StockQP Analytics Professional

StockQP Analytics is an advanced and specialized research tool. It has existed in several forms for more than half a decade, having been used exclusively in research capacities with significant success. It is designed and built around one important question.

"If a stock is down X consecutive days and Z percent over those days, should you buy and hold for Y days?"

This question may be better described as a mathematical theory. It is as follows: Given company C with market cap ~$100 billion whose stock goes down a significant percentage on any arbitrary day of trading, it is more likely to go up than down over the following days of trading.

The most straightforward explanation for this is:

"If a highly-valued company is going to 'stick around' for the foreseeable future and remain somewhere in the neighborhood of its current market cap and its stock price drops a significant percentage, it is more likely than not to rebound than it is to continue decreasing."

This may seem counter-intuitive because the generally accepted idea is that the market is completely chaotic. Many believe that on any given day, any arbitrary stock is as likely to go up as it is to go down. However, the logic in the following paragraph may serve to convince one otherwise.

"Given that a company C is going to survive and maintain a significant market share, then it can be considered impossible that its value will reach or even approach zero. In fact, the closer its stock price gets to zero, given roughly the same market cap and market share, the more out-of-whack its P/E ratio becomes. In order to maintain a relatively market-acceptable P/E ratio, the stock is going to tend to go upwards after a downward drop. In general, the closer company's stock comes to zero, the more likely it is to rebound.?

StockQP Analytics analyzes two decades of stock data in order to produce a percentage of profitability given certain market conditions for certain symbols. In the following sections, detailed examples will be provided showing the power and usefulness of StockQP Analytics. For more information, view the manual.

$79.95 StockQP Analytics Professional

$49.95 StockQP Analytics Standard

Free! Download the trial version

Why Use StockQP Analytics?

StockQP Analytics is an excellent tool for two types of traders. The program is powerful enough to be used as the engine for various trading strategies. In the professional version, its command line runnability can be harnessed by a wrapper program which actively monitors market conditions and uses StockQP Analytics to help identify ideal conditions to get into and out of the market. Short Term traders may find the program extremely useful in backing up their own research and trading methods. For more information, view the manual.

Research Tool for Short Term Traders

StockQP Analytics is an ideal tool for Short Term Traders (day traders who don't mine holding a position for a few days). Most short term traders use a combination of research, media monitoring, other technical analysis, and intuition to determine the correct times buy and sell a stock. If a trader determines that he has a 55% chance of making a profit on a particular trade, but StockQP Analytics tells him that he has only a 42% chance of making a profit, he may want to consider otherwise. StockQP Analytics analyzes historical data. It identifies similar conditions for the symbol in question (and perhaps other symbols that act in a similar fashion) and determines if buying and holding this stock is a profitable endeavor in the long run when this particular condition is met. Example 2.1 may provide further insight:

On August 1^st, 2006 a trader is interested in purchasing 25,000 shares of CIEN, expecting a rebound in 1 or 2 days due to a recent drop in its price. In this hypothetical situation, the trader sees that the stock has been down for 3 consecutive days and is down 8 percent over those days. The trader has also determined that CIEN, JDSU, and FNSR usually seem to fluctuate in a similar pattern (as is common among symbols within the same sector). The trader provides the following inputs to StockQP Analytics:

Days Down:	3
Percent Down:	8
Days to Hold:	2
Amount to Invest:	$90,000
Symbols:	CIEN, JDSU, FNSR

These inputs instruct StockQP Analytics to search for any conditions that meet ?A stock is down 3 consecutive days and 8 percent over those days?. It then simulates a purchase of CIEN, JDSU, FNSR when these conditions are met, holding the position for exactly 2 days and selling. The trader also specifies the range of historical quotes to search through:

Start Date:	01/01/89
Finish Date:	08/01/06

StockQP Analytics will search through all quotes for the listed symbols that fall within this range and provide the appropriate statistics afterwards. StockQP reports that these conditions have been met 534 times for these symbols. 276 trades resulted in an average profit of 8.38% while 246 trades resulted in an average loss of 6.48%. The expected outcome of buying CIEN now and holding for 2 days, as estimated by StockQP Analytics, is a 1.34% profit (or ~$1,200).

Example 2.1 provides a great example of the usefulness of StockQP Analytics as a research tool to aid short term traders. Spending only seconds in StockQP Analytics can give a trader a big advantage over other traders and provides another great source of information about the strength of a potential position in the market.

The following image should interest potential and existing users of StockQP Analytics.

In the image, the red line represents a composite value that gives a good representation of 3 symbols, YHOO, CNET, and PCLN. Stocks were purchased when they were down for 3 consecutive days, 4 percent over those days, and held for 2 days. It is amazing to many that such a trading scheme could hold up so well despite such a market crash (in late 2000). For more information, view the manual.

$79.95 StockQP Analytics Professional

$49.95 StockQP Analytics Standard

Free! Download the trial version

Stand-Alone Tool for Identifying Ideal Market Conditions

StockQP Analytics is powerful enough to be used as an engine at the core of a trading strategy. The strategy in section 2.1 was to use StockQP Analytics to determine if the current conditions for a potential position are favorable. In this section, it is shown how StockQP Analytics can be used to identify the most profitable conditions for all symbols, market-wide.

This can be done by writing a wrapper program or script that uses StockQP Analytics as a slave. In slave-mode, StockQP Analytics is executed via the command line. Example 2.2 provides the general design of such a trading system.

After the market is closed on a trading day, a trader fires up his trading program which uses StockQP in slave mode. For the sake of simplicity, we'll say this trader is only interested in trading in semiconductor stocks.

The trader identifies a list of stocks in the semiconductor industry which go up and down in value in a similar fashion. For the sake of simplicity, we'll say he chose only INTC, AMD, MU.
The next step it to determine how far back in history to have StockQP Analytics to analyze for profitable conditions. The trader decides that the three stocks have been trading in a similar fashion since 1998, so he chooses 1/1/98 through present day as his range.
Next, the trader sets up the wrapper program to track the results of different combinations of X, Y, and Z values over the range of dates and symbols selected in the previous steps. (where X is days down, Y is days to hold, and Z is percent down).
The next step is to sort the different combinations of X, Y, and Z to determine which are most profitable.
The last step is implementation specific. It is the trade system administrator's duty to interpret these results in the most profitable fashion.

In our example, we'll say StockQP, in step 4, returned these following three conditions as the most profitable:

Stock is down 2 days, 8 percent, hold for 1 day 55.3% with 120 trades
Stock is down 1 day, 7 percent, hold for 2 days 54.9% with 99 trades
Stock is down 3 days, 7 percent, hold for 1 day 53.2% with 412 trades

The trader has decided to take these top three results, and execute a trade on INTC, AMD, or MU if the current market conditions match any of the conditions just listed. In this type of trading strategy, the trader might not even look at news or vital statistics about the stocks. How can somebody trust a program that says when to get into the market? The next chapter gives some important insight. For more information, view the manual.

$79.95 StockQP Analytics Professional

$49.95 StockQP Analytics Standard

Free! Download the trial version

How accurate are the statistics?

This is the most important question to consider when formulating a trading system or analyzing a research tool for short-term traders. StockQP Analytics analyzes historical data and determines which conditions were most profitable over the long haul. In a way, StockQP Analytics ignores news, hype, and earnings because these things are not included in the database. However, if you look at it another way, StockQP Analytics considers everything there is to consider about stocks. Since the program considers thousands upon thousands of quotes, it encounters earning situations, good news situations, bad new situations, bull markets, and bear markets time and time again. Any and all of these situations are factored into the statistics reported by StockQP Analytics. For more information, view the manual.

This brings us back to the primary question. How accurate are the statistics? While Rubicite Interactive offers no guarantee as to the accuracy of the statistics reported, the author has seen good results. Though it is a nontrivial task, writing a simulator (as seen below) can analyze and determine the accuracy of StockQP Analytics' predictions. The general design for such a simulator is listed below:

Writing a Simulator

For every day of 2006, starting on January 1st
- For each sector in the list of sectors
  - Determine optimal trading conditions based upon the previous 5 years of trading for this sector (2001 -> Current Day)
- Analyze the market conditions on this day of trading
- See if any of the current market conditions match a 'profitable condition' found for any symbol in any sector
- Purchase any stocks that match profitable conditions, and hold them for the predetermined amount of time.
- Track statistics about how accurate StockQP Analytics' predictions actually were.
Display overall statistics about the accuracy of StockQP Analytics' predictions.

Scoring System
There exist many ways to implement such a simulator. For example, if there are 150 combinations of X, Y, Z conditions being analyzed for each sector on each day, how many should be selected for active trading? If the top 3 are taken, fewer trades will be executed due to the rarity of such events. If the top 20 are taken, many trades will be executed, but profitability may suffer due to too many bad conditions being selected. Balance must be achieved. In fact, the author of StockQP Analytics suggests a weighted scoring system for X, Y, Z conditions based upon: number of trades a condition yielded historically, the percentage of profitability, the average gain per profitable trade vs. the average loss per unprofitable trade, and the overall exposure of the bankroll.

What does a simulator tell us?
The results from the simulator can give a good indication of the predicted chance of profitability vs. the actual profitability. Again, it should be stated that no two implementations of such a simulator will be the same due to many, many variables which must be set during development. However, despite these many variables, it can be seen that the 5 years previous to 2005 can be used to predict with some accuracy the movement of stocks in 2005. It really goes back to the discussion in the Introduction... As high market-cap companies approach zero, they are more likely to bounce back in order to maintain a market-acceptable P/E ratio.

What kind of results are we looking at (For a trading system)?
StockQP Analytics' best results are going to be high-volume conditions yielding a 52% to 55% chance of profitability. This means that nearly half of the time, trades will result in losses or no gain when StockQP Analytics is used as the only tool for determining if a trade should happen. However, when StockQP Analytics is used at the core of a trading system, a bankroll management system which utilizes chunks of size 2 to 20 percent of the overall bankroll to execute trades is going to be much more successful in the long run. It's like being a card counter at a blackjack table. Their goal is to hit that 51% chance of profitability over and over. However, they don't use their entire bankroll. These use chunks as mentioned previously. They realize that despite their greater than 50% chance of profitability, they still have a significant chance of losing money on every hand. As card counters say, "We want the law of large numbers to take over". If your sample size is great enough, you should end up making a profit. This brings us back to StockQP Analytics. If we're given a 54% chance of profitability on a trade, a trading system that bets the entire bankroll is sure to fail and fail fast. Bankroll management will be discussed further in the Strategy Chapter 5.

What kind of results are we looking at (For Short-Term Traders)?
StockQP Analytics is going to return results like "52% chance of profitability on the next trade". Typical percentages are in the range of 45% to 55% on high-volume conditions (conditions that happen often). These percentages take everything into account. So, used in conjunction with other research, market analysis, and good intuition, it should serve to increase profitability. If a trader is still undecided about whether or not to make a trade, but StockQP Analytics is saying that historically there is a 55% profitability rate for such a condition, it may be wise to invest. On the other hand, StockQP Analytics can serve warnings to people in the same situation. A 42% chance of profitability would not seem very appetizing, even if the trader 'has a good feeling'. That's a pretty low rate of profitability in the long run! The primary goal of this implementation of StockQP Analytics is to serve as a tool for short-term traders to aid them in making decisions about when to and when not to enter a position in the market. For more information, view the manual.

$79.95 StockQP Analytics Professional

$49.95 StockQP Analytics Standard

Free! Download the trial version

FAQ

How will this software benefit me?
StockQP Analytics provides an excellent service to short-term traders and trade-system administrators. For short-term traders, it gives some assurance as to whether a given condition may be profitable or not. To trade-system administrators, it provides yet another resource for improving the bottom line. For more information, view the manual.

How steep is the learning curve for this program?
Ever wonder what would happen if you traded a certain way for a year? With StockQP you can find out with just a few clicks. It is simple to use as a research tool, yet powerful enough to be used as an engine for a vast trading scheme. For more information, view the manual.

Does the program offer stock quote database updates?
Yes. StockQP Analytics Professional users will enjoy monthly complete-updates to their database with the ability to update individual symbols daily through the program itself. Standard-Version users must wait for annual upgrades to their database. For more information, view the manual.

Are there any guarantees? What If I lose money because StockQP said I was more likely than not to make a profit?
StockQP Analytics and Rubicite Interactive provides no guarantee as to whether or not its predictions will yield profits. The statistics provided are based upon thousands of historical quotes and market conditions. These statistics are saying, "In the long run, based upon historical data, if things continue the way they have been, these statistics should hold roughly true". That being said, we think StockQP Analytics users will thoroughly enjoy using the product and analyzing their results. For more information, view the manual.

Why does this product cost so much?
It was much debated internally about whether or not to release this program at all. But after careful consideration and the desire for the company to move towards game development, the decision was made to polish up a commercial version for sale to the public. The program has evolved over years of development as heavy research tool. StockQP Analytics was at the core of major simulators analyzing trends in the market. The code is very specialized but, we think, provides a great service. We believe the cost of the product reflects its usefulness and quality. For more information, view the manual.

$79.95 StockQP Analytics Professional

$49.95 StockQP Analytics Standard

Free! Download the trial version

For more information, view the manual.