The Stock Bot

1. The Stock Bot •Stock Pricing Primer •Prediction Necessities Select Navigation Menu Item •Prediction Technology •Exchange Traded Stocks

2. Stock Pricing Primer • Long term price movement is determined by: • Actual Economy or Perception of the Future Economy by Traders • Financial News – Both Good and Bad • Major incidences such as 9/11 • Strength and Profit potential of the company • Strength of the company’s business sector • Debt to Equity Ratio along with dozens of additional accounting ratios • Earnings Potential of the sector and other companies of similar size and type within the sector • Trader perception of the stock going up or down with associated profit taking • Many others •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

3. Stock Pricing Primer Short term price movement from 1 minute to the next is determined by: • Spread and direction of the Bid and Ask Prices • Size of Bid and Ask Offers and Requests - This is used to determine Overbought or Oversold Conditions with associated price movements. • Price change resistance points and levels • Supply and Demand reflected in the Bid and Ask Size, Price, and Direction, factored by Resistance Profit is made my Buying Low (Long) and Selling when the price goes up or Selling High (Short) and Buying when the price goes down. The profit or loss is determined by the spread between the high and low price. •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

4. Prediction Necessities • Very Fast Computer capable of thousands of calculations per second • Fast Internet access for Instant Quotes, Buys, and Sells • Exchange Traded Funds • Data Cluster Accumulation Technology • Statistical Law of Large Numbers •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

5. Prediction Technology Time Sliced Data Cluster Accumulation Live Stock Market Data Live Internet Data Socket •Stock Pricing Primer •Prediction Necessities (M)ath (A)utomated (P)rocessor (D)ata (P)attern (M)odeler (A)rtificial (I)ntelligence (M)odule •Prediction Technology •Exchange Traded Stocks Prediction Manager–Processes all Data and Makes Final Buy, Sell, Or Hold decisions.

6. Prediction Technology • Time Sliced Data Cluster Accumulation • Prediction Manager – Gets data from: • MAP – Math Automated Processor on Time Sliced Data Clusters • DPM – Data Pattern Modeler C. AIM – Artificial Intelligence Module • Pareto Principal – 20% of the data affects 80% of the moves. Prediction Technology Accuracy •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

7. Time Sliced Data Cluster Accumulation •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks Each Cluster is Loaded with data every 2 seconds from the stock market data socket. Trends are measured for each individual cluster. Trends are then computed between each cluster creating pressure valves. When all clusters are filled, the last cluster is dumped, the remaining clusters are moved over, and the first is filled with new data. Pressure valves are all re-computed. This is a continuous ongoing process. All of these computations made by the (M)ath (A)utomated (P)rocessor.

8. MathAutomated Processor • Time Sliced Data Cluster Accumulation • (M)ath (A)utomated (P)rocessor • Sets up Pressure Valves and computes pure mathematical trends based on Time Sliced Data Cluster Accumulation Data • Determines Model Signature Patterns • Determines Up Stages 1, 2, 3 or Down Stages 1, 2, 3 from the Pressure Valves • 4. Turns data over to Data Pattern Modeler •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

9. Data Pattern Modeler (M)ath (A)utomated (P)rocessor (D)ata (P)attern (M)odeler 1. Takes mathematical data and adjusts Pressure Values based on models determined from the Model Signature. Models are based on previous Stock Market Dynamics under similar circumstances. 2. Determines the critical 20% of data necessary to make a sound decision. 3. Tracks and Records Price Change Resistance points 4. Turns Adjusted data over to (A)rtifical (I)ntelligence (M)odule •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

10. Artificial Intelligence Module (A)rtificial (I)ntelligence (M)odule 1. Takes submitted data, creates an AI Stream and determines the following patterns: A. Wiggle: Prices go up and down with no discernable pattern. B. Bounce: Prices have hit a resistance point and are ready to either bump up or down. C. Trend: A discernable trend going either up or down. D. Correction: A price reaction usually negative) of at least 10%. 2. Adjusts Pressure Valves for Overbought or Oversold conditions and Price change resistance points. 3. Turns Data over to the Prediction Manager for a final Buy, Sell, or Hold Position. (D)ata (P)attern (M)odeler •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Funds (ETF)

11. Exchange Traded Funds Broad Market Funds Traded as Stocks • More predictable because the stock price is based on the indexed average of dozens of broad market stocks. IE: NASDAQ – QQQ, Dow Jones – DIAmond, S&P SPYder and about 16 More • Safer because of diversification over a large broad section of the market • Allows the statistical law of large number to come into play because of the number of stocks in the fund along with very large volume • Can be sold Short on the down tick unlike regular stocks, which usually cannot •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

12. Pressure Valves •Stock Pricing Primer •Prediction Necessities A descriptive term used to reflect and record the trends between the Time Sliced Data Clusters. A trend is determined in each individual Data Cluster as it is loaded from the stock market data socket. The MAP then determines the trends between the Data Clusters and the pressure values are updated varying the pressure from very strong down (-100), neutral (0), and very strong up (+100). It is the interpretation of the combination of all pressure values along with other data that is used to determine buy, hold, and sell positions in the DPM, AIM, and Prediction Manager. Remember that the Pressure Valves are constantly changing. When the last cluster is filled and all valves computed, that cluster’s data is dumped, the others are moved over and the 1st cluster is reloaded. The pressure valves are re-computed and the process continues. This is the core of the prediction technology. •Prediction Technology •Exchange Traded Stocks

13. Stock prices are partially determined like other commodities, real estate, automobiles, etc. – Supply and Demand. For example: If there are many more home buyers than available homes, housing prices usually go up. If there are many more homes than available buyers, prices generally drop. This is what tends to happen with stocks. If there are more buyers than sellers, prices tend to go up etc. Pressure Valves are adjusted in the AIM based on the size of the Ask (Buyers) and Bid (Sellers) sizes. Bid and Ask sizes are reported to the Time Sliced Data Accumulation queues every 2 seconds from the live stock market data socket to determine overbought (not enough buyers) or oversold (not enough sellers) conditions. Put another way, Overbought means that the market no longer has enough buyers. Oversold means that the market no longer has enough sellers. Severe overbought conditions tend to drive the prices down to attract more buyers while sever over sold conditions tend to drive the prices up to attract more sellers. This is another of many factors the Prediction Manager uses to determine Buy, Sell, or Hold, Positions. Overbought or Oversold Conditions •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

14. Levels or points of resistance where the stock prices tend not to move above or below without additional factors present. The Stock Bot AIM automatically determines these resistance points with data from the MAP. If a price reaches a point and moves in the other direction 2 or move times, that level and number of times reached is recorded as a Price Change Resistance Point. When the price reaches that level again, the Pressure Valves interpretation is adjusted to require more pressure to make predictions above or below this level based on number of times reached. These levels may be penetrated, but the Prediction Manager requires more pressure in ever increasing amounts to make a prediction. This is another of many factors the Prediction Manager uses to determine Buy, Sell, or Hold, Positions. Price Change Resistance Points . •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

15. Bid and Ask Prices along with the Bid and Ask size is at the core of the prediction technology. The Bid price is what the investor is willing to accept in order the sell the stock. The Ask price is what the investor is willing to pay to buy the stock. The Bid price is recorded when an investor sends request to sell at a specific price. The Ask price is recorded when an investor sends an order to buy at a specific price. The Spread is the difference between the Bid and the Ask Prices. As each data cluster is loaded the, initial Bid and Ask Prices, the spread, and the Bid and Ask sizes are recorded. Each successive input from the Stock Market Data Socket compares the data with the previous input go get an Up, Down, Or Neutral trend based on previous data for each cluster. Trends are then factored for Resistance Points and Overbought or Oversold conditions. Once the trends for the individual clusters are processed and pressure valves are computer between clusters in the MAP. The results are sent to the PDM to factor results according to specific Stock Model Signatures. These are then reported to the AIM for additional analysis before sending results to the Predication Manager for a final Buy, Sell, Or Hold Decision. . Bid and Ask Prices •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

16. Stock Market Data Socket In order to make accurate predictions, a very large volume of data is needed for the Prediction Manager to accurately predict Buy, Sell, and Hold Positions. An Internet Connection (Live Data Socket) is made with streaming Stock Market data being read directly by Stock Bot. New data comes in every two seconds and is read by the Time Sliced Data Accumulation clusters to be processed by the MAP. This is the starting point for the Prediction Technology to perform it’s function. In addition, the live data is recorded to a disk table as it is read from the Live Data Socket. This data can be accessed anytime to replay the Live Stock Market ticker with identical results. Stock Bot can read the data from this table instead of the Internet with no difference in program functionality. This allows back testing of the settings and allows testing of different program defaults without any money at risk. •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

17. The Stock Market is by nature unpredictable and self correcting. The good news however is that stock prices can only do two things – move up or down. The bad news is that these two things are difficult to predict because of dozens of different Stock Market Dynamics. • Stock Bot uses an equalizer called the Data Pattern Modeler or DPM. Models are setup to reflect Stock Market Behaviors under a wide variety of different circumstances. • For Example, the market may open below the previous days close, trend up than down below the open and end up above the open. There are dozens of other scenarios. The MAP determines a Model Signature based on data patterns and passes that information to the DPM to fit the prediction to the Stock Market Dynamics in effect. • The individual models have expected Pressure Valve values, Overbought or Oversold condition circumstances, Resistance Points, and other pertinent data that occurred in the past given the model dynamics. The models also have the ability to learn as the circumstances change. Stock Model Signatures •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

18. Nothing can realistically predict the Stock Market over a long period of time. There are far too many factors involved. When Pattern Day Trading (buying and selling during the course of day closing all positions by the end of the day), it easier to discern specific patterns because many of the long term factors are taken out of the equation and supply and demand is the primary reason stock prices change. Using Exchange Traded Funds makes predicting Stock Market Dynamics easier. Since stocks either go up or down. Guessing the direction over time will result in a 50% accuracy rate according to statistical theory. This makes money for the broker only at the expense of the investor. In most cases, a 60% accuracy is necessary to pay the commission fees and make a profit. This is why most day traders loss money. Even using the advanced Prediction Technology, 100% accuracy is not possible with Stock Bot or with any other of the dozens of available prediction methods, however, with over a 1½ years of live testing and back testing Stock Bot has a consistent 80% to 85% accuracy rate. Prediction Technology Accuracy •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

19. Factors that tend to cause the price of securities to move up or down. Some of the factors include: a. Bid Ask Spread – Difference between the Bid and Ask prices. b. Bid Ask Size – Number of shares available to sell or number of requests to buy. c. Market Volume – Number of shares bought and sold. d. Overbought or Oversold Conditions. e. Price Change Resistance Points f. Stock Upward and Downward Pressure measured by Stock Bot’s Pressure Valves. Stock Market Dynamics •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

20. Pattern Day Traders buy and sell during the course of day and close all positions before the end of the day. Buying and Selling – There are two ways to open a stock position. A stock can be bought first then sold when the price goes up. This is opening and closing a long position. The profit is the difference between the buying and the selling price. This is the way most stocks are bought and sold. The 2nd way to open a position is to sell a stock you don’t own. You are in fact borrowing the stock and the borrowed money from the sale is credited to your account. The position is closed by buying the stock with the borrowed money when the stock goes down. The borrowed money is paid back from the sale and the profit is the difference between the selling price and the buying price. This is opening and closing a short position. Opening a long position is what most investors are familiar with. The investor actually owns the stock. Short selling is used less because the investor never actually owns the stock. It is borrowed be sold later. In either case, it is accurately predicting the direction of the move (up for long and down for short) that makes a profit. A wrong prediction always results in a loss. Pattern Day Trading •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

21. Short Position - In a short sale an investor sells shares that are not owned. They are borrowed and eventually must be returned by buying the them back (Cover). Profit (or loss) is made on the difference between the price when the shares are sold compared to when they are purchased and returned. An investor makes money only when a shorted security falls in value. (Special Note: Borrowing the shares to open a short position happens automatically when the investor issues a buy order for shares that are not owned. Returning the borrowed shares happens automatically when the investor issues a buy order to cover the sale and close the short position.) Long Position – In a long purchase an investor buys and owns the shares until sold. Profit (or loss) is made on the difference between the price when the shares are purchased compared to when they are sold. An investor make money only when a long security rises in value. The purpose of Stock Bot is to tell when stock has upward pressure and will tend to go up or has downward pressure and will tend to go down. Without this information, selecting a Long or Short position is only a guess. Guessing wrong is always a loss for the investor. The broker always make a profit. Stock Positions •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

22. Time Sliced Data Cluster Accumulation Live Stock Market Data Live Internet Data Socket Prediction Technology •Stock Pricing Primer •Prediction Necessities (M)ath (A)utomated (P)rocessor (D)ata (P)attern (M)odeler (A)rtificial (I)ntelligence (M)odule •Prediction Technology •Exchange Traded Stocks Prediction Manager-Processes all Data and Makes Final Buy, Sell, Or Hold decisions.

23. Time Sliced Data Cluster Accumulation •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks Each Cluster is Loaded with data every 2 seconds from the Live Data Socket. Trends are measured for each individual cluster. Trends are then computed between each cluster creating pressure valves. When all clusters are filled, the last cluster is dumped, the remaining clusters are moved over, and the first is filled with new data. Pressure valves are all re-computed. This is a continuous ongoing process. All of these computations made by the (M)ath (A)utomated (P)rocessor.

24. MathAutomated Processor • Time Sliced Data Cluster Accumulation • (M)ath (A)utomated (P)rocessor • Sets up Pressure Valves and computes pure mathematical trends based on Time Sliced Data Cluster Accumulation Data • Determines Model Signature Patterns • Determines Up Stages 1, 2, 3 or Down Stages 1, 2, 3 from the Pressure Valves • 4. Turns data over to Data Pattern Modeler •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

25. Data Pattern Modeler (M)ath (A)utomated (P)rocessor (D)ata (P)attern (M)odeler 1. Takes mathematical data and adjusts Pressure Values based on models determined from the Model Signature. Models are based on previous Stock Market Dynamics under similar circumstances. 2. Determines the critical 20% of data necessary to make a sound decision. 3. Tracks and Records Price Change Resistance points 4. Turns Adjusted data over to (A)rtifical (I)ntelligence (M)odule •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

26. Artificial Intelligence Module (A)rtificial (I)ntelligence (M)odule 1. Takes submitted data, creates an AI Stream and determines the following patterns: A. Wiggle: Prices go up and down with no discernable pattern. B. Bounce: Prices have hit a resistance point and are ready to either bump up or down. C. Trend: A discernable trend going either up or down. D. Correction: A price reaction usually negative) of at least 10%. 2. Adjusts Pressure Valves for Overbought or Oversold conditions and Price change resistance points. 3. Turns Data over to the Prediction Manager for a final Buy, Sell, or Hold Position. (D)ata (P)attern (M)odeler •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Funds (ETF)

27. Statistical Law of Large Numbers The Law of Large Numbers says that in repeated independent trials with the same probability of success in each trial, the percentage of successes is increasingly likely to be close to the chance of success as the number of trials increases. More precisely, the chance successes tends to grow as the number of trails grows. Using Exchange Traded Funds spreads the trials over a large number of stocks (Broad Market) along with new data from the Stock Market Data Socket at a refresh rate of every 2 seconds giving a large volume of data (number of trails) for accurate prediction to occur. •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks

28. Pareto Principal The 80/20 rule that is based on Vilfredo Pareto's research that states only a vital few, (20%), of causes will have a greater impact than the many, (80%), causes. (i.e. 80% of the stock moves come from 20% of the stock data). Part of the job of the Data Pattern Modeler (DPM) is to use past stock market data to help determine the critical 20% that will lead to an accurate prediction. •Stock Pricing Primer •Prediction Necessities •Prediction Technology •Exchange Traded Stocks