Патенты

The invention relates generally to technical analysis of financial markets. More particularly, the invention relates to methods and systems for calculating predictive technical indicators.

BACKGROUND

Historically, the methods that have been used by traders to analyze the financial markets in an effort to identify and forecast the direction of price trends have been divided into two distinct approaches: fundamental analysis and technical analysis. Fundamental analysis focuses on underlying macro- and/or micro-economic factors such as Gross National Product, central bank policies, rates of inflation, unemployment rates, market share, earnings, profitability and supply/demand. The premise behind technical analysis is that all the factors that affect a specific market at any given point in time are already built into that market’s price, even if these factors are based on fundamentals or mass-psychology. Technically oriented traders concentrate on using various technical studies, indicators, and market-forecasting theories to analyze market behavior.

Traders are people who buy and sell financial instruments that are publicly traded on exchanges. Trading software applications subscribe to data from the exchanges and present it to traders, usually in the form of charts and watch lists. Traders and trading applications have come up with a variety of calculations that can be performed on electronic exchange data. Some of the more common technical indicators include trend indicators, momentum indicators, and volatility indicators. Many technical indicators, such as moving averages, attempt to filter out short-term variation in price so the underlying trend can be observed. A side effect of averaging past prices is that the indicator tends to lag behind the market. This causes the trader to respond late to market changes, resulting in lost profit opportunity and risk of increased losses.

SUMMARY

This invention relates to the development of methods, systems, and devices for developing technical indicators based on the combination of both historical and predicted data for a market. Historical data for a market can be obtained from available sources such as an exchange where the market trades. Predicted data can be obtained using a predictive server that uses intermarket analysis data to train neural networks to predict financial time series data. The invention can include software running on a back-end server (a predictive server) and a trading application (e.g. an application that runs on a trader’s PC), which operate within an information processing system that includes market exchanges and financial data providers.

In one aspect of the invention, historical time series data can be obtained and analyzed, predicted future times series data can be obtained using intermarket analysis performed using neural networks, conventional technical indicator information can be obtained, and an algorithm can be used to integrate the predicted data with the conventional technical indicator information to arrive at a predicted technical indicator. The use of both historical data and predicted data results in a technical indicator that can lead more (lag less) than the conventional technical indicator on which it is based.

Intermarket analysis searches for relationships between markets that can be used to obtain useful information about what the prices of the markets will do. An intermarket whose price activity leads that of a market of interest is especially useful for financial forecasting. In the invention, intermarkets can be selected on a market-by-market basis prior to the training of the neural networks. The selected intermarkets can then be used during the training process to provide additional inputs to the neural networks, which improve the accuracy of the predictions. The intermarkets can be screened or selected on the basis of correlation analysis. The correlation can be a statistical calculation that measures the degree and type of relationship between two series of numbers, e.g. a positive correlation indicates the two series move together, a negative correlation indicates they move inversely, and zero correlation indicates the two series vary independently of each other.

Accordingly, the invention features a computer-implemented method of calculating a technical indicator of a market. The method includes the steps of: (a) obtaining historical data relating to the market from a server that includes a database of historical time series data for the market; (b) obtaining predicted future data relating to the market, e.g. using a predictive server that uses intermarket analysis data to train a neural network to predict financial time series data for the market, or other suitable methods such as trend-line analysis, fundamental analysis, and other market-forecasting theories; (c) using both the historical data and the predicted future data to calculate the predictive technical indicator; and (d) outputting the calculated predictive technical indicator. The information processing system can be divided into a predictive server and a predictive trading application. The predictive server can include a market manager component, an historical data manager, a neural network trainer, a trading application builder, an intermarket analysis component, a technical analysis component, a predictive technical analysis component, and a predictive server database. The calculated predictive technical indicator can be communicated to a trading application via a communications network. The trading application can include a watch lists component, a charts component, a grids component, a reports component, an intermarket analysis component, a technical analysis component, a predictive technical analysis component, and a predicative trader database.

In another aspect, the invention features a system for calculating a predictive technical indicator of a market. The system can include: (a) a first server that includes a database of historical time series data for the market; (b) a second server that uses intermarket analysis data to train a neural network to generate predicted financial time series data for the market or other suitable forecasting methods; and (c) a computer in communication with the first and second servers, the computer including computer-usable program code that uses both historical time series data for the market and the predicted financial time series data for the market to calculate the predictive technical indicator. The second server can include a market manager component, an historical data manager, a neural network trainer, a trading application builder, an intermarket analysis component, a technical analysis component, a predictive technical analysis component, and a predictive server database. The system can further include a trading application, and the calculated predictive technical indicator can be communicated to the trading application via a communications network. Alternatively, the system can further include a trading application on a trader’s personal computer which calculates the predictive technical indicator using data communicated to it from a geographically remote (e.g. more than 1 km away) server. In the system, the trading application can include a watch lists component, a charts component, a grids component, a reports component, an intermarket analysis component, a technical analysis component, a predictive technical analysis component, and a predicative trader database.

Also within the invention is a computer program product that can include a computer-usable medium including computer-usable program code that, when executed by a computer, calculates a technical indicator for a market. The computer-usable medium can include: computer-usable program code that uses both historical time series data for the market and predicted financial time series data for the market obtained using a neural network that can be trained using intermarket analysis data to calculate the predictive technical indicator.

Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and devices similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and devices are described below. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. In the case of conflict, the present specification, including definitions will control.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram illustrating an information processing system of the invention.

FIG. 2 is block diagram illustrating the architecture of a predictive server.

FIG. 3 is block diagram illustrating the components of a trading application for use in the invention.

FIG. 4 is flow chart showing a method of generating a predictive technical indicator.

FIG. 5 is a chart displaying the price of gold over three months using daily bars, a 5-day simple moving average, and a 10-day simple moving average.

DETAILED DESCRIPTION

This invention provides methods, systems, and devices for developing predictive technical indicators based on the combination of both historical and predicted data for a market, and can include software running on a back-end server (the predictive server) and a trading application (e.g. an application that runs on a trader’s PC), which operate within an information processing system that includes market exchanges financial data. In a preferred embodiment, the methods, systems, and devices are used in conjunction with the methods and systems described in U.S. patent application Ser. No. 12/632,186 entitled “Intermarket Analysis” filed on Dec. 7, 2009 by inventor Louis B. Mendelsohn. The below described preferred embodiments illustrate adaptation of these methods, systems, and devices. Nonetheless, from the description of these embodiments, other aspects of the invention can be made and/or practiced based on the description provided below.

Various aspects of the invention may be embodied as a system, method, or computer program product (e.g. embodied in one or more computer readable media having computer readable program code embodied thereon), and might be in the form of hardware, software, or a combination of software and hardware. Computer readable media may be a computer readable signal medium (e.g. an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing) or a computer readable storage medium (e.g. an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing).

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc. or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, Smalltalk, C++, and procedural programming languages such as C. The program code may execute entirely on a user’s computer, entirely on the remote computer or server, or partly on a user’s computer and partly on a remote computer or server. A remote computer may communicate with a user’s computer through any type of communications network, e.g. a local area network, a wide area network, or the Internet.

In the figures, blocks of the flowchart illustrations and block diagrams might be implemented by computer program instructions, which may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that the instructions execute to implement the functions/acts specified in the blocks. Computer program instructions may be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to produce an article of manufacture.

In some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations might be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Referring now to FIG. 1. in one aspect, an embodiment of the invention includes an information processing system 10 that features a predictive server 12 and a trading application 14. In the system 10. market data from exchanges such as commodity exchanges 16. stock exchanges 18. and the foreign exchange 20 is obtained from one or more financial data providers 22. The market data is transferred from one of the financial data provider 22 to the predictive server 12. The trading application 14 can get its market data from any of the data providers 22. The predictive server 12 processes the market data using neural networks and intermarket analysis to produce trained neural networks which are then provided to the trading application 14 where the predictive indicators are calculated.

The commodity exchanges 16 can be one or more of any exchanges where the buying and selling of commodities such as grain, cattle, and lumber is performed. Examples of commodity exchanges include the Brazilian Mercantile and Futures Exchange, the CME Group, the Chicago Climate Exchange, the HedgeStreet Exchange, the Intercontinental Exchange, the Kansas City Board of Trade, the Memphis Cotton Exchange, the Mercado a Termino de Buenos Aires, the Minneapolis Grain Exchange, the New York Mercantile Exchange, the U.S. Futures Exchange, Bursa Malaysia, the Central Japan Commodity Exchange, the Dalian Commodity Exchange, the Dubai Mercantile Exchange, the Dubai Gold & Commodities Exchange, the Iranian oil bourse, the Kansai Commodities Exchange, the Mercantile Exchange Nepal Limited, the Multi Commodity Exchange, the National Multi-Commodity Exchange of India Ltd, the National Commodity Exchange Limited, Bhatinda Om & Oil Exchange Ltd. the Karachi, the National Commodity and Derivatives Exchange, the Shanghai Futures Exchange, the Shanghai Singapore Commodity Exchange, the Tokyo Commodity Exchange, the Tokyo Grain Exchange, the Zhengzhou Commodity Exchange, the Commodity Exchange Bratislava, the Climex, the NYSE Liffe, the European Climate Exchange, the London Metal Exchange, the European Energy Exchange, and the Australian Securities Exchange.

The stock exchanges 18 can be one or more of any exchanges where the buying and selling of stocks or other securities occurs. Examples of stock exchanges include the American Stock Exchange (AMEX), the Boston Stock Exchange, the Chicago Stock Exchange, the Cincinnati Stock Exchange, the NASDAQ, the New York Stock Exchange (NYSE), the Pacific Exchange, the Philadelphia Stock Exchange, the Toronto Stock Exchange (TSX), the Alberta Stock Exchange (ASE), the Canadian Venture Exchange (CDNX), the Nasdaq Canada, the Bourse de Montreal, the Jamaica Stock Exchange, the Bolsa Mexicana de Valores (BMV), the Euronext, the Helsinki Stock Exchange HEX, the Paris Stock Exchange, the Frankfurt Stock Exchange, the Italy Stock Exchange, the Amsterdam Stock Exchange, the Oslo Stock Exchange, the Lisbon Stock Exchange, the Warsaw Stock Exchange, the Bucharest Stock Exchange (BVB), the Russia Stock Exchange, the Madrid Stock Exchange, the Stockholm Stock Exchange, the Swiss Stock Exchange, the London Stock Exchange (FTSE), the Tel Aviv Stock Exchange, the Tokyo Stock Exchange (TSE), the Nagoya Stock Exchange, the Nasdaq Japan Market (NJ), the Stock Exchange of Hong Kong (SEHK), the Taiwan Stock Exchange, the Thailand Stock Exchange, the Kuala Lumpur Stock Exchange, the Korea Stock Exchange, the Singapore Stock Exchange, the Buenos Aires Stock Exchange, the Sao Paulo Stock Exchange (BOVESPA), the Rio de Janeiro Stock Exchange, the Brazilian Mercantile and Futures Exchange (BM&F), the Maringa Mercantile and Futures Exchange, the Santiago Stock Exchange, the Australian Stock Exchange (ASX), the New Zealand Stock Exchange (NZSE), and the Johannesburg Stock Exchange.

The foreign exchange (FOREX) 20 is where over-the-counter currency trading takes place. There currently is no central clearing house for over-the-counter currency trading, but rather a network of banks, commercial companies, central banks, hedge funds, investment management firms, retail foreign exchange brokers, non-bank foreign exchange companies, money transfer/remittance companies, and other entities.

The financial data provider 22 can be an organization that obtains and delivers information on markets to interested parties via a variety of data products. Examples of financial data providers include the Commodity Research Bureau, Standard & Poor’s, MTS Reference Data, Exchange Data International, Reuters Datalink, Thomson Financial, Interactive Data Corporation, ICAP, and Bloomberg.

The predictive server 12 can be any device or system capable of obtaining data from the financial data provider 22. using neural networks and intermarket analysis to produce a trained neural network, and transmitting data output from the trained network to the predictive trading application 14. The predictive server 12 can accumulate historical data going back decades for thousands of markets supported by the system. It can then use such historical data to train and retrain the server’s neural networks (e.g. which can contain over 1,000; 2,000; 5,000; 10,000; 20,000; 40,000; 60,000; 80,000; or 100,000 connections) to make a suite of indicators over a variety of time durations.

Referring now to FIG. 2. in one embodiment, the predictive server 12 can include at least four (e.g. 5, 6, 7, or 8) of: a market manager component 30. an historical data manager 32. a neural network trainer 34. a trading application builder 36. an intermarket analysis component 38. a technical analysis component 40. a predictive technical analysis component 42. and predictive server database 44. The market manager component 30 manages the collection of markets supported by the system 10 that are packaged into a variety of products in multiple languages. The historical data manager 32 accumulates (manually or automatically) a variety of financial data for every market in the system. The neural network trainer 34 trains the suite of predictions for each market and tests them against new data, retraining as necessary or desired. The intermarket analysis component 38 searches for and quantifies intermarket relationships that can be used by the neural networks to increase the accuracy of the predictions. The technical analysis component 40 computes technical indicators that are used as inputs to the neural networks to enhance the training process. The predictive technical analysis component 42 integrates the neural network predictions with technical indicator calculations to produce predictive technical indicators for prior periods. Their accuracy can be measured by the predictive server. The predictive server database 44 stores all the market data required to support the system 10. The trading application builder 36 extracts the data necessary for a particular product, version and language of the trading application 14 .

The neural network trainer 34 can include an artificial neural network, which is a system that uses a mathematical technique that models the neurons and synapses of a brain. Artificial neural networks have been effectively applied to pattern recognition and time series forecasting problems where the underlying relationships are poorly understood. The neural network can be organized into an input layer, one or more hidden layers, and an output layer. The neural network must be ‘trained’ on a set of data that includes both inputs and outputs. The training process adjusts the weights of the hidden layer neurons in a guided fashion until the inputs multiplied by the hidden weights is as close as possible to the outputs. Once trained, inputs for which the outputs are not known can be fed into the neural network. It will multiply them by the hidden layer weights and produce a predicted output. See, Neural Networks in Finance: Gaining Predictive Edge in the Market. Paul D. McNelis, Academic Press Advanced Finance, 2005; Neural Networks: A Systematic Introduction. Raul Rojas. Springer, 1996; and Neural Networks and the Financial Markets: Predicting, Combining and Portfolio Optimisation (Perspectives in Neural Computing ), Jimmy Shadbolt and John G. Taylor, Springer, 2002. The use of neural networks described in U.S. Pat. Nos. 5,303,328; 5,444,819; 6,247,001; 6,735,580; and 7,082,420 might be adapted for use in the current invention.

In one embodiment of the invention, the predicted output can be a prediction of a future value in a time series. As long as the data being run through the neural network is similar to the training data, the prediction will be approximately as accurate as the results obtained during training The accuracy of the prediction depends on many factors, the most important of which can be the presence of patterns in the data and relationship between the inputs and the value being predicted. Neural networks can be trained on electronic exchange data as well as technical indicators to predict the future price of a market.

Referring now to FIG. 3. the trading application 14 can include a watch lists component 50. a charts component 52. a grids component 54. a reports component 56. an intermarket analysis component 58. a technical analysis component 60. a predictive technical analysis component 62. and a predictive trader database 64. The watch lists component 50 can present predictive technical indicators to a trader in a grid with one market on each row and one indicator in each column. The charts component 52 can present the predictive technical indicators to a trader in graphical chart format. The grids component 54 can present the predictive technical indicators to a trader in a grid with one time period on each row and one indicator in each column. The reports component 56 can present the predictive technical indicators to a trader in a variety of report formats. The intermarket analysis component 58 can use the intermarket relationships stored in the predictive trader database 64 to generate inputs to the neural networks. The technical analysis component 60 can use financial time series data to calculate technical indicators for use as inputs to the neural networks. The predictive technical analysis component 62 can calculate predictions of future values of the time series data. The predictive trader database 64 can store data used by the trading application 14 .

A method for producing predictive technical indicators is illustrated in FIG. 4. In a first step 70. financial time series data is analyzed. In a second step 72. a series of neural network predictions is designed which represents the financial time series data one or more periods in the future. This step 72 can include one or more (e.g. 1, 2, 3, 4, 5, or more) neural network training sessions which can involve using data from different time periods (e.g. all historical, previous five years, previous 1 year, previous 30 days, previous 10 days, previous 5 days, etc.) as inputs to train and retrain a neural network. Intermarket analysis is then performed using the trained neural network in a third step 74 to enhance the accuracy of the predictions. The technical indicator calculations are analyzed in a fourth step 76. and the neural network predictions are then integrated with the technical indicator calculations in a fifth step 78. Finally, in a sixth step 80. predictive technical indicator algorithms are used to produce predictive technical indicators.

As one example, in the first step Analyze Time Series Data 70. the financial time series data can come in two forms, real-time quotes and historical bars. A quote can be for a bid to buy, an ask to sell, or a trade which represents a buy and sell transaction. Each quote has a price and a quantity. An example of a quote would be a bid to buy 500 shares of XYZ stock at $30 per share. Historical bars summarize quote activity over a period of time such as a minute, hour, or day. Each bar contains four prices and one or two quantities. The four prices are open, high, low, and close. The open price represents the price of the first trade of the instrument during the time period covered by the bar. The high price represents the highest price the instrument traded at during the time period covered by the bar. The low price represents the lowest price the instrument traded at during the time period covered by the bar. The close price represents the price of the last trade of the instrument during the time period covered by the bar. The volume represents the quantity of instruments traded during the time period covered by the bar.

In the second step, Design Predictions 72. as exemplified in Table 1 below, predictions can be defined as daily bars, hourly bars, and minute bars; however, any duration from real-time quotes, such as up to monthly bars, could be used. Within each duration, the four components of a bar are predicted—open price, high price, low price, and close price. In addition, in the case of commodities, the next bar’s volume and open interest could be predicted as well. Finally, the short, medium, and long term price trends are predicted. These could be expressed as moving averages of the price or any technical indicator. The trends could be calculated using any of the price components or a combination of price components, such as typical price which can be the average of high, low, and close.