Unlock the secrets of the Falling Wedge (Bearish) Pattern in our comprehensive guide. Discover how this pivotal pattern forms, its implications in directionless markets, and effective trading strategies. Delve into the psychological aspects of pattern trading, from cognitive recognition to emotional management, and learn how to master risk assessment in bearish market trends. Continue reading...
Unlock the secrets of the Ascending Triangle (Bullish) Pattern in trading. Discover how to identify, interpret, and capitalize on this powerful pattern for maximum profit. Gain expert insights into the psychology behind pattern trading and leverage advanced AI tools to refine your trading strategy. Continue reading...
Trading models are emotionless systems for decision-making in trading that can be automated or just used for reference. They tend to have logical parameters, such as “if x, then y” which can use popular trading indicators to implement a strategy that might only be used in certain conditions. Trading models are strategies employed with a specific design. Different trading models will use different technical indicators or types of charts to define and search for certain conditions in which a strategy can be used. Once the conditions are met, the model provides the decision-making logic that is intended to carry out a profitable trade without guesswork or emotion. Continue reading...
Dollar cost averaging (DCA) is a method of hedging against the risk of investing a lump sum at high market prices. With DCA, the investor deploys money at set intervals, hoping to get the best average price per share. If you use the same amount of money to buy shares at set intervals, you will acquire more shares when the market is down, and fewer shares when the market is up, so theoretically you would have acquired more of the advantageously-priced shares overall and will be in a better position in the long run. Continue reading...
The Random Walk Hypothesis states that in an efficient market, prices will correlate around the intrinsic value of securities, but there will always be a randomization and unpredictability to it. The Random Walk Hypothesis suggests that technical analysis and the efforts of chartists cannot beat the market over time, because the market will move randomly and unpredictably, and past results cannot predict future returns. Continue reading...
The analysis of convergence and divergence between indexes and other data seeks to find leading indicators where there is confirmation or non-confirmation of trends. Dow Theory was one of the first examples of such thinking. Charles Dow would watch the movements of Industrials and the Rail and compare the uptrend or downtrend of each. Where trends do not line up (e.g., one is trending downward with lower troughs and the other has “higher lows”) there is “divergence”, and non-confirmation of what was thought to be a trend in one index. Continue reading...
Intraday trading means opening and closing a position, or buying and selling (or short-selling and covering) a security within the same trading day. Intraday traders are active during market hours, buying, selling, shorting, and so forth, to capitalize on the movements of the markets during the day, and they primarily trade positions which are opened and closed during the same day. Intraday traders use technical indicators to find inefficiencies or price fluctuations that they believe will correct. Continue reading...
Accommodation Trading is when two traders enter into a non-competitive trade agreement which disregards the current market price for the securities being traded. The primary reason to engage in accommodation trading is for an investor to avoid taxes by harvesting more losses than actually occurred. One investor will buy shares from another investor for a price significantly below the market value so that the selling investor can report more losses. The partners will typically agree to allow the selling party to buy the shares back later at the same price. Continue reading...
Active management is the practice of attempting to outperform the market with selection and timing. Active management is a thoughtful and time-consuming approach to investing and is the opposite of Passive management. Active managers seek to outperform the benchmarks for their portfolio by researching and selecting stocks and other assets based on strategies and analysis methods thought to be superior. Continue reading...
Active trading is the pursuit of returns in excess of market benchmarks. Investors are advised to have a diverse portfolio, to hedge against the risk of seeing future financial plans devastated due to significant losses in one holding. When attempting to diversify, investors will hear from the increasingly popular camp which believes that the best strategy is to use only passive index funds, which follow indexes using computer algorithms and have low expense ratios. Continue reading...
The Adaptive Market Hypothesis uses theories of behavioral economics to update the aging Efficient Market Hypothesis. There have been many debates surrounding the Efficient Market Hypothesis and its validity, and a lot of research over the last 15 years or so has been done which suggests that behavioral finance holds many of the keys to an accurate “universal theory” of the markets. A marriage between the two schools of thought has given birth to the Adaptive Market Hypothesis, coined in 2004 by Andrew Lo of MIT. Behavioral and evolutionary principals come into play when theorizing about the large-scale behavior and adaptation of humans in a system. Continue reading...
Adaptive Price Zone is a volatility-based trading indicator. Similar to traditional Bollinger Bands, Adaptive Price Zone is a recent development by Lee Leibfarth that overlays two indicator bands around a moving average line. It is more adaptive than many previous band indicators, using several short-term exponential moving averages which are double-smoothed and closely hug changes in volatility and price data. Exponential moving averages give more weight to recent data, which helps the lines hug current data. Continue reading...
Adaptive selling is a sales and marketing principal where the product or services offered are framed or actually modified based on the preferences or demographics of the audience or client. Adaptive selling requires the ability to customize a shopper’s experience as they interface with the real or virtual storefront. The sales system leaves room to learn about the customer and to adopt the language and products offered based on changing interpretations of the customer. This may require a well-trained sales representative or a well-designed computer algorithm, as has been implemented on some e-commerce sites. Continue reading...
“Adding to a loser” describes continuing investment in a stock or fund that has continued to decline. Continuing to invest when it is going down in value can be a solid play up to a point. If you remain bullish on the company or fund, you may be getting a great deal on the shares that you purchase. When the price rebounds, you will have full participation in the upside with more shares than you would have otherwise. Continue reading...
Activist investors buy enough voting shares to influence the decisions of a company, sometimes for political or moral reasons, sometimes for purely financial reasons. Activist investors can act alone or in groups, but their goal is to acquire enough shares of a company’s equity to influence the company’s decisions. Activist shareholders may need as little as 10% of shares to sway corporate governance. Continue reading...
Analytical financial theories and trading strategies can be “backtested” by applying them to historical data. Backtesting is to simulate what it would have been like to use a certain strategy or indicator in the past. Because markets are more complicated than a simple algorithm, such as an assumed future rate of return, it is preferable and somewhat more dramatic to use actual historical data for testing. There is an abundance of historical market data available to those who would like to use it for backtesting a theory, strategy, or indicator. Continue reading...
Financial traders use correlation to describe the movement of securities – how and when they move – relative to each other during a given time period. These relationships lend themselves well to pairs trading, where traders have developed an understanding of correlations and their behavior that allow them to confidently exploit slight changes to minimize risk and maximize profitable transactions. Continue reading...
Covariance is a measure of what degree the returns on two assets move in tandem. A positive covariance implies that returns on two assets move together in the same direction, while the opposite is true with negative covariance. In a diversified portfolio, an investor ideally owns securities with negative covariance, so that returns may be smoothed out over time. Continue reading...
A coefficient of Variation is a statistical measure of expected return relative to the amount of risk assumed. It’s also known as “relative standard deviation,” which makes sense since that implies that your expected risk is adjusted based on the expected return. You can easily calculate the Coefficient of Variation by dividing the standard deviation of the security by its expected return. Continue reading...
Day traders, by definition, trade on a very short-term time frame, seeking to generate profits by opening and closing positions hour-by-hour and having the majority of their positions closed by the end of the day. Short-term profits and income are the goals with most day-traders, and the term is used more and more for “amateur” traders who trade from home and treat it as their primary occupation without being part of a brokerage firm. Day trading has become more and more prevalent for independent, non-affiliated investors who trade from their computers at home for hours a day. Continue reading...
Fourier Analysis is a mathematical method of identifying and describing harmonic patterns in complex oscillating environments, and is used in options pricing among other things. Fourier Analysis is used to compute the probability that results will be within a certain range. Fourier analysis also has many other applications in physics, engineering, and music, for instance, because it can create a system for identifying patterns and simplifying computations for complex systems which feature oscillations and waves which have frequencies. Continue reading...
Momentum trading usually involves long positions in a security that has been experiencing an uptrend and has a high volume of trading, and dropping positions that have lost momentum. Several systems exist to help take the emotion out of trading and to stick to a theory with rules. Momentum trading is such a system, and it can be automated with help from algorithm. Some indicators that can be used are Rate of Change and Relative Strength Index. Some would identify high momentum as steady price increases bolstered by high trading volume. Continue reading...
A theory about what will happen and why is a hypothesis, and to prove the hypothesis has some relevancy it will have to be compared to the probability of getting those results by pure chance. A hypothesis is a testable prediction of results that should be observed due to the effects of an independent variable. Such predictions must be tested against the probability of the resulting observations happening due to complete chance instead of the influence of the independent variable. Continue reading...
Standard Deviation is a measurement of how far from the average (mean) the majority of a data set lies. Standard Deviation is a measure of variability, and it is on a different scale for each data set being measured; there is no “standard” standard deviation. It is possible to normalize it for comparison to other data sets using measurements like r-squared and the sharpe ratio. The number arrived at when computing standard deviation is going to reveal the distance, in terms of one of the quantifiable variables being observed, from the average, in either a positive or negative direction, within which 68% of the data set falls. Continue reading...
In statistics, the number of times that a specific value shows up in a data set is the absolute frequency of that value. The absolute frequency can then be used to find the relative frequency, which is the probability that the specific value is observed in a given number of trials. The relative frequency (empirical probability) takes the absolute frequency and divides it by the total number of trials (cumulative frequency), and can be expressed as a ratio or percentage. Continue reading...