Optimal Trader 3.1.5 + Crack

[longs]

Optimal Trader v.3.1.5

Models and indicators

Optimal Trader works with six models at the same time (one top model and five active models). Optimization of each model is applied on each stock based on its behavioural patterns.
Several off the models are classic indicators, but in Optimal Trader they are improved and refined, for instance with smoothing adaptive to the movement patterns of the stock. Among the models available you can for instance choose Stochastic Oscillator, StochasticInverse (in-house developed indicator), RSI, Regression Analysis (in several degrees), MACD and Parabolic SAR. Read more about the different models here.


Portfolio Scan

With Portfolio Scan you can find winning stocks or funds based on your criteria and neural network forecasting. Optimal Traders Portfolio Scan is a powerful feature because you can combine several criteria, apply a different weight to each criterion and include neural network forecasting.
You can also diversify your portfolio with beta values to minimize risk and estimate the predictability of stocks or funds.
More info: Portfolio Scan - Pick Winning Stocks.

Artificial Neural Network

In addition to the five optimized models there exists another model that always is active: the Top Model.
The Top Model is normally constituted of an artificial neural network. Neural networks have a high ability in detecting patterns in stock price development. A neural network can be described as an artificial brain that can be trained to perform certain tasks, for example predicting future stock price values.

Combined Analysis Model

You can also choose to let the top model be constituted of a weighted fusion of the active models trading signals.
Perhaps you have noticed that some models produce buy signals while at the same time other models may produce sell signals? Which model should you follow? Optimal Trader's Combined Analysis Model merges the buy and sell signals of the active models into a single trading signal. The merging is not static but adapted optimally for each stock. The Combined Analysis Model produces more stable buy and sell signals than single models buy and sell signals.

Best Models and Settings

The Best Models and Settings feature returns the models and optimization settings which historically have performed best (from a large database with historical prices) when price behaviour was similar to the current price behaviour.
Best Models and Settings also shows you a report with the returns you can expect on average with your current settings and the current price behaviour.
More info here.

Optimization

When performing standard optimization, parameters in the trading system are tested with different values and the combination of values that has yielded the largest profit is thereafter used. Normal optimization produces profitable and stable values using long optimization periods, but there are more careful ways to optimize the parameters.
In Optimal Trader you have the possibility of optimizing the models so that they are trying to produce a high and stable profit instead of trying to maximize the total profit. The result is that the probability of profitable future results increases. This option is called Robust Optimization.
Another option is to use Robust Parameters which returns parameters that are more stable in the parameter space. Parameter values around optimal solutions are checked and optimal parameter combinations that produce good results when changing the parameters slightly are valued higher than parameter combinations which perform inferior when changing a parameter value slightly. This setting may thus produce more stable results.

There are many factors to consider when optimizing parameters and you have to be careful not to overfit the models. In Optimal Trader you can adjust the following optimization factors:

• How long period backward do you want to use for optimization (optimization period)?
• Do you want to limit curve fitting of stock data that contain sudden larger price gaps and peaks?
• Do you want price development closer in time to have a higher influence than price development a long time ago?
• Does it cost to buy and to sell your share/fund (Brokerage fees, fund fees)?
• When are your purchases and sells carried out? (default: same day for all equities except for funds)

Notice that the goal of optimization is to find optimal parameters, not to give an estimation of future returns. Optimal parameters will almost always produce superior results for the optimized period compared to realistic usage of the system. Estimate more realistic returns with the Best Models and Settings feature or with Back Testing.
If you do not want to use optimized parameters can you use Optimal Trader's default parameters which are selected to produce reliable and stable results in most situations, or set the parameters yourself manually.
Read more about optimization in the section Technical analysis with optimized parameters.

OptAMA - Optimal Trader Adaptive Moving Average

In technical analysis there is an aim to smooth signals in order to reduce noise. Smoothing is achieved by applying a moving average. The advantage is that indicators produce more unambiguous signals, but moving averages also introduce a delay. The more the signals are smoothed, the larger the delay will get. Optimal Trader uses an in-house developed adaptive moving average, OptAMA, that eliminates much of the delay, but nonetheless smoothes the noise efficiently. The result is clearer signals in many models and fewer erroneous signals when the trend is uncertain.
Signals come consistently earlier which is important in sudden upward or downward movements, since the the largest price differences often happen early in these phases. Read more about OptAMA here.

Back Testing






Optimal Trader can make a backward analysis (step-by-step Back Testing) to evaluate the results of following a certain stock with a certain model. The function simulates use of a model through day-by-day optimization a number of days backward in time in order to use the same buy and sell signals you would have gotten if you would have followed the model in reality and optimized it each day.
Notice that this is not the same as carrying out a simple back test, i.e. to look on how a certain model would have performed backward in the time with some parameters. It is always easy to find good parameter values for a period afterward, just as it is easy to realize how you should have traded a stock afterward. Step-by-step Back Testing gives you a more reliable indication on a model's strength.
One drawback is that Back Testing results are very sensitive to prediction results for rare major price movements. No matter how long the period you are backtesting for, a single 10% investment increase or decrease of a specific day will still affect the overall performance of the complete period with 10%.
Another drawback is that the results, which are a measure of the predictiveness, only are based on a single time series.
Thus, a more stable analysis is provided with the Best Models and Settings feature where you can select to generate report.
Technical Analysis with Neural Networks

Neural networks are perfectly suited to model price movements. They can model price behavior mathematically themselves and they have the ability to extract information from large amount of data which is necessary for complex signals such as financial price movements.

A neural network is made up of a number of interconnected neurons which behave like an artificial brain. The network is stimulated by appropriate input signals, in this case of historical price data and outputs from other technical indicators.
The neural network is trained to find connections between these input signals and future price levels. If there exists such a connection, neural networks have an amazing capability to find these. This is then used to generate appropriate trading signals.

The biological brain

The humane brain is one of the most complex items in the universe. Although the brain has a considerably slower processing speed and less storage capability than a modern computer, it is the nevertheless superior in many areas such as pattern recognition and adaption to new situations. With modern technology neural networks can imitate the brain's way to learn and process information. Neural networks are basically a collection of interconnected neurons, each one with several inputs and one output. The inputs' weight (importance) and numbers can vary. The output is a function of all inputs. The picture below shows how biological neurons interact with each other.

We see that the blue neuron is sending an impulse to the yellow neuron. The yellow neuron may receive other impulses (varying in strength) from other neurons, but is only sending one signal (a function of all the inputs).
This biological system can be transfered to a computer:
The diagram to the right shows a number of inputs with varying strength which the neuron processes and produces an output from. The building of networks of these neurons is a lot more complicated than described here and involves neural models and mathematics that are beyond this short summary.

Although neural networks are not nearly as sophisticated as their biological counterparts, they mimic our way to think to make quicker and better decisions.

Trading with neural networks

• Neurala networks do not follow any predefined rules (as classic technical analysis does) but finds its own connections and rules.
• Neural networks are highly accomplished in finding patterns
• Neural networks can work with different types of inputs and are therefore more flexible than a single indicator. You can feed a neural networks with both historical data and with different technical indicators.

Who Uses Neural Trading Systems ?

There are various financial areas in which financial professionals use neural-based applications: in various bond trading to determine the probability that a given company will default on its debt, in sales forecasting to predict future sales based on a series of inputs, and in equity trading to predict future price movements. Many professionals and institutions are testing and using this technology to more accurately predict events and profit.


Why Doesn't Everybody Use It?

The application of neural networks to trading is relatively new and build neural applications involves complex mathematics. You cannot analyze a neural network and explain how a specific network draws its conclusions.
Some critics say that the idea of a software system learning the behavior of the market is flawed. If humans cannot predict the market with certainty, how can we create software that can achieve something we do not even fully understand?

optim_install.exe
optimal.trader.3.1.5-patch.exe

Installation Notes, Extra Information and Download/Mirror Links

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Portfolio Scan

Pick Winning Stocks

Optimal Traders Portfolio Scan is the perfect feature when it comes to helping you find stocks that meet your investment criteria and help you grow your investing and trading profits. Similar but more basic services are available online for free on many sites, but the features of Optimal Traders Portfolio Scan make it one of the most powerful analytic services available.

• You can combine several criteria and add weight to each criterion based on its importance. You can include price return (growth) measures of several time ranges in the same scan. You can include artificial neural network forecasting in your scan.You can handle risk by minimizing or maximizing Volatility. You can detect changes in stock behavior with the Short-Term Volatility Factor. Diversify your portfolio by measuring Beta values.
• Estimate predictability with the Hurst Exponent.

Weight

The weight of each factor (criterion) determines the importance of the factor when analyzing. You can include several factors in your analysis and add a weight to each factor. You could, for instance, add the weight 30 to the short term momentum, weight 20 to the medium term momentum, weight 25 to volatility and weight 25 to the Short-Term Volatility Factor.
The larger the weight is of a factor, the more important it will become when rating each equity in your portfolio in the result table. A weight of 50 makes the factor twice as important as a weight of 25.

Only integers are allowed.
Normalize Weights

When you normalize the weights they are scaled so that their sum equals 100. Normalization of the weights only affects the numeric size of the scores, not the results.
You do not have to normalize the weights before a scan.
Factor Normalization

Normalization maps each factor to the same range making comparisons between factors easier. The disadvantage is that you can not read out the exact values of the factors.
Normally, you do not have to change this option.
No Normalization: The factors are not normalized, meaning that they are not altered in any way. This can be good if you want to see the exact Beta or Hurst values for every equity, but can make it more difficult to estimate good weight values.
Absolute Normalization: The factor values are scaled to the same standard deviation (1) which makes it easier to assign good weight values. The normalization is independent of the current portfolio, which means that you can compare results between different portfolios.
Relative Normalization: Normalizes each factor to the same mean (0) and standard deviation (1). Makes it easy to assign weights, but you can not read out the exact values of the factors.
Start Scan

Calculates a score for each equity in your portfolio based on your settings, sorts the equities after their scores and presents the result in the result table to the right.
The Result is presented for each stock as a value which will be larger the better the stock has performed by your criteria. The better a stock has performed, the higher it will make it in the result table. It is of course always advisable to check the charts of the stocks which have performed well.
Save Results

Click to save the result table to an Excel or a text file.
Save Settings

Saves all settings of the Portfolio Scan function. Notice that you have to click Save in the main window of Optimal Trader as well to keep your settings until the next time you use Optimal Trader.

Score Result Table

The result table presents the scores of each equity according to your criteria and the values of all factors. You may sort the table by specific factors by clicking the header cell of the corresponding column in the table.
If you are interested in the absolute values of the factors and not just their relative values, you may select No Normalization before you click Start Scan. Note that it scores can be biased towards a specific factor and that it makes estimation of good weight values more difficult.

Global Time Range

Notice that Portfolio Scan can only use the time range limited by the equity with the shortest time range in your current portfolio. This time range is called the Global Time Range. If you have one stock with a price history of 100 days it will thus limit the possibilities of an analysis. The same applies if you have a stock that has not been updated the last ten days. You can then only make an analysis with data up to ten days ago.
If you can not make a time range setting for a factor because the global time range is to short you will have to remove the equities with the shortest available price history from your portfolio to make the global time range larger.
Notice that the time ranges expressed in Optimal Trader are market days, that is 252 days amount to one calendar year.

Examples

Example 1

You would like to pick a mutual fund which has performed well the last month and at the same time also has advanced above average the last six months. At the same time the fund should have a low risk (low volatility). With Portfolio Scan you can sort a number of funds after these criteria and pick out a suitable fund for investing.
Example 2

You would like to find a small-cap stock with potential of growing into a big cap stock. Your criteria may be that the price should have advanced highly the last three weeks, and advanced well the last three months. To maximize possibilities of high returns you want to pick a stock with high volatility and whose volatility has increased the last month. In addition you can also maximize or minimize the beta coefficient depending on the situation. With Portfolio Scan you can fill a portfolio with small-cap stocks and find a stock which fulfils your criteria.
Example 3

You would like to invest in a Russian mutual fund, but do not know from which investment company. If you fill your portfolio with many different Russian mutual funds you can with the help of Portfolio Scan find the mutual fund which will fit your criteria the best.
Example 4
You would like to find stocks for which the neural network predicts high returns. You would also like to favor stocks which are easier to forecast (high predictability). Thus you apply a weight of 60 to the neural network and a weight of 40 to the Hurst Exponent.
Portfolio Optimization

Introduction

Portfolio Optimization is a risk management tool in Optimal Trader based on Modern Portfolio Theory for which Harry Markowitz was awarded the Nobel Price in 1990. This application will help you to optimize your portfolio with regard to the risk of the individual assets, the correlation between the assets and the expected returns of your assets. Portfolio Optimization is a powerful and easy to use risk management feature in Optimal Trader which will help you to allocate assets optimally in your portfolio.

Expected Returns and Risk

The returns an investor will get in the end can deviate considerably from the the returns that were expected. But investment decisions must be made in advance, based on expectations of an uncertain future. It is not sufficient to base these decisions on expected returns alone because expected returns alone provide an incomplete description of the future. A further factor which is of equal importance for a well-analyzed decision is the risk of the investment, that is, a measure of how certain we can be that the expected return will be realized.
Afterwards when we know the outcome, a higher return is always preferred before a lower return, irrespective whether this has been achieved with a high or low level of risk. But hindsight is not an option when making investment decisions. Well-analyzed investment decisions are always based on both expected returns and risk, because this will describe a more complete picture of the future.
More about risk

The risk(volatility) of an equity is measured by the standard deviation of the equity's rate of return. You can think of the standard deviation as measuring how far away from the expected return the realized return is likely to be. The greater the standard deviation, the more variable the rate of return.
The 68-95 rule states that most returns lie within 2 standard deviations of the expected return. About 68% of the returns lie within one standard deviation of the expected return (between the expected return minus one standard deviation and the expected return plus one standard deviation). About 95% of the returns lie within two standard deviation of the expected return. For example, if the standard deviation is 0.5%/day and the expected price for the next day is $85, then there is a probability of 68% that the next day's price will lie in the range $84.50-$85.50.
Correlation

When estimating the risk of a portfolio it is not sufficient to estimate the risk for all individual assets in the portfolio. The risk of a portfolio also depends on the correlation between all assets. Correlation is a measure of the degree to which two assets (or investments) move together.
The correlation between two assets lies between -100% and 100%. The higher the correlation is between two assets, the more similar are the price movements of the assets. A high correlation, for example 60%, means that the assets tend to move in the same direction. If there is no relationship between the movements of two assets, then the correlation is zero and the relationship is governed by randomness. If correlation is negative the assets tend to move in opposite direction. The correlation will be closer to -100% if the negative relationship is strong and closer to zero if the relationship is weak.

Portfolio Risk and Correlation

If correlation is less than 100% the risk of the portfolio will be less than the average of the risk of the assets. Risk, measured with portfolio standard deviation, falls with correlation. The lower correlations are between assets of a portfolio, the lower the risk is of the portfolio. Diversification will thus be profitable (meaning we will get a higher return for a certain level of risk) when combining assets which are not entirely the same.
How Do We Combine Assets Into A Well-Balanced Portfolio?

A well-balanced portfolio consisting of many stocks not strongly correlated will always produce a lower risk for a certain expected return rate, but how do we combine such a portfolio?
For every level of expected return, there is one optimal asset combination which offers the lowest possible risk, and for every level of risk, there is one optimal combination which offers the highest expected return. These optimal combinations are called efficient portfolios. An efficient portfolio is one which has the smallest attainable portfolio risk for a given level of expected return (or the largest expected return for a given level of risk).
There are two demands on an efficient portfolio:

• There is no other combination of assets which offers the same expected return for a smaller portfolio risk.
• There is no other combination of assets which offers a higher expected return for a given portfolio risk.

These optimal combinations can be plotted on a graph, and the resulting line is called the efficient frontier.
The efficient frontier begins on the left side of the chart with the portfolio which has the smallest attainable risk of all efficient portfolios. There is no other combination of assets which can achieve a smaller portfolio risk. This portfolio is selected by default with a red solid circle in Optimal Trader. In the upper left table you will find the asset weights for that important optimal portfolio.

Which efficient portfolio you select depends on your acceptable level of risk. But irrespective of your risk tolerance it is always superior to select an optimal combination of expected return and risk, that is, a portfolio on the efficient frontier.

Portfolio Optimization in Optimal Trader



In the upper left table you can select which stocks Optimal Trader will include when calculating the efficient frontier. You can edit expected returns, minimum allowed weights and maximum allowed weights for each stock. If you want to return to the default settings, simply click the "Default"-checkbox. The column on the right edge of the table shows you the optimal weights of your stocks expressed in percentage of the total portfolio.
The upper right chart displays price movements for all selected stocks normalized to the same starting point. Keep your mouse above a curve and a tooltip will appear with the name of the corresponding stock.
The lower left table contains the correlation matrix with correlations between all selected stocks. The correlation lies between -100% and 100%. As you can see the correlation is always 100% when correlating a stock with itself.
The lower right chart shows all stocks in a risk-return space and the efficient frontier. Risk increases as you move to the right in the chart and expected returns increase as you move upwards in the chart.
Expected returns in Optimal Trader are by default estimated by calculating the geometric mean of the historical daily percentual returns, but can also be estimated with Portfolio Scan results. There are limitations to expected returns estimation which are discussed in the next chapter.
Every colored dot corresponds to a stock in your portfolio placed at its standard deviation and it's averaged return. Keep your mouse above a dot to see the name of the corresponding stock in a tooltip. The same colors are applied to the dots in the lower right chart and the curves in the upper right chart for each stock.
The Efficient Frontier

The curve in the lower right chart is the efficient frontier, which is constituted of all optimal combinations of stocks. The curve begins on the left side with the optimal stock combinations which has the smallest attainable portfolio risk of all possible stock combinations. That portfolio is selected by default which is why a red solid circle is placed there. You can read the weights of that portfolio in the upper left table.
Click on another part of the curve to move the red solid circle and select another efficient portfolio. The weights in the upper left table will immediately adapt to your change.
The combination far out to the right on the efficient frontier only tries to maximize return without any risk consideration. That portfolio is solely constituted by the stock with the highest averaged return and is of course not a realistic portfolio combination.

Expected Returns Estimation

When calculating efficient portfolios on the efficient frontier, three measures are needed:

1. Expected returns for all portfolio assets
2. Risk for all portfolio assets
3. Correlation between all portfolio assets

Risk and correlation are fairly constant measures and do usually not change much over time. Expected returns, on the other hand, is a much more difficult factor to handle in reality.
Traditionally and theoretically, expected returns are simply estimated by averaging historical daily returns for all stocks. Although that is not a reliable estimation it is often used because there are no good alternatives. But if a stock historically has yielded an average annualized return of 10% and another stock historically has yielded 5% it is bold to make the assumption that this relation will hold in the future.
For that reason some investors only use one efficient portfolio: the one with the lowest attainable risk. This portfolio is situated furthest to the left of the efficient frontier and is special because it does not consider expected returns at all. It is simply constituted by the portfolio which has the lowest attainable risk of all possible portfolios. This portfolio is selected by default in Optimal Trader when calculating the efficient frontier and is thus marked by a solid red circle by default.
To use this strategy you should include a limited number of equities, maybe 5-15 stocks which you are certain that you want to invest in. You can use Optimal Trader's Portfolio Scan to find stocks that meet you investment criteria.

Integration with Portfolio Scan

You can let the expected returns be proportional to Portfolio Scan results. Portfolio Scan is a feature in Optimal Trader which helps you to find stocks that meet your investment criteria and helps you to compare stocks. The result of Portfolio Scan is a value for each stock which will be larger the better the stock has performed with regard to your criteria.
Portfolio Optimization will allocate to assets in your portfolio with regard to Portfolio Scan scores of each stock, the risk of each stock and correlation between stocks. Portfolio Optimization combined with Portfolio Scan results is thus a very powerful investment tool.
This feature is found under the Options button and is named Expected returns: Portfolio Analysis. To return to traditional expected returns estimation select Expected returns: Default.

[JoeDOM_Admin]

All installation was smooth

Patcher provided by our contributor Longs is straightforward. Just place the patcher executable inside the installed folder and run it; and you are done.

I advaned the PC clock 1 year forward andran the software, it still ran properly.
Updated some data from yahoo.com and they downloaded the daily data.
The software allows you to use ASCII or Excel files to feed in data.

Finally below you can see the About box: