StockQP Analytics v1.0 Manual

Copyright © 2006 Rubicite Interactive

Table of Contents:

 

1 - Introduction to StockQP Analytics


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.

2 - 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 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.

2.1 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 tells him that he has only a 42% chance of making a profit, he may want to consider otherwise. StockQP 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 1st, 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 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).

2.2 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 to determine if the current conditions for a potential position are favorable. In this section, it is shown how StockQP 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 as a slave. In slave-mode, StockQP 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.


  1. 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.

  2. The next step it to determine how far back in history to have StockQP 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.

  3. 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).

  4. The next step is to sort the different combinations of X, Y, and Z to determine which are most profitable.

  5. 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:
  1. Stock is down 2 days, 8 percent, hold for 1 day       55.3% with 120 trades
  2. Stock is down 1 day, 7 percent, hold for 2 days       54.9% with 99 trades
  3. 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.

3 - 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 analyzes historical data and determines which conditions were most profitable over the long haul. In a way, StockQP ignores news, hype, and earnings because these things are not included in the database. However, if you look at it another way, StockQP 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.

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's predictions. The general design for such a simulator is listed below:

Writing a Simulator

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 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's 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 is used as the only tool for determining if a trade should happen. However, when StockQP 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. 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 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 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 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 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.

4 - Menus, Tabs, and Functionality

 

In this chapter all aspects of the user-interface and program functionality are explained in detail.

4.1 The Tabs

4.1.1 The Main Tab


The Wizard

The wizard guides the user through setting up an analysis. For details on what each part does, please see the section below about Manual Analysis Setup.

Manual Analysis Setup

In general, the Manual Analysis Setup is an efficient way for experienced users to setup an analysis without having to traverse the many dialogs of the wizard.



Figure 4.1.1    Manual Analysis Setup on the Main Tab


Perform Test - The Perform Test button first determines if the combination of inputs are valid. Upon validation, the inputs are handed off to the primary StockQP engine. Results of the analysis are listed on the Trade Log, Statistics, and Chart tabs.
Show Trade Log - This button offers the user a convenient way to switch to the Trade Log tab.
Show Statistics - This button offers the user a convenient way to switch to the Statistics tab.
Show Chart - This button offers the user a convenient way to switch to the Chart Tab.

1. Money Management Selection


This section allows the user to select different styles of money management. The popular choice is to start with $1,000,000 and trade with what's left after previous trades. This choice is more likely to be used in real-life trading. The option to trade with the full amount of money on every trade is offered along with an option to trade in chunks of any size. Chunks of 2 to 20 percent of the size of the bankroll are common. The options to trade with $1,000,000 per trade or 10,000 shares per trade speak for themselves. The choice to purchase 10,000 shares per trade is of particularly limited use due to 10,000 shares being worth vastly varying amounts from symbol to symbol.

2. Selection of the X, Y, and Z Variables



X - This variable refers to the number of consecutive days down a stock must be down in order to be considered for purchase.
Y - This variable refers to the number of days a stock is to be held before selling
Z - This variable refers to the percent a stock must be down over X days in order to be considered for purchase.
Trade Fee - The trade fee is the cost per purchase or sale of a stock.
Ave Spread - The average spread is the average distance between the bid and ask price of a stock.

There is currently a limitation of the range of the X, Y, and Z variables. If a wider range is needed, please contact the dev team. The current range was chosen to allow for a wide range of testing while keeping the engine as efficient as possible.

3. Selection of a Time Span



This time span refers to the section of the database to analyze. Quotes that fall between the Start and Finish dates are analyzed for symbols in the Active Symbol List. Sometimes it is best to search a few years back instead of the full 20 years due to the split-adjusted quote representation. Details are discussed in chapter 5.

4. Symbol Selection



This section allows for the specification that StockQP should consider one particular symbol in the Active Symbol List or all symbols in the Active Symbol List. It was designed such that most users will trade with all symbols in the Active Symbol List. If these users suspect a particular symbol is giving strange results, it is simply a matter of clicking on 'Single Symbol' and re-running the same test. The Chart, Trade Log, and Statistics Tabs will give a wide range of information pertaining to the single symbol instead of a combination of all.

4.1.2 Trade Log Tab



The Trade Log tab tracks the progress of every trade executed in the latest analysis. Below, each column is described in detail:
Type - This column contains either Buy or Sell referring to the type of entry in the Trade Log
Symbol - This column contains the symbol of the stock being bought or sold.
Date - This column contains the date of the purchase or sale.
Price - This column refers to the current price of the symbol listed in the symbol column.
Percent* - When this column is red, it refers to the percent the price of the stock is down over X days. Remember, X refers to the number of days a stock must be down before being considered for purchase. When the percent is blue, it refers to the percent a symbols' price has changed since it was purchased.
Shares Bought - This column contains the number of shares bought on any given trade.
Shares Sold - This column contains the number of shares sold on any given trade.
Profit - This column contains the dollar value of profit on individual sales.
Cash - This refers to the amount of cash currently not invested.
Net Worth - This column refers to the amount of cash currently not invested plus the total amount of all outstanding investments.

4.1.3 Statistics Tab



The statistics tab is commonly the most-used tab. For every analysis, it is important to study key statistics such as Funds Performance, Trade performance, and basic symbol performance (especially Average Daily Variation!). The tool in the bottom left part of the screen allows the user to cycle through each symbol selected for the current analysis (which will normally be the entire active symbol list). One symbol is added which is the combination of of all symbols.

All* - This symbol is a combination of all symbols in the Active Symbol List. Its price is the sum of all 'active' symbols. In other words, symbols which have yet to IPO or appear on the market aren't counted until they IPO or appear on the market. Some statistics such as stock price extremes are ignored on this symbol because they are irrelevant. Statistics such as Overall Growth give a good idea of the overall performance of all selected symbols.

Basic Stats



Symbol - Current Symbol whose statistics are being displayed
Start Price - The earliest quote encountered for this symbol
Finish Price - The latest quote encountered for this symbol
Average Price - The average value, in dollars, of this symbol over the specified range of dates.
High - Highest price the symbol reached.
Low - The lowest price the symbol reached.
Days Up - Number of days the symbol went up from one day to the next.
Days Unchanged - Number of days the symbol went neither up nor down from one day to the next.
Days Down - Number of days the symbol went down from one day to the next.
Ave. Daily Variation - The average amount the symbol goes up or down on any arbitrary day of trading. A high value implies a volatile stock.
Best Yearly Growth - The best year of growth, where a year is considered to be from January 1st to December 31st. (not any 365 day period)
Worst Yearly Growth - The worst year of growth.
Ave. Yearly Growth - This value may be confusing to some. It is possible to have a positive average yearly growth, but have an overall decrease in the price of a stock. For example, if stock XYZ began one year at $10 and finished at $20, we'll say it had a 100% growth that year. If it decreases by 40% over the next year, it will finish at $12. The next year, it decreases by 50% to $6. However, when the average yearly growth is computed we come up with a positive number. (100% - 40% - 50%) / 3 = 3.33% average yearly growth. The reasoning behind this representation for Average Yearly Growth is that short-term traders and trade-system administrators are less interested in the Overall Growth divided by the number of years, and more interested in the average performance a stock has on any given year.
Overall Growth - This value is simply the (Finish Price - Start Price) / Start Price. It gives the percentage the stock is up or down from the start to finish of the selected date range.

Stock Price Extremes



These statistics are symbol specific and are tracked over the specified date range.

Trade Stats



These statistics are not symbol specific. In other words, all symbols selected for a particular analysis contribute to these statistics.
Total Trades - Total Amount of trades across the specified date range.
Best Trade (Profit) - The best trade in pure dollar amount. (Not necessarily the best percentage trade)
Best Trade (Percent) - The best trade percentage-wise. (Not necessarily the best money trade)
Trades Resulting in Gains - Number of Profitable Trades.
Chance of Gain next trade - ((# of Profitable Trades) / (Total Trades)) * 100%
Average Gain (percent) - (Sum of all gain percentages) / (Number of trades resulting in gains)

Worst Trade (profit) - The worst trade in pure dollar amount. (Not necessarily the worst percentage trade)
Worst Trade (percent) - The worst trade percentage-wise. (Not necessarily the worst money trade)
Trades resulting in losses - Number of trades that resulted in a negative income.
Chance of Loss next trade - ((# of trades resulting in losses) / (total trades)) * 100%
Average Loss Percent - (Sum of all loss percentages) / ( Number of trades resulting in losses)

Average Profit per Trade - (Sum of all trade results (%)) / (Total Trades)
Average Profit per Trade - (Sum of profits ($) of all trades) / (Total Trades)
Trade Ratio - (Total # of Trades resulting in gains) / (Total # of Trades resulting in losses)

Longest Streaks



Price Up / Price Down - All selected symbols in an analysis are monitored for the longest days-up streak as well as the longest days-down streak. As seen in the picture above, WMT was up 12 consecutive days and 14.05% percent over those days ending 2-6-95.

4.1.4 Chart Tab



Combined Stock Price - The combined stock price is calculated in the following fashion.
  1. The maximum and minimum price for each symbol are determined
  2. For each day of trading, each symbol's price compared to its high is determined as a percentage. Example: If the all-time high (in the specified date range) for MSFT is $400 on 1/1/2003, and its price on 1/1/2002 is $200, then its price compared to its high is 50%. This number is 'registered' as a 50.
  3. To determine the combined stock price for any given trading day, each 'active' symbol's price percentage (from step 2) is summed up and averaged. This gives a number between 0 and 100.
This is done because it gives a more accurate representation of the overall movement of all symbols in the active symbol list.

Stock Price vs. Funds

The blue line on the right-most Y-Axis refers to the level of trading funds. The numbers refer to the amount, in thousands, that the funds have varied from the neutral point (0). The red line refers to the Combined Stock Price. The two plots give an excellent representation of the overall performance of a trading strategy given different market conditions. Some strategies offer better performance on a down-market, some prefer an up-market.

4.1.5 Active Symbol List Editing Tab



Active Symbol List - The Active Symbol List, also seen on the Main Tab, contains up to 50 Symbols which can be used in an analysis.

Complete Symbol List - When one of the radio buttons are selected to the right of the Complete Symbol List, the Complete Symbol List fills up with all symbols which start with the first letter associated with the clicked radio button. This has proven to be an efficient way of sorting through 5000+ symbols. Note: It is possible to select multiple symbols and add them all at the same time by clicking the ADD button.

Add Button - When symbols are selected in the Complete Symbol List, the ADD button sends them over to the Active Symbol List.

Del Button - When symbols are selected in the Active Symbol List, the DEL button removes them from the Active Symbol List.

Save Button - This button allows the user to save an Active Symbol List to file. This provides a convenient way to manage often-used Active Symbol Lists.

Load Button - This button provides the user with a way to load Active Symbol Lists from file.

Default Button - This button loads the Default Symbol List. The Default Symbol List is located in the root program directory and is called "Default Symbol List.txt". Feel free to edit it. (Max 50 symbols).

Clear Button - Clears either the Active Symbol List or Complete Symbol List depending on which Clear button is selected.

Add Single Symbol to Active Symbol List - This area provides the user with a quick way to add particular symbols to the Active Symbol List. There is no need to hit the Insert Symbol button. it is only necessary to type in the symbol name and press the enter key.

Sector Management - Sectors are stored in Sectors.dat in the root program directory. They are stored in the form: <Sector Name> <Symbol1> [Symbol2] [Symbol3] ... [Symbol N] where <> refers to a mandatory entry and [] refers to optional entries. In other words, for every sector enter a sector name followed by up to 50 symbols separated by spaces. Show Symbols provides a message box displaying the symbols in the current sector. Move Symbols to Active Symbol List moves all symbols in the current sector to the Active Symbol List. Move ALL Symbols to Active List moves symbols from all sectors to the Active Symbol List. This is only recommended if there are fewer than 51 symbols across all sectors.

4.1.6 Update Quotes Tab



The Update Quotes Tab provides the user with a simple way to update individual quotes via StockQP Analytics' online quote database. Note: This service is enabled for professional users only. Professional users also receive a completely up-to-date database every month, whereas Standard users are limited to an up-to-date database every 12 months.

The Buttons - The top button checks the online database for the date of the latest quote for a particular symbol in the online database. The Middle Button checks the local database for the latest quote entry. The Lower Button actually performs an update for the specified symbol. (Internet Connection Required for the Lower and Upper Buttons.)

4.2 The Menus

4.2.1 File Menu



Open - This provides the ability to load a saved analysis. Once a .SQP file is chosen, StockQP will automatically set the checkboxes, dropdown menus, etc to reproduce a pervious analysis.

Save & Save As - These provide the ability to save an analysis to a .SQP file. The files remain small making them convenient for email.

4.2.2 Options Menu


The General Options dialog allows the user to configure StockQP Analytics.

General Options - Main



The Main Tab of the General Options Menu allows the user to toggle two checkboxes.

1. Remember Trade Settings - If this is checked, StockQP Analytics will write the last-used trade settings to file upon closing. When StockQP is loaded in the future, those settings are reenacted. (Mainly items on the Main Tab)
2. Show Critical Statistics - If checked, a dialog will pop-up after every analysis displaying several key statistics about that analysis. Some users may prefer to go directly to the Statistics Tab so this option is provided.

General Options - Active Symbol List
This tab provides the user with another way to load the default symbol list.

General Options - Trade Log



The Trade Log Tab allows the user to set various colors of the Trade Log ListView (on the Trade Log Tab of the main program) or to toggle the Grid Lines on the Trade Log.

4.3 Command Line

Note: This functionally only exists with the professional version

The command line runnability of StockQP Analytics allows it to be used as an engine in a broader trading scheme. Typically, a wrapper program will be written that generates a StockQP input file and then calls StockQP. The program would then analyze the results and proceed. Such a program may call StockQP thousands of times in order to strengthen an existing trading scheme or building a new one.

Usage

SQP.exe <InputFile> <OutputFile>


Input File Format

An example input file is provided in the program directory (A.input). The file contains 11 required lines.
  1. Days Down
  2. Days Up
  3. Percent Down
  4. Start Date Format: m/d/yyyy or mm/dd/yyyy
  5. Finish Date Format: m/d/yyyy or mm/dd/yyyy
  6. Chunk Size
  7. Fund Type. Choices are: 10kPerTrade , 1MillionPerTrade, 1Million
  8. Active Symbol List to use for the analysis (File Name)
  9. Trade With Chunks? True or False
  10. Trade fee
  11. Average Spready

Each Entry must contain at least 1 character followed by a colon followed by the entry. An example for Days Down would be: "Days Down:3". It is important to not leave any characters between the colon and the entry or after the entry. For more details, see A.Input in the program directory.

Output File Format

An example output file is provided in the program directory (A.Output). The file contains 21 statistics about an analysis.

  1. Total Trades
  2. Best Trade (Profit)
  3. Best Trade (Percent)
  4. Trades Resulting in Gains
  5. Chance of Gain next trade
  6. Ave Gain percent
  7. Worst Trade (profit)
  8. Worst Trade (percent)
  9. Trades Resulting in Losses
  10. Chance of Loss next trade
  11. Average Loss percent
  12. Average profit per Trade (%)
  13. Average profit per trade ($)
  14. Trade Ratio (Gains:Losses)
  15. Funds Start Value
  16. Funds Finish Value
  17. Funds High Value
  18. Funds Low Value
  19. Funds Average Value
  20. Longest Up Streak
  21. Longest Down Streak

These statistics mirror those from the Statistics Tab in content and format. The format for each entry in the output file is: <Description>:<Value> Where the description describes the type of entry and is followed by a colon which is followed by the value. There is no space before or after the colon. To determine the appropriate way to parse the text-based values, view A.Output in the program directory.

5 - Advanced Use

 

Bankroll Management

When using the "Start with 1 million and trade with what's left after previous trades" option, it is important to determine the chunk size that is most appropriate for a particular trading strategy. If the X, Y, and Z conditions may 'hit' on 20 or 30 symbols on a single trading day, it makes sense to invest in several of the stocks. In the author's experience, chunk sizes 10-20% of the size of the entire bankroll typically yield interesting results.

Trade Fees
Keep in mind that trade fees are counted twice for a particular investment; once for the purchase and once for the sale. Trade fees can be set on the Main Tab.

5.1 Design of a Typical Program which Harnesses StockQP Analytics' Command Line Runnability

Given the following

General Design

Note: There is absolutely no guarantee that profitable conditions in the past will result in profits in the future. Please visit section 1 for the logic behind StockQP.

5.2 Design of a Simulator to test the usefulness of StockQP

It would be nice to run StockQP on historical data up to today in order to give a good idea which conditions may yield profitability for certain groups of symbols, and track the results during the actual trading day. This may be interesting one or twice, but in order to build up a useful sample size, this needs to be done thousands of times. This is where a simulator comes in. A simulator effectively goes back a year or two in time, and does just this. The general design for such a simulator is listed below:

Given:


Many of these simulators have been written with very interesting results. The main focus here is designing a good algorithm for ranking conditions based upon predetermined likihood for profitability, actual predicted profitability in StockQP, and actual profitability in the past. These three items do not always line up perfectly, so there must be some balance. Other important things to consider is bankroll management - what size chunks should be used? These are just a few of the variables one will encounter when designing a simulator for StockQP, and all make a big different in the results.

6 - FAQ

 

How will this software benefit me?
StockQP 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.

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.

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.

Are there any guarantees? What If I lose money because StockQP said I was more likely than not to make a profit?
StockQP 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 users will thoroughly enjoy using the product and analyzing their results.

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.
Copyright © 2006 Rubicite Interactive