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Basic forex strategies - page 7

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The London Stock Exchange (LSE) has been an institution caught in the midst of political, economic and technological change. Systemic changes sweeping through the industry have set off chain reactions of demutualization, consolidation and diversification. The LSE has not been immune to these changes, yet, it still stands more or less alone.

Adaptation, for a mutual association with such a long history of independence and such hidebound attitudes, was not easy. However, as compromise became a necessity, and later, unavoidable exposure to global change became a reality, the LSE found its feet in the new world order. As ambitions of creating truly global exchanges grew, suitors also became more savvy about how they approached the notoriously “hard to get” LSE.

This paper explores the factors dictating the responses of the LSE to the pressures of change. It is a cautionary tale, highlighting, among other things the importance of institutional culture and personalities in the exchange business. As large and significant financially as exchanges may be, they are small, potentially closed worlds in other respects. In the case of the LSE, the imperial past has also played a part. Rather than looking outwards to engage internationally, as NASDAQ and the NYSE were obliged to do, the City of London instead brought the world (in the form of diversity of the markets, expertise and human capital) to London. The most recent of a long string of merger negotiations, that of the LSE and Deutsche Bӧrse, will test this strategy. Will the commercial and institutional forces supporting the merger be strong enough to overcome the political and regulatory uncertainty produced by Brexit?

This paper appears as a chapter in a longer study, funded by the Centre for International Finance and Regulation (Sydney, Australia), which chronicles the major exchange consolidations of the last decade, successful and unsuccessful.
lynntee:

Hi Guys, 


For basic Trading most widely uses

1) trend trading

2) Support resistance breakout 

 - Pennant . flags recognition  

3) fibonaci entrancement 

4) candle Sticks recognition

5) Moving averages cross ( EMA/ Maverage )

4) Strong understanding of divserse environment in trading fundamental 

 

Personally this is my MUST have Indicators ( I am a very EA and Indicator Person) sorry Naked Chart ain't my style 

1) Average Day Range 

2) Currency Meter 

3) Currency Scanner 

4) Trader Dynamic Index 

5) Bull and Bear signals 

6) Flags and Pennants 

7) Education Modules and Indicators Manuals 

8) Candles Recognition  

1) Where is the strategy? These are some more or less bad indicators and attempts to use them.

2) Breakouts are good when you know that it is one. Traders often jump into bears or bulls traps and wonder why "the market is trolling" them.

3) IMO your 'MUST have' is a collection of "hands off" stuff, except the first of (7), but what you mean?

This is neither for beginners, advanceds, nor for masters.

Electronic trading has transformed foreign exchange markets over the past decade, and the pace of innovation only accelerates. This formerly opaque market is now fairly transparent and transaction costs are only a fraction of their former level. Entirely new agents have joined the fray, including retail and high-frequency traders, while foreign exchange trading volumes have tripled. Market concentration among dealers has risen reflecting the heavy investments in technology. Undeterred, some new non-bank market participants have begun to make markets, challenging the traditional foreign exchange dealers on their own turf. This paper outlines the players in this market and the structure of their interactions. It also presents new evidence on how that structure has changed over the past two decades. Throughout, it highlights issues relevant to exchange rate modelling.
We investigate the profitability of the quantitative market timing technique of candlestick technical analysis in the U.S. equity market. Despite being used for centuries in Japan and now having a wide following amongst market practitioners globally, there is little research documenting its profitability or otherwise. We find that these strategies are not generally profitable when applied to large U.S. stocks. Basing trading decisions solely on these techniques does not seem sensible but we cannot rule out the possibility that they compliment some other market timing techniques.
seekers_:
they're bashing candlesticks cause they wanna sell their products unlike price action guys which offering they strategies for free !
In this paper, we analyze the usefulness of technical analysis, specifically the widely used moving average trading rule, from an asset allocation perspective. We show that when stock returns are predictable, technical analysis adds value to commonly used allocation rules that invest fixed proportions of wealth in stocks. When there is uncertainty about predictability, the fixed allocation rules combined with technical analysis can outperform the prior-dependent optimal learning rule when the prior is not too informative. Moreover, the technical trading rules are robust to model specification, and they tend to substantially outperform the model-based optimal trading strategies when there is uncertainty about the model governing the stock price.
We find that individual investors who use technical analysis and trade options frequently make poor portfolio decisions, resulting in dramatically lower returns than other investors. The data on which this claim is based consists of transaction records and matched survey responses of a sample of Dutch discount brokerage clients for the period 2000-2006. Overall, our results indicate that individual investors who report using technical analysis are disproportionately prone to have speculation on short-term stock-market developments as their primary investment objective, hold more concentrated portfolios which they turn over at a higher rate, are less inclined to bet on reversals, choose risk exposures featuring a higher ratio of nonsystematic risk to total risk, engage in more options trading, and earn lower returns.
Using a dynamical microeconomic model which generalizes the classical theory of adjustment to include finite asset base and trend-based investment preference, we develop a foundation for the technical analyis (or charting) of securities. The mathematically complete system of (deterministic) ordinary differential equations that has provided a quantitative explanation of the laboratory bubbles experiments generates a broad spectrum of patterns that are used by practitioners of technical analysis. The origins of many of these patterns are classified as (i) those that can be generated by the activities of a single group, and (ii) those that can be generated by the presence of two or more groups with asymmetric information. Examples of (i) include the head and shoulders, double tops, rising wedge while (ii) includes pennants and symmetric triangles. The system of differential equations is easily generalized to stochastic ODE'S. Application is also made to Japanese candlestick analysis.
This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators — the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005) — into momentum strategy. Empirical results show that the combined investment strategy includes stocks with larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.
Managed-futures funds and CTAs trade predominantly on trends. There are several ways of identifying trends, either using heuristics or statistical measures often called “filters.” Two important statistical measures of price trends are time series momentum and moving average crossovers. We show both empirically and theoretically that these trend indicators are closely connected. In fact, they are equivalent representations in their most general forms, and they also capture many other types of filters such as the HP filter, the Kalman filter, and all other linear filters. Further, we show how these filters can be represented through “trend signature plots” showing their dependence on past prices and returns by horizon. Our results unify and broaden a range of trend-following strategies, and we discuss the implications for investors.
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