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Altreva Adaptive Modeler FAQ

General

What is Adaptive Modeler?

Adaptive Modeler is a software application for creating agent-based market simulation models for price forecasting of real world stocks, ETFs, forex currency pairs, Bitcoin, other cryptocurrencies, commodities or other markets.

What are "agent-based models"?

Simply said, an agent-based model is a bottum-up computer simulation of many people and/or organizations interacting in some environment, in order to see their collective effects. For more about this and how we make use of it, please see the Technology section.

How does Adaptive Modeler differ from other trading software?

This is explained in the Product > Advantages section.

Is there a free Evaluation Edition?

Yes. The free Evaluation Edition allows users to get familiar with the technology and allows non-business users such as students and researchers to do research and experiments. The Evaluation Edition thereby contributes to a large and diverse user base resulting in knowledge sharing between users and valuable feedback. This is also beneficial for the further development of Adaptive Modeler. The Evaluation Edition delays the processing of recent quotes and can therefore not be used for actual trading.

How long can I use the free Evaluation Edition?

You can use the Evaluation Edition as long as you like. It does not have a limited trial period.

Is there a Professional Edition?

Yes. The Professional Edition is intended for traders, investors and other professional users. It offers more features, more computing power and real-time forecasts and trading signals (see Compare Editions).

There is also a Standard Edition that offers the main functionality including real-time forecasts and trading signals.

What are the benefits of the Professional Edition?

This is explained in Compare Editions (in particular see Benefits of features).

Is Adaptive Modeler Open Source?

No, Adaptive Modeler is not Open Source.

What expertise is needed to use Adaptive Modeler?

General knowledge of investing, trading and financial markets is assumed. For advanced use, a basic understanding of how Adaptive Modeler creates trading rules by Genetic Programming is recommended. This is explained in the User's Guide. No programming skills are required.

How do I get started?

The User's Guide contains a Getting Started Tutorial to guide you through the main concepts and features of Adaptive Modeler.

Installation

What do I need to run Adaptive Modeler?

This is described in the Product > System Requirements section.

Does Adaptive Modeler run on Mac or Linux?

Currently Adaptive Modeler requires the Microsoft .NET Framework 4.8 Runtime.

How do I install Adaptive Modeler?

1. Download Adaptive Modeler from the download page of the Altreva website (Evaluation Edition) or from the personal download link provided by Altreva Support (Standard and Professional Edition). In either case, select "Save".
2. If you are a user of previous versions of Adaptive Modeler, please read Upgrading from previous versions first.
3. Unzip the downloaded file, run the installer file and follow the instructions on screen.

How do I upgrade from previous versions?

Users of the Evaluation Edition can download the latest version from the download page. Users of the Standard or Professional Edition should contact Support for upgrading instructions.

Please take note of the following before installing a new version of Adaptive Modeler:

See the latest Release Notes
These may explain changes relevant to users of previous versions that are not evident from the user interface and may contain other important information.
Uninstall previous installations
Previous installations of Adaptive Modeler must first be uninstalled. Also the Evaluation Edition must first be uninstalled before installing the Professional or Standard Edition.

Importing market data

How are quotes being retrieved?

Adaptive Modeler reads quotes from ASCII (CSV) files in familiar formats such as those used by popular charting and technical analysis software packages.

Does Adaptive Modeler support online datafeeds?

No, Adaptive Modeler only reads quotes from ASCII (CSV) files. Most providers of market data provide software to export the received data to ASCII (CSV) files. Also several third party conversion tools exist. For more information, see Market data.

What quote data formats does Adaptive Modeler support?

Adaptive Modeler contains a flexible and intelligent data file reader that automatically reads ASCII (CSV) files in a wide range of formats such as those used by most charting and technical analysis software packages. Most files will be read without adjustments or require only minimal conversion. See the User's Guide for more information on how to import market data.

Can Adaptive Modeler read MetaStock quote files?

Adaptive Modeler can only read MetaStock ASCII files, not the binary MetaStock files. For more information on how to import ASCII files, see the User's Guide.

Can I use intraday quotes?

Yes. See What quote intervals are supported?

What quote intervals are supported?

Adaptive Modeler supports any quote interval ranging from 1 millisecond to multiple days provided that processing time per quote is short enough. For high-frequency data, the actual usable minimum interval thus depends on situation specific factors such as CPU speed, model parameters and data retrieval latency. Usually, processing time per quote is only a fraction of a second. Variable intervals are also supported (i.e. for constant range bars or tick data).

How many historical quotes do I need?

Since Adaptive Modeler uses evolutionary computing (meaning that it learns over time), it is recommended to provide the model with some historical market data. At least 1,000 historical quotes is recommended. The more historical data is used, the more opportunity the model has to learn and the more information the user gets on how a model performs during different periods and market regimes. Also, a sufficient number of quotes needs to have been processed for demonstrated forecasting performance to be statistically significant.

Can I use artificially created historical price data for (extra) training of models?

Artificially created price series may be useful to "train" models, especially when insufficient real historical data is available for a security. There is no specific support for this but we encourage users to experiment.

How does Adaptive Modeler process splits and dividends?

Adaptive Modeler does not automatically adjust for splits and dividends.

As stock splits have a distorting effect on model evolution and on return calculations, the quote history file should be adjusted for splits before creating a model. When a new split occurs, a new model should be created after re-adjusting the historical quotes.

Whether or not historical quotes should be adjusted for dividends and by which method depends on situation specific factors and should be considered by the user. Note that return calculations in Adaptive Modeler do not include dividend payments.

Note that it may be necessary to adjust the rounding settings in Adaptive Modeler when historical prices have become very low after adjusting for splits and/or dividends.

When will Adaptive Modeler retrieve a new quote?

Adaptive Modeler retrieves and processes new quotes as soon as they have been added to the quote file (except when the model is paused). (The Evaluation Edition processes recent quotes with a delay of a few days).

Why does a quote not get imported although it is in the quote file?

When a quote does not get imported even when there is a valid and complete entry in the quote file, this can have the following causes:
1. The model may be paused. Press F3 to resume model evolution.
2. The quote file may (temporarily) not be accessible because another application is locking the file. Make sure that the quote file is not unnecessarily being locked by another application.
3. In the Evaluation Edition, processing of recent quotes is delayed with a few days.

How can I correct invalid or incomplete quotes in the quote file?

When quote data is found to be invalid or incomplete by Adaptive Modeler, a warning is shown and model evolution is paused. The user can then correct the quote file (see the User's Guide for quote data requirements). After correcting the quote file, the user should resume model evolution (press F3) and operation will resume normally.
Erroneous quote data that might not be detected by Adaptive Modeler as invalid or incomplete, should be corrected before model evolution comes within 100 bars of the quotes to be modified or else they may already have been read into a buffer and may not be read again.

Adaptive Modeler can't read my quote file. What should I do?

Make sure that the quote file is properly formatted in one of the supported quote file formats (see User's Guide). If you still experience problems, please contact Support and include a small part of the quote file.

Performance

What returns can be made with Adaptive Modeler?

The potential performance of trading based on a model's trading signals depends on:

forecast accuracy (more precisely the Forecast Directional Accuracy and Significance; or roughly the percentage of bars for which the price change direction was forecasted correctly)
volatility (on the quote interval being used)
transaction costs (broker commissions, spread and slippage)
Trading System parameters

Note: all returns shown in Adaptive Modeler are after all transaction costs (broker commissions, spread and slippage) but exclude dividends and interest payments.

The forecast accuracy in its turn depends on:

security or market being forecasted
quote interval being used
number of historical quotes provided
quality of market data
usefulness of additional custom input variables
model parameters
random numbers used in model creation and evolution

It is therefore not possible to say anything in general about the potential performance of Adaptive Modeler. Adaptive Modeler is an application for creating market models that produce price forecasts. The accuracy of the forecasts of a specific model depends on all of the factors given above. Adaptive Modeler does not pretend to be an always winning trading system and its performance depends on how the user uses it and to what extent the market actually contains predictive patterns (sometimes markets are truly efficient).

We do not provide performance information of models that we may have created for our own use or for others. As these models are proprietary and/or subject to confidentiality agreements with their owners, it is not possible, nor would it be appropriate, to provide information about their performance. As a software publisher we do not want to make claims about investment returns, especially not unverifiable claims that could be seen as misleading and may in fact be illegal in some legislations. Also, such information would be largely irrelevant to users since they would not be able to use those particular models nor is there any reason to expect that those models would be the best possible models that could be created with Adaptive Modeler. Obviously, there are many different combinations of securities, quote intervals and model parameter settings. The vast majority of these combinations have not been explored by us. In order to prevent (unintended) optimization (overfitting) of the internal design of the agent-based model to historical market data, we do not extensively or systematically explore the search space of combinations. It is therefore likely that combinations of security, quote interval and model parameters exist with better performance than known to us.

Exploring the potential performance for a given security and quote interval requires experimentation and careful observation of results. With a given set of model parameters values, the results of different runs (separate model evolutions) can still vary because of the use of random numbers inherent to agent-based modeling and genetic programming. It may therefore be necessary to do a number of runs with the same parameter values for a reliable analysis of results.

Adaptive Modeler provides a variety of ways to review the accuracy of previous forecasts and trading signals. Adaptive Modeler contains a Trading Simulator with a Performane Overview showing various return/risk indicators, sub period statistics and trades statistics. Also it is possible to project likely future trading returns under given conditions through stochastic simulation. However, as with any system that aims to make predictions about the future, there is no guarantee that any demonstrated forecasting success or trading performance will remain the same in the future. The user should be aware of this and consider the risks and potential rewards of every investment or trading decision on its own merits. See also this Important Information.

How can I see the performance of the Trading Simulator?

Click on the "Performance" tab below the Charts window or open the Performance Window through the "View" menu. The Performance Window should now be showing. A "Settings" button in the top-left corner allows changing the calculation period and other settings. For more information, see the User's Guide.

What indicators and charts do I need to look at to evaluate the accuracy of forecasts?

To evaluate the forecasting accuracy of a model, several indicators are provided. For example, the Forecast Directional Accuracy (FDA) measures the percentage of bars for which the price change direction was forecasted correctly. More about this and other indicators is explained in the User's Guide.

Can Adaptive Modeler predict the next market crash or bubble?

Adaptive Modeler only forecasts one bar ahead. This way the most recent market price data is always available to the agent-based model when calculating a forecast. Therefore, a market crash or bubble (or a new trend) can only be anticipated bar by bar as it unfolds. The effect of forecasting multiple bars ahead could be imitated by evolving a separate model using a longer quote interval.

Why are the results of different runs different?

The results of different runs (separate model evolutions using the same quote data and model parameters) can still vary because of random factors inherent to agent-based modeling and genetic programming. Random numbers are for instance used in the creation and evolution of trading rules. (Note that to recreate a model exactly it is necessary to use the same quote data, the same model parameter values, the same random seed value and the same version of Adaptive Modeler. Model evolution may also vary slightly across different CPU types, OS versions/settings and Microsoft .NET Framework runtime versions because of small floating point calculation differences).

For a more complete analysis of the potential performance of a given combination of quote data and model parameters, it is recommended to do a number of runs to see the variation in performance indicators.

For more information about the creation of trading rules through genetic programming and running multiple model evolutions, see the User's Guide.

Achieving optimal results

What markets and quote intervals does Adaptive Modeler work best on?

Adaptive Modeler is primarily designed for active trading of stocks or stock indices (i.e. using ETFs or futures) with sufficient volatility and small spreads. Other markets such as forex currency pairs, commodities, Bitcoin or other cryptocurrencies may also be used since in principle Adaptive Modeler can process any kind of time series.

In general, the volatility on the used quote interval must be high enough to cover transaction costs (broker commissions, spread and slippage). If not, (simulated) trading performance will be poor even with high forecasting accuracy. For instance it will be more difficult to achieve good performance using a 1-minute interval than using a daily interval because the 1-minute price changes may be too small to cover transaction costs. This means that the break-even forecast accuracy level for a 1-minute interval is higher than for a daily interval.

We have not extensively researched which securities can be forecasted best. Many different stocks, ETFs, forex currency pairs, commodities and other securities are being traded on financial markets around the globe. Also, several different quote intervals could be used for each of these securities. Different securities and quote intervals may require different model parameter values. In order to prevent (unintended) optimization (overfitting) of the internal design of the agent-based model to historical market data, we do not extensively or systematically explore the search space of combinations of security, quote interval and model parameters.

What parameters values should I use? What are the best settings?

There is no general answer to this. Some parameters clearly depend on the quote data being used. For other parameters, the default settings may be a good starting point but experimentation is strongly recommended. No person or team alone can ever explore all the possible parameter value combinations for all the quote data of different securities and intervals. As said earlier, we do not extensively or systematically explore the search space of all possible parameter values, in order to prevent (unintended) optimization (overfitting) of the internal design of the agent-based model to historical market data. Therefore, the default parameter values are unlikely to be the best values for all cases.

Customizing the parameters to the specific quote data being used includes things like:
- on the General tab; setting the right Market Trading Hours
- on the Model tab; entering the right Rounding settings
- in the Gene Selection; enabling/disabling the open, high, low, bid, ask genes (depending on which data is included in the quote file and whether or not agents should see it; note that bid and ask also apply to the Virtual Market; note that high and low prices are sometimes considered unreliable because of false spikes)
- in the Gene Selection; enabling/disabling volume related genes (depending on whether or not volume data is included in the quote file and agents should see it)
- in the Gene Selection; enable/disable genes related to custom input variables (depending on whether or not these are included in the quote file and whether or not agents should be able to see them)
- on the Trading System tab; entering realistic transaction costs (broker commission, spread and slippage) and your personal trading preferences
- etc.

What Forecast Directional Accuracy is needed for a model to be profitable?

This depends on factors such as volatility, transaction costs and other Trading System parameters. In general, it should at least be above 50%. Then the forecasted direction of bar-to-bar price changes is more often right than wrong. With the Statistical Simulation data series it is possible to project the potential returns based on expected values for FDA, FDS, volatility, transaction cost and other Trading System parameters. See the User's Guide sections about Statistical Simulations for more information about this.

Can trading signals automatically be enabled / disabled based on FDA?

Yes. On the Trading System tab in the "Model Configuration" dialog, check "Apply FDA filter" and enter the desired threshold value. The FDA calculation settings can be changed by clicking on the "FDA settings..." button.

For how long are the trading signals valid?

A trading signal remains valid until a new signal is given. For more details on how trading signals are being generated, see the Trading Signal Generator section in the User's Guide.

My intraday models are not profitable. What is wrong?

If your models are not profitable even though the Forecast Directional Accuracy (FDA) is clearly above 50% on average, then the bar-to-bar price changes are probably too small to cover transaction costs (broker commissions, spread and slippage). For small price changes, FDA needs to be higher to reach break-even than for bigger price changes.
To increase FDA, try experimenting with other model parameters. If this still doesn't work, then consider using a longer quote interval.

Which forecast is better: the Virtual Market Price or the Best Agents Price?

By default, Adaptive Modeler uses the Virtual Market Price as the forecast. Optionally, the forecast can also be based on the Best Agents Price. This feature is useful since it allows a comparison between the predictive abilities of the Virtual Market Price (which is based on the behavior of all agents) versus that of the Best Agents Price (which is based only on a group of the best performing agents).

Since an essential principle of Adaptive Modeler is to use the Virtual Market Price as the forecast, it may be interesting to see whether or not this in fact outperforms a forecast based on the behavior of only the best performing agents (which may seem more intuitive to some people and more in line with methods generally used by trading software).

As far as we have observed, the Virtual Market Price almost always performs better than the Best Agents Price. However, this may not be the case in all situations. Experimenting is recommended. Note that the accuracy of both forecasts can easily be compared by showing two FDA data series together in one chart and setting the source parameter of one data series to the Virtual Market Price and the other to the Best Agents Price. Also note that the Best Agents group size can be changed on the Model tab of the "Model Configuration" dialog.

Agent-based model

How many agents can a model contain?

The Professional Edition supports a population size of up to 250,000 agents. The Standard Edition supports 25,000 agents and the Evaluation Edition 5,000 agents.

How do agents make trading decisions?

Each agent has its own (technical) trading rule. The trading rules can use historical price and volume data as input and return an "advice" consisting of a desired position to hold in the security and an order limit price for buying or selling the security. The internal logic of the trading rules consists of various functions such as price and volume data access functions; average, min, and max functions; logical and comparison operators; and some basic Technical Indicators. In most cases, agents are technical traders.

The trading rules are created by and evolve through a special adaptive form of strongly typed genetic programming. For more information about this, see the User's Guide.

It is also possible to simulate "zero-intelligence" trading by using the RndPos and RndLim genes. With these genes the position advice and order limit price are established randomly. This can be useful for comparison purposes. (To simulate complete zero-intelligence trading, select only the genes "advice", "RndPos" and "RndLim"; disable "Create unique genomes"; disable breeding by setting "Breeding cycle frequency" to 1,000,000; set "Broker Commission for agents" to zero).

Do agents learn?

Through an evolutionary breeding process, agents with poor performance are regularly being replaced by new offspring agents. These new agents each get a trading rule that is created through crossover and mutation of the trading rules of the best performing agents. This way (parts of) trading rules that perform well are reproduced and recombined while poor performing trading rules are being removed. This way, the population of trading rules as a whole attempts to adapt to changing market behavior. So the population is adapting rather than learning, as market behavior is dynamic.

Technically speaking, agents themselves don't adapt or learn since their trading rule doesn't change during their lifetime. (Trading rules only "change" by the replacement of old agents and their trading rules by new agents with new trading rules).

Do agents communicate with each other?

Agents in Adaptive Modeler currently do not directly communicate with each other. Agents are not connected in any network topology nor do they swarm. This is still being researched. Of course agents indirectly exchange information through the Virtual Market and also through the breeding process.

Are there different types of agents?

No different types of agents have explicitly been defined in Adaptive Modeler. So there are no specific agents defined for giving buy or sell signals, no broker or market maker agents, no long term or short term agents, etc. However, agents all have their own trading rule directing their trading behavior in different ways. Therefore, groups of different types of agents (in terms of their trading behavior) may emerge through evolution. This can be observed with agent distribution data series that reveal trading style characteristics such as Trade Duration Distribution, Volatility Distribution and Beta Distribution (either in distribution charts or in the Population window). For more details, see the User's Guide.

How does market making take place on the Virtual Market?

The Virtual Market is a simulated double auction market where all buy and sell orders from agents are collected. Every bar, after all agents have evaluated their trading rule and placed their order, the clearing price is calculated. The clearing price is the price at which the highest trading volume can be matched. All matching orders are then executed at the clearing price. For more details, see the User's Guide.

Is price impact taken into account?

Yes. In the Agent-based model, the Virtual Market clearing prices are based on the order limit prices of the agents. Because of the volume weighted clearing price calculation mechanism, an agent offering a higher bid price increases the chance of its buy order being executed and thereby having an increasing effect on the clearing price (vice versa for sell orders).

Price impact may also be an issue in the Trading Simulator when (simulating) trading by amounts that may affect the real world market price of thinly traded securities. In this case price impact can be taken into account with the slippage parameter (on the Trading System tab of the "Model Configuration" dialog).

How are the forecasts and trading signals made?

Adaptive Modeler calculates a new forecast every bar. The forecast is normally based on the Virtual Market price. This is the clearing price of the Virtual Market calculated using the volume weighted pricing mechanism that includes all agent orders. The forecast is therefore based on the buy and sell orders of a large number of agents. As explained elsewhere, the forecast can alternatively be based on the Best Agents Price.

After every new forecast the Trading System determines if a new trading signal (long, short or cash) needs to be given based on the forecast, the last known security price and the Trading System parameters. A new trading signal will only be generated when the new suggested position differs from the last generated signal. For more details on how trading signals are being generated, see the Trading Signal Generator section in the User's Guide.

Does a model keep evolving when I add new quotes to the quote file?

Yes. There is no difference in the way historical quotes and new quotes are being processed. The model keeps evolving with every new quote. (The Evaluation Edition processes recent quotes with a delay of a few days).

Once the entire quote file has been processed, what do I do?

When a model has reached the end of the quote file, the forecast for the next bar has already been calculated. The forecast is shown in the Current Values window (if not, drag the Forecast dataseries from the Data Series window into the Current Values window). The trading signals can be seen in the Trading Signals window. The most recent signal is at the bottom of the list.

When a new quote is added to the quote file, it will automatically be read by Adaptive Modeler. The model will then evolve one step further and a new forecast (and trading signal if necessary) will be calculated. (The Evaluation Edition processes recent quotes with a delay of a few days).

Is there a difference between a "training" phase and an "operational" phase?

No. In fact, there are no "training" or "operational" phases in Adaptive Modeler. There is no difference between the way the model processes historical quotes and the way it processes new quotes. The model keeps evolving with every new quote that is added to the quote file. A model does not first repeatedly train or optimize on historical data. Every quote bar is experienced (traded on) only once by the agents.

Since it is possible to switch the Trading Simulator on and off, it could be said that an "operational" or "trading" phase starts when the Trading Simulator gets started. However, this has nothing to do with the evolution of the Agent-based model or the calculation of forecasts. The Trading Simulator and the generation of trading signals has no effect on the Agent-based model.

Trading

Does Adaptive Modeler give live signals for intraday trading?

Yes. If you use intraday market data, then intraday trading signals will be generated. (The Evaluation Edition processes recent quotes with a delay of a few days).

For what trading strategies is Adaptive Modeler suitable?

Adaptive Modeler gives trading signals for entering and exiting Long and/or Short positions based on the direction of bar-to-bar forecasts. (Short positions are optional. The "Cash" signal indicates exiting any Long or Short position).

The built-in Trading Simulator simply uses these signals to always enter Long/Short positions for 100% of total equity (wealth) and holds these positions until the next signal is given.

For actual trading however, the user may decide alternative position sizing and exit rules based on personal trading and risk preferences. Because signals tend to switch frequently, Adaptive Modeler is suitable for day trading or swing trading strategies amongst others.

Some parameters are available to specify how forecasts are translated into signals such as Allow Short Positions, Significant Forecast Range and FDA filter. These can be found on the Trading System tab of the Model Configuration dialog. To use Adaptive Modeler's forecasts and signals for other specific trading strategies, forecasts and signals (and other data) can be exported for further processing by other applications.

How can I get less frequent signals?

Adaptive Modeler attempts to predict the direction of the bar-to-bar price changes. Since in most financial markets this direction typically changes frequently (almost every bar), new signals are usually generated frequently. By changing the Significant Forecast Range (on the Trading System tab of the Model Configuration dialog) too small and/or too large forecasted price changes can be ignored. Also an FDA filter can be used, to only generate trading signals when Forecast Directional Accuracy is above a given threshold. If you want to use other strategies or filters for processing forecasts into trading signals or for post-processing the signals, then Adaptive Modeler's forecasts, signals and other data can be exported for further processing into trading signals by other applications.

What prices does the Trading Simulator use to enter/exit positions?

The Trading Simulator executes simulated trades at the closing price of the last received bar (or last tick price), adjusted by the spread and slippage values specified in the Trading System parameters. When in reality it is not possible to trade at the closing price (for example when using daily quotes and there is no after-hours market), the slippage parameter can be used to account for an average expected price change from the closing price to the next opening price.

Can Adaptive Modeler send orders to online brokers?

See Does Adaptive Modeler support order routing to online brokers?

Can Adaptive Modeler be used for High-Frequency trading?

See What quote intervals are supported?

Features

Can I edit an agent's trading rule?

It is not possible to directly edit an agent's trading rule. It is however possible to influence the creation of trading rules through genetic programming by specifying what functions ("genes") are to be included/excluded and by changing size and depth limitations of the trading rules. To do this, go to the "Genomes" tab of the "Model Configuration" dialog.

Can I use my own trading algorithms through an API or scripting language?

This is not possible. If agents would be able to use trading logic written in an external (unknown) language, genetic operators such as crossover and mutation could not be applied anymore. Also the control and calculation of genome statistics would become problematic.

Can I change the selection criteria for breeding and replacement of agents?

The selection criteria for breeding and replacement are the Breeding Fitness Return and the Replacement Fitness Return. These can not be changed currently. It is possible however to change the method of selecting the best agents for breeding. This can be either truncation (default method, maximum selection pressure) or tournament (adjustable selection pressure).

Can I import multiple time series into one model (multivariate analysis)?

Yes, in the Professional Edition agents can use data of up to 100 additional input variables that are included in the quote file. This can be any data that could help forecasting such as fundamental data, economic indicators, other price series or technical indicators. See the section Custom input variables in the User's Guide for how to set this up.

Can I forecast multiple securities in one model?

Adaptive Modeler currently creates "single security" models. So only one security can be forecasted by a particular model.

Can I automatically create models for several securities and get the results?

Yes. This is possible through the Batch function (from the "Tools" menu).

Does Adaptive Modeler support options and futures?

Any quote data that meets Adaptive Modeler's quote data format requirements can be fed into Adaptive Modeler but no specific functionality is included for futures, options or other derivative instruments.

Is multibar or multi-timeframe forecasting possible?

Adaptive Modeler forecasts one bar (or tick) ahead. There is always only one forecast, and that forecast is intended for the next bar (or tick). The effect of forecasting multiple bars ahead could be imitated by creating a separate model that uses a longer quote interval.

Integration

Can I export data from Adaptive Modeler to other programs?

Yes. All data series (including forecasts and trading signals) can be exported to a CSV file (comma separated values) using the Export function from the "Tools" menu.

Can I export data to Excel?

After exporting data to a CSV file, the CSV file can be opened or imported with Microsoft Excel or any other program that can read CSV files.

Does Adaptive Modeler connect with databases?

No, Adaptive Modeler does not connect with databases.

Does Adaptive Modeler support order routing to online brokers?

Adaptive Modeler does not support automatic order placement with online brokers. By exporting trading signals to CSV file, orders could automatically be generated and sent to a broker by another application.

Can Adaptive Modeler be integrated into MetaTrader4?

No, this is not possible. However, it is possible to import .CSV quote files exported by MetaTrader4 into Adaptive Modeler.

Is there an API or DLL to use this technology in my own programs?

No, this is not provided.

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