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Most attempts to develop neural network forecasting models, whether in a simplistic manner or more elaborately, have focused on individual markets A serious problem with the use of individual markets, however, is the limited number of data points available on which to train the net This situation leads to grand opportunities for curve-fitting (the bad kind)--something that can contribute greatly to the likelihood of failure with a neural network, especially with less than ideal data preprocessing and targets In this chapter, however, neural networks will be trained on a whole portfolio of tradables, resulting in the availability of many tens of thousands of data points (facts), and a reduction in curve-fitting for small to moderatesized networks Perhaps, in this context, a fairly straightforward attempt to have a neural network predict current or near-future market behavior might be successful.



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{ class SocketPolicyConnection { private TcpClient Connection; // Buffer to receive client request private byte[] Buffer; private int Received; // The policy to return private byte[] Policy; //The request string that is expected from the client private static string PolicyRequestString = "<policy-file-request/>"; public SocketPolicyConnection(TcpClient client, byte[] policy) { } // Called when we receive data from the client private void OnReceive(IAsyncResult res) { } // Called after sending the policy and //closes the connection public void OnSend(IAsyncResult ar) { } } } The SocketPolicyConnection class constructor stores a reference to the policy file data and starts receiving the request from the client. public SocketPolicyConnection(TcpClient client, byte[] policy) { Connection = client; Policy = policy; Buffer = new byte[PolicyRequestString.Length]; Received = 0; try { // receive the request from the client Connection.Client.BeginReceive(Buffer, 0, PolicyRequestString.Length, SocketFlags.None, new AsyncCallback(OnReceive), null);

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In essence, such a network could be considered a universal market forecaster, in that, trained across an entire portfolio of tradables, it might be able to predict on all markets, in a non-market-specific fashion FORECASTING WITH NEURAL NETWORKS Neural networks will be developed to predict (1) where the market is in terms of its near-future range and (2) whether tomorrow s open represents a turning point Consider, first, the goal of predicting where the market is relative to its near-future range An attempt will be made to build a network to predict a time-reversed Stochastic, specifically the time-reversed Slow %K This is the usual Stochastic, except that it is computed with time running backward The time-reversed Slow %K reflects where the current close lies with respect to the price range over the next several bars.

sending traffic. Once established, the ATM cells are routed based upon a virtual circuit address. A virtual circuit is simply a connection that gives the user the appearance of being dedicated to that user, when in point of fact the only thing that is actually dedicated is a time slot. This technique is generically known as label-based switching, and is accomplished through the use of routing tables in the ATM switches that designate input ports, output ports, input addresses, output addresses, and QoS parameters required for proper routing and service provisioning. As a result, cells do not contain explicit destination addresses, but rather contain timeslot identifiers. There are two components to every virtual circuit address, as shown in Figure 7-9. The first is the virtual channel (VC), which is a unidirectional conduit for the transmission of cells between two endpoints. For example, if two parties are conducting a videoconference, they will each have a VC for the transmission of outgoing cells that make up the video portion of the conference. The second level of the ATM addressing scheme is called a virtual path (VP). A VP is a bundle of VCs that have the same endpoints and that, when considered together, make up a bidirectional transport facility. The combination of unidirectional channels that we need in our twoway videoconferencing example makes up a VP.

If something could predict this, it would be useful to the trader: Knowing that today s close, and probably tomorrow s open, lies near the bottom of the range of the next several days prices would suggest a good buy point; and knowing that today s close, or tomorrow s open, lies near the top of the range would be useful in deciding to sell Consider, second, the goal of predicting whether tomorrow s open is a top, a bottom, or neither Two neural networks will be trained One will predict whether tomorrow s open represents a bottom turning point, ie,.

The basic services that ATM provides are based on three general characteristics: the nature of the connection between the communicating stations (connection-oriented vs. connectionless), the timing relationship between the sender and the receiver, and the bit rate required to ensure proper levels of service quality. Based on those generic requirements,

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