Finding Hot Stock Markets to Heat Up Your Trading Portfolio


We do not like day trading stocks, but we are short term traders and we DO like to hit and run in the US stock market. We like to get into locaiongs when they are moving and then get out in two or three days. We think this is a highly effective way to trade and combines safety with very high yields.

But to do this we use a very unconventional style of trading. We set up a very large group of markets, currently 96, limit our commitment to each market to about $1,000 and then take mechanical trading signals from a trading system we have programmed and have traded with real money for many years. We use a custom trading platform that interfaces with live streaming data from E-signal. We sit in front of a computer for six and a half hours per trading day and we typically take 10 to 30 trades a day.


But because we take so many trades and are only in trades for two or three days our methods will not work in dead markets. Our methods require that we clarify volatile markets.

Identifying good volatile markets can be a little tricky. At one time I used a simple form of back testing to do this. I would grab a market, get a associate months of tick data for that market and then apply our trading system and look at the results. If the results looked good I would put the market into my portfolio and if the results looked bad I would discard the market.

The results of this method could be disappointing. A market that had made good money for 8 weeks might produce a string of two or three losing trades just as I was putting real money on it and the market that I had discarded might start making money.

What I soon realized was that this approach was really a form of optimization that was, in effect, trying to predict future trading system performance by trying to fit a system to a given set of data. It was a form of “curve fitting” and curve fitting is the worst thing you can do to clarify profitable trading. This simply was not a good approach.

But what I realized when working with market data was that the basic factors for identifying profitable markets was volatility and follow by.

I then investigated some commercial software that allowed the user to examine large numbers of markets and go into certain criteria to clarify markets that met those certain criteria. I did find this commercial software helpful for identifying volatile markets but the results were nevertheless not as satisfactory as I had hoped for.

The problem was that most commercial software uses range over a period of time to determine volatility. The problem was that sometimes that range took place in a single day and the rest of the time the market was dead.

I will give you an example of a market with a lot of volatility for two days but that was nevertheless a waste of time to trade the rest of the time. On 12/16/09 there was some breaking news on DCGN, deCode Genetics, and the market exploded and put in a range from 6 cents to over 30 cents, quadrupling its value in a single day. That is volatility! One day this market was at the top of the list for market gainers and on the next day it was on top of the list for market decliners, up and then down in two days. As I write this on 1/10/10 DCGN is back to where it started before the news and is as flat as a pancake. But if you run a volatility examine on all stocks for December 2009 DCGN will probably top the list. And in addition it was but a one day surprise and outside that one day it would be pointless to keep it in a trading portfolio.

This kind of market movement is not uncommon and it creates problems for identifying good markets to trade. Software that uses range over a period of time does not filter out this kind of market.

After some experimentation I hit on a solution to this problem which I will proportion here. What I did was to develop a program that could examine a stream of data and clarify the characteristics that typically work well with our unconventional trading methods.

The markets that worked best with our trading methodology were markets that had repeated expanding, volatile break outs with follow by for a day or two. After an expansion of range the market might contract for a few days but this contraction might then be followed by another expansion and then some more follow by.


To clarify such markets I programmed a dummy day trading system. We do not day trade and I am NOT recommending day trading or this system for actual trading. But to clarify good break out markets for our methodology I set up the following simple rules for the dummy day trading system:

1) The “system” uses our proprietary programming method for calculating the number of contracts traded and limits the size of our locaiongs to approximately $1,000 per position taken. In the world of stock trading this might be considered a tiny position. We do this to allow us to trade a large number of markets with a small amount of money. We currently trade 96 markets and by doing so we protect our trading equity by diversification. Hence we will buy 1000 shares of a stock selling at 98 cents per proportion but only 100 shares of a stock selling at $10.02 per proportion.

2) After the close on a given day the DUMMY SYSTEM determines the range for that day. It then calculates 25% of that range and adds that value to the market close to determine a buy point for the next day. Hence virtually any kind of meaningful upside move the following day will consequence in the dummy system buying the market. Typically the dummy system will get a buy signal about every other day and show around ten trades for every 20 trading days or so.

3) A day of entry stop is closest entered when a position is taken. Using 15 minute bar data this stop will exit a market if it retraces its move more than 75% from the last intra-day high. This stop is rarely hit.

4) All locaiongs are closed out on the close of the trading day.

This dummy system is really just a screening device. This is uncompletely results from a GOOD MARKET, BIOF, which was tested on intra-day data for eight weeks from 11/09/2009 by 1/08/10:

BIOF BioFuel Energy Corp. (NASDAQ) 15 min bars 11/09/09 – 1/08/10

Total Net Profit = $552
Number Trades = 17
Wins = 10 (59%)
Average profit per trade (wins and losses) = $32.49

This is uncompletely results from a BAD MARKET, ARBA, which was also tested on Intra-day data for eight weeks from 11/09/2009 by 1/08/10:

ARBA Ariba, Inc. (Public, NASDAQ) 15 min bars 11/09/09 – 1/08/10

Total Net Profit = $44

Number Trades = 19

Wins = 12 (63%)

Average profit per trade (wins and losses) = $2.32

When you look at the three month charts of these markets you may be inclined to believe that both markets are volatile and would be good markets to trade. traditional methods of calculating volatility will probably show that both markets are indeed volatile. But when we apply the dummy system to the 15 minute charts the difference between these markets becomes apparent.

The bottom line is that BIOF is a great market for our methods, but we are wasting our time with ARBA. The problem is that ARBA is simply not volatile enough to conquer our transaction costs when trading our comparatively small locaiongs. For this reason we must reject this market.

As a rule of thumb when I examine markets with the dummy system I like to see the average trade (win loss) over $10. If the average trade is less than $10 I reject the market for use in our portfolio.

I have found that this method of market selection for identifying “hot markets” for short term trading to be superior to other methods, commercial or otherwise. I have found that markets that show an average trade greater than $10 using the dummy system will usually show excellent real time profits trading our short term trading methods.

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