THE BEST TRENDLINE METHODS OF ALAN ANDREWS EPUB DOWNLOAD

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The Best Trendline Methods of Alan Andrews and Five New Trendline Techniques. 96 Pages·· MB· Downloads. Andrews used to make a fortune in. Ebook The Best Trendline Methods of Alan Andrews and Five New Trendline Techniques pdf by Patrick Mikula download, download online book The Best. The rare trendline methods originally taught by Alan Andrews have been collected and Download and Read Online The Best Trendline Methods of Alan.


The Best Trendline Methods Of Alan Andrews Epub Download

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The rare trendline methods originally taught by Alan Andrews have been collected and . Download and Read Online The Best Trendline Methods of Alan . The Best Trendline Methods of Alan Andrews and Five New Trendline Techniques - site edition by Patrick Mikula. Download it once and read it on your. Alan Andrews Course 1 - Download as PDF File .pdf), Text File .txt) or read online. we have been informed are better ways of using other well known methods, . Imaginary trend line used by Morton's Rule found only in this Course Moving.

Secrets of the Millionaire Mind is two books in one. Part I explains how your money blueprint works. Through Eker's rare combination of street smarts, humor, and heart, you will learn how your childhood influences have shaped your financial destiny.

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We strictly limit our optimization to the earliest data segment in order to avoid over curve-fitting. The earliest data segment we use to find our best specific parameter is known as seen data or in-sample data , while later and more recent data segments not yet used are known as unseen data or out-of-sample data.

We hold back the more recent unseen data segments for walk-forward simulation Step 6. Rather, only performance data based on unseen data can be counted in our walk-forward simulation performance evaluation in Step 9. Walk Forward. This newly discovered, optimal indicator parameter from Step 5 is projected forward in time onto the next and more recent segment of out-of-sample, unseen data that was not included in the brute-force optimization search.

We record the performance data for this simulation for evaluation in Step 9. We add the data segment we just used for our walk-forward simulation Step 6 to our previously seen database from Step 5. After each walkforward step, our seen database grows and our unseen database shrinks.

Repeat the cyclical pattern established in Steps 5, 6, and 7 until we use all unseen data. We start the Step 5 optimization process again, with the just-seen data from Step 6 now included in a larger seen database we use for optimization in Step 5.

After rerunning the Step 5 optimization, we use the resulting newly found best parameter to walk forward Step 6 on the next segment of unseen data. Then we add back that just-seen data just used 12 Evaluating Technical Market Indicators in Step 6 to the seen optimization database from Step 5 , which grows larger with every repetition.

We repeat this loop, performing Steps 5, 6, and 7 over and over again until we walk forward over all unseen data, bringing us to the current moment in time. This may seem like work, but it is extremely quick and easy compared to judging an indicator in real-time, one day at a time.

Evaluate Results. If we have acquired enough clean accurate data, and if we have broken that data into enough segments, we have gained a realistic perspective on how our indicator would have evolved and performed through many iterations over the years.

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We have seen whether or not our indicator would have been sound and consistent through past time. We can now compare the simulated results of a variety of indicators so that we can select the best indicators for real-time application. We have an objective basis to accept or reject our indicator hypothesis.

If our simulated test results on unseen data are acceptable, we run a final optimization on all available data to establish an indicator parameter to use going forward in real-time. We may retain in our seen database all of our previously seen data, no matter how old it is.

Human beings make markets, and basic human nature does not change much over the years. So, old data may not be obsolete, and old historical patterns created by crowd psychology may reappear. Alternately, we could allow for faster evolution of our decision rule over time by systematically deleting some predefined quantity of the very oldest seen data as an equal quantity of new unseen data is added to the optimization database.

Systems Thinking, : Managing Chaos and Complexity: A Platform for Designing Business Architecture

In other words, we could use a moving time window of predefined length to determine our specific parameters. If there is a good reason to believe that the basic nature of a market may have changed over time, we would be justified in systematically deleting the oldest time segments of historical data.

This simulation process imposes the rigor of the scientific method on technical analysis theories. If our indicator developed on seen data produces consistent and relatively strong performance when projected forward onto future periods using unseen data, then we have a reasonable justification to use this indicator going forward into the unseen future immediately ahead.

Obviously, it is far better to find out if our indicator is effective or inadequate in simulation, rather than finding out in real time with real money.

As Thomas Edison pointed out, a rejected hypothesis is very useful information that allows us to turn our resources to other approaches that may produce better results.

The process of doing the research often rewards us with more realistic insights into market behavior and inspires new ideas. We choose exponential moving averages crossovers for this demonstration because of their simplicity and attractive theoretical and practical advantages. We prefer exponential smoothing as a moving average method because it is more responsive to newer data and less dependent on older data than simple moving averages.

Also, exponential smoothing is less sensitive to newer data and less dependent on older data than weighted moving averages. Compared to other methods, exponential smoothing is more stable.

This strategy downloads long and covers short when price crosses above its own trailing exponential moving average, and then it sells long and sells short when price crosses under its own trailing exponential moving average. This strategy is always in the market, either long or short. The clearly and precisely defined download and sell signals leave absolutely no room for uncertainty, subjective judgment, or interpretation, which can be sources of problems.

Moving average smoothing is the basis of many trend-following approaches and systems, and our unqualified crossover rule is its simplest form. Avoid hindsight bias.

Best Trendline Methods

We go both long and short because we must be very careful not to inadvertently introduce a bullish bias, which is a common but incorrect unspoken assumption of those who choose a long or cash strategy without selling short. It is only obvious in hindsight that the stock market has had a strong bullish upward bias over the past century, but that information was not at all available a century ago. So, a long and short strategy is a good test, since any indicator that survives equal-opportunity short selling and still performs strongly has to be good.

The only parameter that can be varied is the period length, n.

With such a simple, unbiased approach, there is only one parameter variable to optimize: the number of time periods in this case, the number of weeks used to estimate our 14 Evaluating Technical Market Indicators exponential smoothing constant. A large n means a slow, insensitive, and inactive indicator that generates few trading signals.

A small n means a fast, sensitive, and active indicator that generates many trading signals. Transaction costs, dividends, margin, and interest can vary substantially and complicate the analysis. These costs are not included in this example, solely in the interest of simplicity of presentation, and not at all because they are insignificant in reality. On the contrary, these deserve careful consideration when choosing an investment strategy. The more frequent the trading signals, the greater the transaction costs, which not only include commissions but also slippage—the price you actually receive on an order compared to the price you hoped to receive.

Slippage can be highly variable, and it can exceed the bid-ask spread, especially in fast markets. Slippage is usually a negative number, and therefore a cost. Because margin and leverage can greatly magnify profits and losses, they are another major consideration. This data also is available from other sources.

To simplify our work for this example, we sample end-of-week data only. We carefully examine data to insure its accuracy, by visual inspection of the chart looking for outliers odd excursions and by systematic spot checks. We correct any data errors. Segment the Data. We choose to segment the data into 1-year time intervals, from January 1 to December 31 each year.

Alan Andrews Course 1

Therefore, we will walk forward by one year at a time, and our seen database will grow in size by 52 Friday closing prices each year, while our unseen database available for future walk-forward simulation will shrink by 52 Friday closing prices each year.

We select 16 years of weekly closing prices from January 1, to December 31, for our initial earliest data segment. This initial segment must include a minimum of 30 trades and cover an integer multiple of a full low frequency cycle for example, the well-known 4-year cycle in order to eliminate a download or sell bias.

Two 4-year cycles are eight years, and two 8-year cycles are 16 years. See Cycles. We conduct a brute-force optimization on our initial segment of data the 16 years of weekly price closes , systematically trying all Exponential Moving Average period lengths from 1 week to 50 weeks. All this initial data is considered to be seen data, so it will not be counted in our walk-forward, blind simulation performance evaluation in Step 9.

We apply the best parameter from our optimization in Step 5 to unseen data for the next year-ahead data segment. We carefully record the results of this walk-forward, real-time simulation for evaluation in Step 9. We add that just-simulated data from Step 6 to the previously optimized, seen database from Step 5 , so our original 16 years of weekly price closes is now 17 years.

We retain all the old seen data in our optimization database, never deleting any seen data from our ever-growing optimization database, even as we add new, just-seen data each year. The length in years of our optimization database will grow from 16 years, to 17 years, to 18, to 19, 20, 21, 22, 23, 24, 25, We repeat Steps 5, 6, and 7, over and over again, one year at a time, until all unseen data is used in our walk-forward simulation.

We evaluate the cumulative results of our walk-forward simulation performed on unseen data only. This will provide us with a realistic perspective on how our method would have evolved and performed in real-time through many iterations over the years. We carefully record the profits and losses of each walk-forward simulation on unseen data each year so that we can build a realistic, simulated cumulative track record of the performance results of our indicator over time. At this point, we have selected our hypothesis, acquired our data, thoroughly checked our data, segmented our data, and are now ready for our next step, Step 5, Optimize.

We systematically try every exponential moving average period length from 1 to 50 against the weekly closing prices for the first 16 years of the past century. We find that all the period lengths tested would have been profitable. The maximum profit would have been recorded by the 4-week exponential moving average crossover.

We measure the simulated performance of this 4-week exponential moving average crossover strategy over the next 52 weeks of unseen data, from January 1, , through December 31, We record the result for future evaluation in Step 9. Elliot's Rule Fan lines or radiating ribs as in the Courses exclusive "Horn of Plenty" study First notice day, the day holders of positions are notified of delivery due under each terminating futures contract Denoted by G as in GU meaning gap up, or GD means gap down.

Shown on bar charts when no price on today's range is opposite any of Yesterday's range. Technically and empirically the price at one extreme of a days range may be opposite the extreme of the next day's range, and still have the properties of a true gap.

Hagopian's rule found only in this Course To take insurance of some kind against loss. Imaginary trend line used by Morton's Rule found only in this Course Moving average Market if touched means that your order is to be executed at market when a specified price has been reached even if only the prior orders to yours were executed at the specified price.

Used to signal change in trend when price touches or pass past these lines, under specified conditions. Multi-pivot lines, lines that pass through 3 or more pivots. Pivot, a turning point. It is the extreme on a bar chart where a change in trend takes place.

Knowing where Ps will occur is key to profit from fluctuations. A line from the Peak of a price swing to the Low. L-P from Low to Peak.

Reaction line, the equidistant line from a CL that the Action A line is opposite. Probability of pivot at or near WLs. Slopes of alternate MLs of comparable length indicate the trend. When both recent MLs slope in the same direction the trend is strong and price change rapid. There is a high probability that: 1. Seite 5 von 47 3. Prices reverse at any ML or extension of a prior ML. Frequently, after crossing a lower MLH, prices continue to rise along the MLH before the further drop that was signaled by passing through.

So here you can use a sliding parallel through the bottom of the range of the most recent day as a sell signal if prices drop through that SH. MLH are places beyond which each day you place a download or sell order before the market opens the next day if prices pass through that MLH.

When a second "space" reversal negates a previous one, there has been a "shake out" that signals a larger move in the opposite direction. Also use MMLH as a stop loss right after entry.

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Converging lines that meet prices have high probability of trend reversal. MMLH lines can be drawn through the daily range after a gap. Two to four days is usually a maximum between 2 and 3 for an MML. P1 can be 1 day or more back from 2 and 3. Normally use a down sloping R line to call a sell point, with A line measured through a bottom.

Exception: still using Dt R line to call the sell point, when CL is a , the top seems to work for the A distance, as well as the bottom. When prices pass through R lines, it often drop back as with MLs that are passed by prices, but signals probability of further move in direction before the pull-back. Since each gap is 2 Ps they can be used for A points.

Hagopian's Rule applies to R lines. The longer the CL the more reliable it seems. His discovery of the natural law that Action and Reaction are equal and opposite in the field of physics also has been applied in the Course to the random changes of price movements in free markets. This application of the Action-Reaction law enables you to learn in advance where the probable reversals of price trends will come in the future.

When we speak of any scientific law, we mean a statement that a relationship has been observed among certain given conditions. We mean "if these conditions now, then those conditions will follow, and can be expressed mathematically".

The Best Trendline Methods of Alan Andrews

We have "order" through which we can know the outcome from these conditions. We can therefore take advantage of this knowledge, and thereby progress and profit. So Newton was one of the great discoverers of this "orderliness" that underlies all of the Creator's work, even if we are often slow in discovering it.

Newton's Laws therefore as stated above, have benefited the users in both flowing and random changes. The definition of randomness implies that future conditions are unascertainable, because there seems to be a lack of order underlying such change.Conservation of capital is the first rule of any prudent investment strategy, and the probability of large losses can be effectively reduced by the disciplined application of tested Technical Market Indicators.

If there is a good reason to believe that the basic nature of a market may have changed over time, we would be justified in systematically deleting the oldest time segments of historical data. Seite 1 von 47 the interpretation and application of price action concepts NQoos ;- Master your setup. This enables you to understand the scientific reason for each new position taken based on simple geometry. Babson believed investors could closely forecast the length of a depression based on the normal line method.

In contrast, many Technical Market Indicators offer simple, sensible, intuitively obvious, easy-tounderstand, and precisely defined formulas based on a manageable number of variables.

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