The Electric Day Trader and Ruin. How probability oppposes short-term investors.

Quote from ProfLogic:

BLSH, I do have a lot of respect for some of your posts but quoting a PhD/CPA that is clueless on price movement or its natural construction is IMHO . . . worthless.

These guys are literally clueless on why or how price moves. This is why they are simply restating what true traders already know.

Chaos can't be viewed in a chaotic environment with the expected outcome of an increase in profit probability.
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I'm not saying I agree with these researchers or their thesis. I'm not saying I disagree with their general conclusions, either, as well.


I wanted to put this out there for some who may not have seen a single statistical study on probabilities, yet.

That's all.


Also, the chart doesn't format correctly when copied and pasted, so if you're interested in seeing the statistical chart, click on the article so that you view the format in its proper state.
 
The author completely misses the fact that prices do not follow normal distribution. This fact alone completely breaks his thesis. If you look carefully you can find many other holes in that article. Although I must admit that the article serves one good purpose and that is warning the public that making money in the markets is not for everyone.

Another proof that it's impossible to make money in the markets, BLSH? Keep proving it to yourself and maybe one day you will even convince yourself. :)
 
Quote from ByLoSellHi:

I'm not saying I agree with these researchers or their thesis. I'm not saying I disagree with their general conclusions, either, as well.
I wanted to put this out there for some who may not have seen a single statistical study on probabilities, yet.
That's all.
Also, the chart doesn't format correctly when copied and pasted, so if you're interested in seeing the statistical chart, click on the article so that you view the format in its proper state.
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Thanks . . . I did.

Statistical information is best applied to erratic environments like are discribed in the article because they are a "fudged" math to begin with.
Logical information is best applied to fixed environments like how price action actually moves when viewed in non-variable charting environments.
 
"Day trading is particularly risky. While the study found that three (3) accounts in twenty-six (26) could successfully conduct short-term trading, there was only one successful day trading account.

A Sharpe Ratio analysis of the only account considered successful in both short-term and day trading showed the trading returns were not commensurate with the risks to which the account was exposed. The most successful account in the study, A8, had limited short-term trades and no day trading."

Footnote 1
This study will utilize Risk of Ruin tables developed by Nauzer J. Balsara author of " Money Management Strategies for Futures
Traders." Mr. Balsara was featured in a December 1992 article for Technical Analysis of Stocks & Commodities, from which the Tables were taken.


http://www.nasaa.org/content/Files/Day_Trading_Analysis.pdf

Professional Report
Ronald L. Johnson
Investment Consultant

1424 Seagull Dr. Ste.107 Phone 727-771-7020
Palm Harbor, FL 34685 Fax 727-771-0980

Day Trading

An Analysis of Public Day Trading at a Retail Day Trading Firm

The Purpose of The Analyses

Numerous market studies have concluded that accurate market timing is not possible, even for professional money managers.

Day trading is the ultimate test of market timing in that the trade is opened and closed within the same day.

The emergence of the Internet and the availability of almost instantaneous real-time market data have
increasing numbers of public investors interested in trading on a short-term or intraday basis.

Retail brokerage firms concentrating on this speculative activity frequently claim that a high percentage of their retail public
clients are profitable.

The purpose of this analysis was to analyze a statistically significant sample of public day trading experiences in order to determine whether public retail customers really have been successful day traders, and to identify
and quantify the risks that public investors face as day or short-term traders.

How The Analysis Was Conducted

Step 1. The Project Group on Day Trading randomly chose thirty (30) short-term trading accounts for analysis
from a retail day trading firm:

Thirty accounts were analyzed in order to provide a representative sample of public short-term trading activity. The accounts were chosen without knowing either the distribution of short-term trades within the
account or the profitability of the trading conducted.

Step 2. A matched trading analysis, commission-to-equity analysis, and turnover analysis was conducted for
each account by STZ Analytical Services.
A matched trading analysis matches opening trades with closing trades and was required to identify the
profitability and duration of all trades in each account.

A typical matched trading analysis conducted for this report is shown at Exhibit A-1.

Commission-to-equity and turnover analyses were conducted for each account to quantify the degree of
activity and the costs associated with that activity in each account. Typical turnover and
Page 1

Page 2

Professional Report

commission-to-equity analyses conducted for this report are shown at Exhibit A-2.
Step 3. This analysis addresses all of the trading as well as the day trading conducted in each account.

Trading statistics were calculated and evaluated based on the matched trading results of Step 2. The typical set-up
analyses conducted for this report is shown at Exhibit A-3.

The analysis established important selected trading statistics for each account (shown at the top of Exhibit
A-3).

The individual account statistics were calculated on the basis of matched trading record shown below the heading "QTY, DAYS HELD, P/L". (Exhibit A-3 includes only the first 26 trades, sorted by Days Held for
illustration).

Account A7, for example, had four day trades (0), three two day trades (2), two three (3) day trades, etc. The
majority of the accounts traded 1,000 share lots.

....READ STUDY IN PDF FORMAT HERE...

http://www.nasaa.org/content/Files/Day_Trading_Analysis.pdf
 
It seems like this article just reinforces the point that a statistical edge is not enough to guarantee success, and must be used with proper money management.

The author says "if the trader plays the game indefinitely, betting 25% of initial capital on each trade, the probability of ruin is perilously close to the results of a coin flip!" But this is pretty obvious, anyone who risks losing 25% of their account on any single trade is bound to blow their account out. I'm surprised the odds of survival aren't lower under these conditions.

If you look at Table I, the probability of survival is 98.19% when beginning with 20 trade units (in other words, risking only 5% on each trade). Plus, many traders risk even less, in the 0.5 to 2% range, which increases their odds even more.
 
Anybody who bases a study on day trading on the assumption that traders risk between 10 and 25% of their capital on each trade deserves to remain in academia for the rest of their lives.
 
Quote from ByLoSellHi:


I wanted to put this out there for some who may not have seen a single statistical study on probabilities, yet.
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So your target was a newb. Well, that figures. Would have expected that.

Having a 55% edge doesn't mean anything if the percentage won and percentage loss aren't significantly different. It also fails to consider the diversification of futures and ETFs. Buying one stock that way, sure, would increase your risk of ruin, but it appears from the way they use it that the risk of ruin is any outcome that leaves the trader with less money than when they started.

By their metric, this means I have a 40% edge, and that's plenty of edge especially when the percentage losses are significantly less than percentage winners.

Also, each probability should be based on the binomial theorem, not this calculation. You either win or lose, and you use the theorem to calculate your probability of winning a certain number of trades out of a certain number of trades. The risk of ruin calculation as it is used here is not the correct calculation to express the unseen risk. It will depend on the trader's edge, which has to be around 60%, not 55%, and the money management techniques have to be decent.
 
Are you sharing whatever edge you claim to have with noobs?

Quote from bwolinsky:

So your target was a newb. Well, that figures. Would have expected that.

Having a 55% edge doesn't mean anything if the percentage won and percentage loss aren't significantly different. It also fails to consider the diversification of futures and ETFs. Buying one stock that way, sure, would increase your risk of ruin, but it appears from the way they use it that the risk of ruin is any outcome that leaves the trader with less money than when they started.

By their metric, this means I have a 40% edge, and that's plenty of edge especially when the percentage losses are significantly less than percentage winners.

Also, each probability should be based on the binomial theorem, not this calculation. You either win or lose, and you use the theorem to calculate your probability of winning a certain number of trades out of a certain number of trades. The risk of ruin calculation as it is used here is not the correct calculation to express the unseen risk. It will depend on the trader's edge, with has to be around 60%, not 55%, and the money management techniques have to be decent.
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