Big Data Analytics of Financial Strategies

Abstract

This paper first presents an evaluation of profitability of three well-known trading indicators, i.e. The Simple Moving Average (SMA), the Relative Strength Index (RSI) and the Connors Relative Strength Index (CRSI). The evaluation of different trading strategies was based on a financial time-series big data spanning from 2003 to 2013. To overcome the respective weakness and enhance the strength, ensemble approaches combining multiple trading strategies were considered to be more effective. In the literature, the 2-period RSI strategy, commonly known as RSI2, is created by combining SMA and RSI. However, it is known that RSI quite often generates false signals and whipsaws that trigger the unnecessary selling and buying. A whipsaw is when a signal to trade is reversed over a short period of time. False signals increase the probability for losses while whipsaws generate commissions that eat away at profits and test trading stamina. In this paper, an enhanced ensemble trading strategy is proposed. Different from RSI2, the proposed approach uses SMA and CRSI as two base indicators in the ensemble strategy. CRSI helps us identify the trend while simple moving average confirms the trend and indicate the most explosive part, i.e. The highest jump in price. This combination helps minimize acting on sideways movement and instead trading only when the market is showing a profitable movement. Using this in conjunction with a large portfolio set, the experimental results showed that a combination of the Connors RSI and Simple Moving Average resulted in stronger and more appropriate signals and in turn led to generate greater returns. The respective underlying indicators are also tweaked further to create an optimized strategy to maximize profits.