MrPip, the data I use is freely available from Alpari from this location – http://www.alpari-idc.com/en/dc/databank.php. Download all the M1 data (about 15 months available) for the currency pair you’re interested in and uncompress it to a known location. Go to ToolsHistory Center and locate the symbol you’re working with. Delete all data (M1 to MN) for each period by first double-clicking on the period, highlighting ALL the data in the window and hitting Delete. The reason for this is that the Period Converter Script doesn’t seem to work properly if the fields already contain data. Also work offline when doing this because if any EAs or charts are active these will be updated in the background. Next, still in the History Center, ‘Import’ all the M1….
Distills complex theories for the benefit of the average trader with little or no background in finance or mathematics by offering a wide range of valuable, practical strategies for limiting risk, avoiding catastrophic losses and managing the futures portfolio to maximize profits. Numerous topics are explored including: why most traders lose at the futures game most of the time; why most mechanical trading systems are apt to fail; the probabilistic approach to trading; how to make stop-loss orders work for, rather than against you; the pros and cons of options versus futures trading; and how to limit risk through diversification.
Market risk management under normal conditions traditionally has focussed on the distribution of portfolio value changes resulting from moves in the mid-price. Hence the market risk is really in a “pure” form: risk in an idealized market with no “friction” in obtaining the fair price. However, many markets possess an additional liquidity component that arises from a trader not realizing the mid-price when liquidating her position, but rather the mid-price minus the bid-ask spread. We argue that liquidity risk associated with the uncertainty of the spread, particularly for thinly traded or emerging market securities under adverse market conditions, is an important part of overall risk and is therefore an important component to model.
Divergence, which is a term that technicians use when two or more averages or indices fail to show confirming trends, is one of the mainstays of technical analysis. Here’s a new way to use oscillators and divergence as well as methods to locate entry levels during a trend.
We examine the effects of trading after hours on the amount and timing of price discovery over the 24-hour day. A high volume of liquidity trade facilitates price discovery. Thus prices are more efficient and more information is revealed per hour during the trading day than after hours. However, the low trading volume after hours generates significant, albeit inefficient, price discovery. Individual trades contain more information after hours than during the day. Because information asymmetry declines over the day, price changes are larger, reflect more private information, and are less noisy before the open than after the close.
This book describes the trading strategies used by a professional stock trader in his own trading. The ideas come both from friends who are successful traders as well as his own experience with SOES trading. The collection of trading patterns described represents one of the first full-fledged books of instruction on short term, swing and day trading in individual stocks. The author’s intraday trend trading approach and his scalping method are both described in detail. He uses the setups daily in his own trading. This manual should prove valuable to the thousands of short term stock traders who seek to make their living from speculating on short term price swings. It is a toolbox for finding high probability trades for success as you trade the stock market. The technical ideas are primarily crafted around the personality of the NASDAQ market but may also be implemented in New York trades.
Five stock price sequences are examined quantitatively for structure as predicated by:
1) a random walk model;
2) a continuously differentiable price process;
3) a dynamic model consisting of transients of 8. discrete process.
The first and third models also make predictions in agreement with trading lore. The date are examined by the method of coincident events. Positive evidence is found for both the random Walk and discrete transient model, and slightly against the continuous price process. The theoretical predictions seems better confirmed by data at price minima than price maxima . The data are in partial disagreement with the predictions of both the random Walk and discrete transient model that large Volume and large second differences of price should tend to occur at the same time. Some confirmation is found for items of trading lore not predicted by theory. The non-random properties of stock prices are primarily found in short interval (daily and weekly) and in individual stock prices as opposed to an average.
The term “market-neutral investing” refers to the use of a group of investment strategies intended to neutralize certain market risks by taking offsetting long and short positionsin related instruments.At fi rst glance, these strategies seem to be quite different. But all market-neutral strategies derive returns from the relationship between long and short elements of the portfolio, whether that relationship occurs within the portfolio or within the instruments themselves. These strategies look for investments that are not correlated. Correlatedinvestments offer similar returns under similar market conditions. Market-neutral strategies look for pairs of investments that behave differently under a given set of market conditions.
Speculative trading stems from disagreements among traders. Besides the approaches based on the existence of private information (and noise traders) or the dierences of opinions, Harrison and Kreps(1978) and Morris(1996) relied on the presence of diverse beliefs to explain speculative phenomena. This paper proposes a new model of speculative trading by introducing rational beliefs of Kurz(1994) and Kurz and Wu(1996). Agents hold diverse beliefs which are rational” in the sense of being compatible with observed data. In a non-stationary environment the agents may learn only about the stationary measure of observed data. Agents’ beliefs can be non-stationary and diverse even when their stationary measures become the same as that of the data with complete learning.In a Markovian framework of dividends and beliefs, we obtainanalytical results on how the speculative premium depends on the extent of heterogeneity of beliefs. In addition, we demonstrate the possible emergence of endogenous uncertainty (as dened by Kurz and Wu(1996)) and the persistent presence of diverse beliefs and positive speculative premiums.