Trading Simulations. Tradecision Performs Realistic Trading Simulations

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Tradecision Performs Realistic Trading Simulations

Before you risk your trading account, Tradecision can perform a realistic trading simulation and inform you about the performance of your trading ideas. Tradecision introduces the true, rigorous and sound methodology of evaluating the trading system robustness.

The state-of-the-art Trading Systems tools

Strategy Builder lets you manage your trading strategies

With Tradecision Strategy Builder you can create a workable trading strategy, reliably measure its profit potential and risk, and then historically test it to see how it works.

You can create, edit, copy, and delete a strategy, insert strategy into a chart, and perform strategy optimization and simulations.

No matter what level of trading expertise you have, with the Strategy Builder you can quickly identify and eliminate the causes of bad performance of your trading system as well as improve your trading ideas to make them profitable.

In order to get a comprehensive analysis of your trading system, you have to define buy and sell rules, money management preferences and build your custom indicators using the Tradecision formula language.

For your convenience, the most popular money management rules are predefined and easily accessible with advanced point-and-click money management editor. Visit the Advanced Money Management page for more information.

Use a powerful and easy-to-use formula language

The Alyuda Tradecision Formula Language offers you a variety of opportunities to formalize and test you own trading ideas.

Tradecision includes over 120 built-in functions and indicators as well as detailed help system containing many examples and descriptions.

Tradedecision Neuro-Strategy tool

Tradecision offers an innovative artificial intelligence technology to support your profitable trading decisions.

With automatic data preprocessing and architecture selection algorithms the neural networks became easy enough for every trader to use - without expensive formal training.

Tradecision’s Neuro-Strategies allow you incorporating the state-of-the-art neural networks into your trading systems.

Neuro-strategy is a strategy, rules of which are based on neural network predictions. Conditions based on neuro-predictions can be combined with traditional conditions based on technical indicators within one rule. A neuro-strategy can be tested, optimized and used to generate buy/sell signals just like traditional trading strategy. Visit the Artificial Intelligence page for more details about Advanced Neural Network Models.

Tradedecision Simulations Manager

Strategy analysis is a vital stage of the strategy development process. When you insert your strategy into an active chart, a new simulation is automatically created and all performance reports are generated and stored in the Simulations Manager. This is the fastest way to simulate and analyze a strategy.

Tradecision offers advanced simulation capabilities designed specifically for professional traders to help them easily identify advantages and disadvantages of a strategy.

With the New Simulation Wizard you can simulate one strategy with different symbols at once. And even more, in one simulation you can back-test several strategies with one symbol in one run. In both cases you can easily compare results.
All this means you’ll have more efficient strategy analysis.

With the Simulations Manager you can easily create, run and store as many simulations as you wish. Simulations are stored in an express simulations list that can be quickly categorized or filtered. Any stored simulation can be inserted into a chart or analyzed with one click.

Advanced Strategy Optimization

Optimization is the process of performing multiple tests while modifying parameters used in strategy rules or indicators. The purpose of optimization is to discover the most profitable and best setting for a particular indicator or price pattern traded on a specific security.

Optimization can greatly improve your knowledge of each strategy and your competence about what works in different types of markets. It allows you to play with the parameters of your strategy without changing its main point functions.

Many traders wrongly use optimization to force an unfinished strategy towards completion. But if you use it correctly, optimization can be the key step that will prepare your strategy for real-time trading.

After you have tested the strategy and are confident enough about its profitability, only then it will be the right time to Start optimization.

You can optimize the strategy over a variety of different time periods and markets, and maintain its peak performance.

The Strategy Optimization Wizard provides a step-by-step guidance through the optimization process.

You can use optimization to make sure you are using optimal parameters for the indicators applied to your strategy and entry/exit conditions.

To find the optimal parameters, you need to replace numeric constants within the trading rules with optimization variables. You can select any name for an optimization variable, but it should be preceded by “#” character. For example, if you want to find the optimal moving average length, in your trading rule you need to write “SMA(Close, #length) instead of SMA(Close, 14).

Additionally, you need to specify the range (minimum, maximum) and step (increment) for each optimization variable. Please note that the more optimization variables you have and the more values must be tested for each variable, the longer the optimization process will last.
Apply the algorithm that suits your needs

Optimization process can take seconds, minutes or hours depending on the number of simulations being run. To reduce the time required for optimization, you can either reduce the number of optimization parameters or use the Genetic Algorithms for optimization. For the cases when you have many optimization variables and wide search ranges, Genetic Algorithms are much faster than exhaustive search while remaining very robust.

Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. They combine survival of the fittest among candidates with a structured yet randomized information exchange.

GAs combine the best characteristics of other optimization methods such as fast convergence that doesn’t depend on properties of optimization criteria (like smoothness) and robustness.

Exhaustive search verifies all possible combinations of optimized parameters and therefore can definitely find the best possible solution. But the time required for the exhaustive search increases rapidly when the number of parameters grows.

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