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A Hybrid Financial Trading System

Third Phase:- Financial Trading and Portfolio Management System

Though the profitable speculation depends on accurate price rate forecasting, in reality it is very hard to achieve. Financial trading systems can reduce the reliance on the accuracy of the forecast in improving returns by managing the risk to return ratio. The key to profits is not to anticipate trends, but to follow them [Babcock 1989]. According to Babcock, a mechanical approach is the only way to avoid the destructive emotionalism that permeates trading, which perhaps explain why trading systems can enhance profitability in trading. Transaction costs such as slippage and commissions are an overhead cost that must be added to every trade.

Many of the past research in trading systems that reported amazing success excluded transaction costs. If the costs were taken into account, most of these systems will fail to provide an abnormal return. However, there is work done that reported successful returns with costs taken into account such as Tan [1995ab]. Our research will follow on Tan’s work and will take into account all transaction costs. In the third and final phase of our research, we will develop a set of rules to perform money management, portfolio and risk management, signals the trades (if any) to be done and reports the profit pr loss of the overall system.

The portfolio management module will combine various financial time series that have been identified in Phase 2 of the project as having the potential to return abnormal profits, to see if an optimal risk/return ratio can be found. We intend to optimize the portfolio selection using Genetic Algorithms. We will use the conventional Markowitz’s model to benchmark against the optimized portfolio. The money management module will be designed to optimize the amount of funds to be committed for each trade based on the forecast strength of the second phase, amount of total funds currently available, the maximum amount of drawdown that is allowed, etc.

The trading rules modules will be analyze for optimal return and we may use Genetic Algorithms to select technical indicators for the rules as well as find the optimal parameters for those technical indicators. Technical indicators that we may use include: moving averages, oscillators e.g. momentum and stochastic, directional movement indicators, etc. The simple rules that have been used in Tan’s [1995ab] models consisted of buying or selling a security if the forecast from the models were higher or lower than the current prices by a certain factor. The factors include transaction costs and filter values that were used to eliminate trades that have small amount of forecast price movements.

The last module is the record keeping and profit or loss reporting. We intend to design this model to be as flexible as possible in terms of adding parameters such as variable transactions costs (as this can vary depending on the financial security that is being traded), amount of risk tolerance desired (i.e., aggressive or risk averse), number of successive trading losses to be tolerated, maximum amount of loss per trade, etc. The reporting module will not only report the net amount of profit or loss but identify other benchmark measurements such as best/worst trade, profit/loss per trade, etc. as identified by Refenes [1995].

Prof. Clarence N W Tan

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