gray areas of practicality and for being less than eloquent with my words. But at the end of the day, we are still humans (though I think FT may be an android in disguise), and human behavior is not always a case of "optimal F".... if humans were driven by mathematics, there would be no such things as credit card debt and we would not have the sovereign debt problems we see on such a grand scale. What fool would spend more than he takes in? It's possibly the most simple math on the planet, yet, humans fail to obey even that. So, I think there is more to making money with a discretionary approach than accepting a 1:1 as optimal.

Reply With Quote
The following 4 users say Thank You to josh for this post:
 
  #83 (permalink)
Elite Member
Bala, PA, USA
 
Futures Experience: Intermediate
Platform: NinjaTrader
Broker/Data: Mirus, IB
Favorite Futures: SPY, Oil, Euro
 
monpere's Avatar
 
Posts: 1,858 since Jul 2010
Thanks: 300 given, 3,276 received



josh View Post
First of all I want to thank @Anagami for creating this thread and for @Fat Tails for adding so much context and understanding to it.

In trying to take the content from the world of theory down to "how do I apply this?" I thought about some considerations I have, and would like to discuss them here.

Do all of the simulations here all assume a mechanical approach, where the stop and target are always fixed? My assumption is yes but I may be incorrect.

My issue with the mathematical content in this thread, besides the fact that my limited brain power prohibits me from fully grasping it after only one brief read, is that it assumes that we are trading a "system" and that we have a definite signal, and that we take every one, and that we are trading a system that implies the market is essentially the same at all times and days (at least that's what I think it assumes).

But as a day trader trading ES, with the trades I normally take, a 1:1 would absolutely kill me. I would have to modify my approach drastically to obtain the optimal F that FT is touting. My trades are based around the structure of the typical ES day, and taking the trade off simply at 1R when I know that it will yield more profit seems a bit unusual to me. In other words, I would be trading a system, and not the market.

I apologize for taking this from a great mathematical discussion to the gray areas of practicality and for being less than eloquent with my words. But at the end of the day, we are still humans (though I think FT may be an android in disguise), and human behavior is not always a case of "optimal F".... if humans were driven by mathematics, there would be no such things as credit card debt and we would not have the sovereign debt problems we see on such a grand scale. What fool would spend more than he takes in? It's possibly the most simple math on the planet, yet, humans fail to obey even that. So, I think there is more to making money with a discretionary approach than accepting a 1:1 as optimal.

I trade a very mechanical system with a fixed 2:1 risk/reward. I generally take an average between 10 and 15 trades per day. At the end of every day I review all my signals and determine how the method would have done if I traded with a various risk/reward ratios. The 1:1 ratio generally always has more winners, but is also almost always less profitable. I have been doing this exercise for years just out of habit. I don't know how that fits in with the theoretical findings here, but this has been my experience with a real world mechanical method.

Reply With Quote
The following 5 users say Thank You to monpere for this post:
 
  #84 (permalink)
Fiddler
Nashville, TN
 
Futures Experience: Intermediate
Platform: NinjaTrader
Broker/Data: IB
Favorite Futures: NQ ES
 
Posts: 468 since Feb 2011
Thanks: 323 given, 542 received


josh View Post
Do all of the simulations here all assume a mechanical approach, where the stop and target are always fixed? My assumption is yes but I may be incorrect.

For the Risk Adjusted Optimal F spreadsheet, your assumption is correct. Only 2 outcomes to a trade, win "x" or lose "y". Giving you ratio x:y. Basically, you enter a trade and put on an ATM with a fixed target and fixed stop loss and walk way.

I don't think that anywhere it was said a pure 1:1 is best. The outcome that Fat Tails noted was that higher accuracy and lower W/L ratio allowed more leverage than low accuracy and high W/L ratio.

65% accurate and 1.5 : 1
50% accurate and 2.6 : 1

Those scenarios are almost equivalent in this fixed stop and fixed target world, with a slight edge going to the one with higher accuracy.

For a discretionary guy, I think the only real take-way is that a consistent trader should work on increasing accuracy before increasing W/L ratio to allow for an increase in leverage or a smoother equity curve. Really it is a balancing act between the two, trying to make sure you don't give up too much of one for the other.

Reply With Quote
The following 4 users say Thank You to Luger for this post:
 
  #85 (permalink)
Fiddler
Nashville, TN
 
Futures Experience: Intermediate
Platform: NinjaTrader
Broker/Data: IB
Favorite Futures: NQ ES
 
Posts: 468 since Feb 2011
Thanks: 323 given, 542 received

@monpere Since you trading is directly applicable to this thread. When looking at the profitability of 1:1 or 2:1, have you considered the impact of being able to increase leverage on the 1:1 due to accuracy?

Reply With Quote
The following user says Thank You to Luger for this post:
 
  #86 (permalink)
Elite Member
Bala, PA, USA
 
Futures Experience: Intermediate
Platform: NinjaTrader
Broker/Data: Mirus, IB
Favorite Futures: SPY, Oil, Euro
 
monpere's Avatar
 
Posts: 1,858 since Jul 2010
Thanks: 300 given, 3,276 received


Luger View Post
@monpere Since you trading is directly applicable to this thread. When looking at the profitability of 1:1 or 2:1, have you considered the impact of being able to increase leverage on the 1:1 due to accuracy?

If I was trading 1 contract on both methods, and I now double the contracts on the 1:1 and also double the contracts on the 2:1, wouldn't the 2:1 still come out ahead? or is there a piece of the puzzle I am missing?

Reply With Quote
The following user says Thank You to monpere for this post:
 
  #87 (permalink)
Elite Member
Berlin, Europe
 
Futures Experience: Advanced
Platform: NinjaTrader, MultiCharts
Broker/Data: Interactive Brokers
Favorite Futures: Keyboard
 
Fat Tails's Avatar
 
Posts: 9,653 since Mar 2010
Thanks: 4,226 given, 25,601 received
Forum Reputation: Legendary


Luger View Post
Would it not make sense to find out the statistics of the low performance on a Monte-Carlo and then run those numbers in the Risk adjusted optimal F?

While writing this post I have spent too much time implementing my idea to see if it was worthwhile...lol.

I used the results of the bot that I have been running this year as my starting point.

Take the win loss ratio from the Low PnL on the Monte-Carlo simulator, then apply that to the Risk Adjusted Optimal F template. With my example, the accuracy went from 63% to 48.5%. Granted you have only taken care of one variable, accuracy, with the win/loss ratio still potentially in flux.

W/L changes are much more subjective. I adjusted average win down by the same percentage as average loss, basically reducing my W/L ratio at current accuracy until the profit reached the Low PnL on the Monte-Carlo simulator. Again with my example, W/L ratio went from 1.65 to 0.95.

I then have 2 points that I can use for sensitivity analysis. One if I have adjustments in my system accuracy, the other for W/L ratio changes.

@Luger: I think that we are talking about two different things here ....

(a) the risk that the trading system is correctly represented by the sample
(b) the risk derived from the variance of the sample


How good is the sample?

The sample trades are those backtested over the in-sample period. The question whether the sample correctly represents the edge of the trading system is important. There is a systemic risk (a1) that the behaviour of the market participants has changed and the edge since evaporated, which cannot be easily estimated with statistical tools. And then there is a risk that

(a2) the sample size is too small and represents too favourable an outcome
(a3) the trading system has been curve fitted to the sample

Your approach deals with (a2), if you analyze the low point of the MonteCarlo simulation. My approach ignores that type of risk. I assume that my sample has a sufficient size and that it correctly represents the edge of the system. So I focus on the risk (b), which comes with the variance of the sample.


Adjusting position size to risk of ruin

Example: I have an account of $ 100,000. I do accept a risk of ruin of 1%. My definition of ruin is that the equity has dropped from $ 100,000 to $ 50,000. How many contracts should I trade to comply with the specified risk?

The risk of ruin is equivalent with a maximum drawdown and the probability that the maximum drawdown is reached or exceeded.

What do I now? I take the backtest of my trading system based on a single contract and do a Monte Carlo Analysis with 1,000 different equity graphs. Then I look at the maximum drawdown that occured during the in-sample period for each of them. I plot a distribution of the 1,000 drawdowns and take the lower 1 percentile value, which is the worst remaining occurence, once I have eliminated the worst 10 drawdowns of the Monte Carlo analysis.

Now let us assume that the worst remaining occurence produced a maximum drawdown of $ 12,500. Starting from my requirement that there be a risk of ruin of 1% of a drawdown to an equity value of $ 50,000, I can now leverage the system by trading 4 contracts.


Luger View Post
I made 2 copies of the Optimal F sheet. Put data tables below where I could feed in "Low Accuracy" and current W/L ratio to the model for one sheet and current accuracy and "Low W/L ratio" on the other. I can then evaluate impacts to accuracy given the "Low W/L" and impacts to the W/L assuming "Low Accuracy". Basically, conducting a sensitivity analysis on each variable given the "worst" situation for the other variable.

To me this seemed a decent way to check system risk. In the end, my position sizing on the bot match my analysis results. @Fat Tails is welcome to tell me how statistically unsound this is, so that I know how much statistical risk I have remaining.

I would not use the Optimal F sheet for other than Bernoulli distributions. I will have to check, whether Ralph Vince has developed a more universal approach that can be used to do that. The simple spreadsheet is probably not the best tool for position sizing in a real world.

Reply With Quote
The following 5 users say Thank You to Fat Tails for this post:
 
  #88 (permalink)
Fortitudo et Honor
Austin, TX
 
Futures Experience: Advanced
Platform: TradeStation
Favorite Futures: Futures
 
Posts: 882 since Mar 2011
Thanks: 128 given, 703 received


Fat Tails View Post
@Luger: I think that we are talking about two different things here ....

(a) the risk that the trading system is correctly represented by the sample
(b) the risk derived from the variance of the sample


How good is the sample?

The sample trades are those backtested over the in-sample period. The question whether the sample correctly represents the edge of the trading system is important. There is a systemic risk (a1) that the behaviour of the market participants has changed and the edge since evaporated, which cannot be easily estimated with statistical tools. And then there is a risk that

(a2) the sample size is too small and represents too favourable an outcome
(a3) the trading system has been curve fitted to the sample

Your approach deals with (a2), if you analyze the low point of the MonteCarlo simulation. My approach ignores that type of risk. I assume that my sample has a sufficient size and that it correctly represents the edge of the system. So I focus on the risk (b), which comes with the variance of the sample.


Adjusting position size to risk of ruin

Example: I have an account of $ 100,000. I do accept a risk of ruin of 1%. My definition of ruin is that the equity has dropped from $ 100,000 to $ 50,000. How many contracts should I trade to comply with the specified risk?

The risk of ruin is equivalent with a maximum drawdown and the probability that the maximum drawdown is reached or exceeded.

What do I now? I take the backtest of my trading system based on a single contract and do a Monte Carlo Analysis with 1,000 different equity graphs. Then I look at the maximum drawdown that occured during the in-sample period for each of them. I plot a distribution of the 1,000 drawdowns and take the lower 1 percentile value, which is the worst remaining occurence, once I have eliminated the worst 10 drawdowns of the Monte Carlo analysis.

Now let us assume that the worst remaining occurence produced a maximum drawdown of $ 12,500. Starting from my requirement that there be a risk of ruin of 1% of a drawdown to an equity value of $ 50,000, I can now leverage the system by trading 4 contracts.



I would not use the Optimal F sheet for other than Bernoulli distributions. I will have to check, whether Ralph Vince has developed a more universal approach that can be used to do that. The simple spreadsheet is probably not the best tool for position sizing in a real world.

It's good to see others doing the same stuff

TS sucks for certain capabilities but for analysis like Monte Carlo, Walk Forward, etc...it's awesome.

TS's walk forward optimizer actually does a pretty good job and has built in rules for pass/fail for strategies. It rejects (fails) those that have a certain number of "runs" that aren't profitable or those that have an excessive drawdown, etc. It even gives you recommendations on the next optimization interval.

Interestingly, I wanted to discuss your comment about in sample.

I find a lot of misconception about in sample size. Contrary to popular belief, you can actually include TOO much data. If for instance, the market has recently shifted significantly (i.e. a margin increase crushes volatility), then going further back with your in sample analysis is simply muddying the water more.

Although it's helpful to try to find an in sample period that includes multiple market structures and conditions, why go back any further than a couple of years on lower time frame strategies? On CL for example, if you had data, you could try to go back 35 years and you'd find there was a decade in there when oil didn't move hardly at all. So obviously optimization or analysis including periods like that is going to affect/sway your results, possibly in the wrong direction.

I find that it's best to find a happy medium of enough data to give you a trade size that's statistically significant (and I don't accept the college textbook n=30 value) and features a couple of recent relevant market structures, but doesn't give me so much history that it favors out of date/obsolete conditions.

"A dumb man never learns. A smart man learns from his own failure and success. But a wise man learns from the failure and success of others."
Reply With Quote
The following 3 users say Thank You to RM99 for this post:
 
  #89 (permalink)
Trading Apprentice
glasgow+scotland
 
Futures Experience: Intermediate
Platform: none
Favorite Futures: stocks
 
Posts: 21 since Jul 2012
Thanks: 2 given, 18 received

Dice

How does your 7% theory relate to dice?x

Reply With Quote
 
  #90 (permalink)
Live Your Bliss
Canary Islands, Spain
 
Futures Experience: Advanced
Platform: OA
Favorite Futures: What Moves
 
Anagami's Avatar
 
Posts: 701 since Dec 2010
Thanks: 474 given, 1,398 received



josh View Post
First of all I want to thank @Anagami for creating this thread and for @Fat Tails for adding so much context and understanding to it.

In trying to take the content from the world of theory down to "how do I apply this?" I thought about some considerations I have, and would like to discuss them here.

Do all of the simulations here all assume a mechanical approach, where the stop and target are always fixed? My assumption is yes but I may be incorrect.

My issue with the mathematical content in this thread, besides the fact that my limited brain power prohibits me from fully grasping it after only one brief read, is that it assumes that we are trading a "system" and that we have a definite signal, and that we take every one, and that we are trading a system that implies the market is essentially the same at all times and days (at least that's what I think it assumes).

But as a day trader trading ES, with the trades I normally take, a 1:1 would absolutely kill me. I would have to modify my approach drastically to obtain the optimal F that FT is touting. My trades are based around the structure of the typical ES day, and taking the trade off simply at 1R when I know that it will yield more profit seems a bit unusual to me. In other words, I would be trading a system, and not the market.

I apologize for taking this from a great mathematical discussion to the gray areas of practicality and for being less than eloquent with my words. But at the end of the day, we are still humans (though I think FT may be an android in disguise), and human behavior is not always a case of "optimal F".... if humans were driven by mathematics, there would be no such things as credit card debt and we would not have the sovereign debt problems we see on such a grand scale. What fool would spend more than he takes in? It's possibly the most simple math on the planet, yet, humans fail to obey even that. So, I think there is more to making money with a discretionary approach than accepting a 1:1 as optimal.

Hi @josh
Thanks for such a thoughtful response.

The main purpose of the thread was not to promote 1:1 (or any other RR for that matter) or a mechanical approach. It was to simply see how trials and the capital curve emerge as one changes RR and the winning %. FT introduced position sizing into the picture, which is also paramount.

The Kelly formula assumes that the outcomes and probabilities are known, so it has certain inherent modelling limitations (particularly if we believe in 'let the profits run', as we cannot predict the outcome). In theory, it is actually impossible to fulfill the formula conditions, because the trade probabilities are never known with certainty.

However, you can still get a ballpark position sizing recommendation by averaging your winners, losers, and probabilities and plugging them in. It's not the same, but gives you some idea.

More on Kelly here.

I should add that most people seem to prefer to position size less than Kelly suggests (say, half Kelly), as the capital curve can bit a bit too rocky for comfort (giving up some gain for smoothness and minimizing the impact of unaccounted-for risks).

"...the degree to which you think you know, assume you know, or in any way need to know what is going to happen next, is equal to the degree to which you will fail as a trader." - Mark Douglas
Reply With Quote
The following 2 users say Thank You to Anagami for this post:

Reply



futures io > > > Why 7% is the Difference between Failure and Success in Trading

Thread Tools Search this Thread
Search this Thread:

Advanced Search



Upcoming Webinars and Events (4:30PM ET unless noted)

Jigsaw Trading: TBA

Elite only

FuturesTrader71: TBA

Elite only

NinjaTrader: TBA

Jan 18

RandBots: TBA

Jan 23

GFF Brokers & CME Group: Futures & Bitcoin

Elite only

Adam Grimes: TBA

Elite only

Ran Aroussi: TBA

Elite only
     

Similar Threads
Thread Thread Starter Forum Replies Last Post
Success and Opportunities wh Psychology and Money Management 9 February 16th, 2016 06:18 PM
Bizman70 The Journey to success of Trading Live bizman70 Elite Trading Journals 88 September 15th, 2014 10:22 AM
Is there a big difference between trading long and trading short ? kittyan Traders Hideout 16 July 30th, 2010 07:19 AM
Key To Success George Psychology and Money Management 1 December 27th, 2009 01:12 AM


All times are GMT -4. The time now is 08:36 AM.

Copyright © 2017 by futures io, s.a., Av Ricardo J. Alfaro, Century Tower, Panama, +507 833-9432, info@futures.io
All information is for educational use only and is not investment advice.
There is a substantial risk of loss in trading commodity futures, stocks, options and foreign exchange products. Past performance is not indicative of future results.
no new posts
Page generated 2017-12-15 in 0.21 seconds with 20 queries on phoenix via your IP 54.234.247.118