Basic forex strategies - page 3

This paper will provide more information of complexity science and how it can be used in finance. The following topics are covered in this paper: (a) complexity science, (b) agents, (c) automata, (d) the optimization problem, and (e) Strategy filtering. Within the optimization problem a section on trading strategies and simulation methods will be put forth. In the strategy filtering section a review of agent based models will be discussed to pick a maximizing solution. The goal of this paper is to introduce complexity science and system thinking to financial trading and economic problems.

We carry out a large-scale investigation of technical trading rules in the foreign exchange market, using daily data over 45 years for 30 developed and emerging market currencies. Employing a stepwise test to counter data-snooping bias and examining over 21,000 technical rules, we find evidence of substantial predictability and excess profitability in both developed and emerging currencies, measured against a variety of performance metrics. We cross-validate our results using out-of-sample analysis. We find time-series and cross-sectional variation in sub-periods and cultural and/or geographic groups, respectively, suggesting that temporarily not-fully-rational behavior and market immaturity generate technical predictability and potential excess profitability.

In this paper, we introduce a trading strategy assuming price action to be a martingale. Borrowing the philosophy from short- and long-run reversal strategy, we quantify specific entry points for traders to act on. Such strategy allows traders to allocate risk on benchmark in a reasonable manner.

There have been wide applications of trend following based trading system in the real world. The traditional methods include 5 days moving average, 20 days moving average, 200 days moving average and so on. If the midterm market trend is the biggest concern, 20 days and 50 days moving averages can provide efficient forecast information about stock trend. However, the solo stock price moving average does not provide sentimental information of the market in a timely manner. This paper discusses a mathematical formulation of stock trend decision and the related prediction delay problem. An efficient method which utilizes the market sentiment Index has been proposed, aiming to forecast short term and long term market tops and bottoms. This new method leads to specific trading rules on two major indexes SPY and QQQQ. The experimental results show very accurate position estimation and support that our system implements much more reliable trading rules than the bench mark buy and hold strategy in terms of market exposure and account balance.

We study the optimal dynamic trading strategy between a riskless asset and a risky asset with momentum (momentum asset). The most salient characteristic of momentum is that positive price shocks predict positive future returns. This characteristic leads to big swings in returns over multiple periods. Investors with relative risk aversion greater than one dislike such big swings. We show that it is optimal for such investors to reverse momentum by holding less or even shorting the momentum asset. We find that the optimal portfolio weight also depends on the historical price path, in addition to momentum. Different historical price paths, even if they have the same momentum, lead to different optimal portfolio weights. In particular, with rebound path (a historical price path that decreases at the beginning and then rebounds later to have a positive momentum), it is optimal for investors to hold less or may short the momentum asset and hence suffer less or even benefit from momentum crashes.

In the context of the supervised learning problem for time series forecasting, we focus on financial time series and use the currency pair EURUSD to highlight issues that arise when daily data are utilized for one-day forecasts of currency exchange rate moves. In light of our results for forecast horizons of one day or more, we take a closer look at the EURUSD time series data to get a better understanding of typical intraday moves and their magnitude and how their potential can be harnessed for the development of consistently profitable trading strategies. By combining the results of our own numerical studies with published findings from the literature and illuminating them from a practical perspective, we motivate a simple intraday trading strategy for EURUSD that avoids some of the problems associated with longer-term forecasts.

techmac:
Anybody has the : Exponentially Decaying Weighted Moving Average?

seekers_:
What is going on with PDF uploads?