A Directional Change Based Trading Strategy with Dynamic Thresholds

Traders always seek a trading strategy that can increase and maximize their profits. However, given the current challenges in financial time-series streams – data elements (tick prices) arrive in real-time or almost real-time and at high velocity (at finer time scales) – it is difficult to identify and spot the best time and the most profitable price for trading. The Directional Change (DC) is an event-based approach for summarizing price movements based on a fixed given threshold value. An event in the DC approach is detected if the price change between two points satisfies the given threshold value. In this research, we aim to present a trading strategy based on the DC approach and a dynamic threshold to replace the fixed given one. We call this strategy, the Dynamic Threshold Trading Strategy (DT-TS). Thus, once a DC event is detected (a price change is identified) using the defined dynamic threshold, a trading action is triggered as prices continue to increase or decrease depending on the detected DC event. The trading action to be taken (buy or sell) depends on the previous day price transitions. An experiment was conducted on the FTSE 100 minute-by-minute prices stream to evaluate the DT-TS against different fixed threshold values and different trading strategies. Results showed that the DT-TS was the most profitable strategy among different fixed thresholds and all other examined trading strategies

The price dynamics of common trading strategies

A deterministic trading strategy can be regarded as a signal processing element that uses external information and past prices as inputs and incorporates them into future prices. This paper uses a market maker based method of price formation to study the price dynamics induced by several commonly used financial trading strategies, showing how they amplify noise, induce structure in prices, and cause phenomena such as excess and clustered volatility.Comment: 29 pages, 12 figure

The Nature of Alpha

We suggest an empirical model of investment strategy returns which elucidates the importance of non-Gaussian features, such as time-varying volatility, asymmetry and fat tails, in explaining the level of expected returns. Estimating the model on the (former) Lehman Brothers Hedge Fund Index data, we demonstrate that the volatility compensation is a significant component of the expected returns for most strategy styles, suggesting that many of these strategies should be thought of as being `short vol'. We present some fundamental and technical reasons why this should indeed be the case, and suggest explanation for exception cases exhibiting `long vol' characteristics. We conclude by drawing some lessons for hedge fund portfolio construction.Comment: 22 pages, 5 figures, 3 table

TSFDC: A Trading strategy based on forecasting directional change

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market’s trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. We argue that TSFDC outperforms another DC-based trading strategy

Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

One of the most critical issues that developers face in developing automatic systems for electronic markets is that of endowing the agents with appropriate trading strategies. In this article, we examine the problem in the foreign exchange (FX) market, and we use an agent‐based market simulation to examine which trading strategies lead to market states in which the stylized facts (statistical properties) of the simulation match those of the FX market transactions data. Our goal is to explore the emergence of the stylized facts, when the simulated market is populated with agents using different strategies: a variation of the zero intelligence with a constraint strategy, the zero‐intelligence directional‐change event strategy, and a genetic programming‐based strategy. A series of experiments were conducted, and the results were compared with those of a high‐frequency FX transaction data set. Our results show that the zero‐intelligence directional‐change event agents best reproduce and explain the properties observed in the FX market transactions data. Our study suggests that the observed stylized facts could be the result of introducing a threshold that triggers the agents to respond to periodic patterns in the price time series. The results can be used to develop decision support systems for the FX market