Where is your edge?
Alyuda Tradecision provides with state-of-the-art artificial intelligence technologies that learn from historical data to help you find out the most auspicious times to buy and sell securities.
Advanced Neural Network Models
Four steps of New Model Wizard
Model Builder features
Model-based strategies
Analyze model quality
Prevent curve-fitting
Neural networks proved to be the best technology to predict (to some extent) future prices. All other statistical forecasting approaches perform much worse in stock price forecasting. But neural networks are not a “silver bullet.” They should be used consciously. Neural networks base their predictions on price data patterns they discovered in past data during their training.
The Tradecision Model Builder helps create successful neuro-models, use them in your strategies and thus make better trading systems than others. 출장마사지
Alyuda Tradecision interface is very easy to use. In fact you don’t need to have a mathematical or artificial intelligence background to work with wizard-based creation of neural models.
Another AI technology that proved to excel in optimization tasks is Genetic Algorithms. Alyuda Tradecision uses this approach to provide professional traders with bigger optimization power while creating trading systems. Genetic Algorithms are also used to optimize neuro-model inputs and their parameters.
Advanced Neural Network Models
Tradecision lets you exploit adaptable and precise constructive neural networks for price forecasting. Constructive networks are new state-of-the-art nets unlike any others you may have seen or tried before. These networks grow and train themselves during iterative price data analysis. 출장마사지
Neural model will analyze the symbol data of the current chart and forecast the specified number of bars ahead. A new neural model can be easily created using New Model Wizard that guides you through the process step by step.
Four steps of New Model Wizard
Step 1: Selecting target and lookahead
At this step you need to define model target and specify how far into the future you want the model to predict.
Step 2: Selecting training and test data ranges
At this step you need to define how much data will be used for model preparation. You can also manually allocate the portion of these data to be used for walk-forward testing.
Step 3: Selecting model inputs
At this step you need to define model inputs. You need to select indicators you think contain valuable data to predict model target. New Model Wizard can find optimal parameters for indicators that you use as inputs. 신용카드현금화
Step 4: Selecting strategy rules for performance testing
To test a neural model you need to define a strategy based on the model forecasts (model-based strategy).
Trading strategies produced by New Model Wizard are based solely on the model forecasts. These strategies produce buy/sell signals that depend only on model forecasts and therefore allow testing model performance only without affecting money management and indicator-based rules.
Model Builder features
You can analyze over 100 model performance figures trades generated by the model-based strategy.
You can verify statistical measures of the model forecasting quality with the Model Performance Report.
With Input Importance Chart you can understand relative importance of each model input.
Actual vs. Forecasted Graph displays a line graph of the actual and forecasted target values for all bars from
the training or test period. 신용카드현금화
Model-based signals can be inserted on the main chart for visual analysis with one click.
All models are automatically saved into internal database. At anytime you can edit, re-train or delete a model
stored in the database.
Model-based strategies
To test a model, Alyuda Tradecision applies a simple model-based strategy to historical data and finds out whether you would have made money had you traded based on that strategy. If you find that it was profitable and fits your trading personality, you can feel confident in using the model to make future trading decisions. 신용카드현금화
A major advantage of model testing using a model-based trading strategy is a possibility to rigorously test the model over different time and with different parameters.
To help you thoroughly analyze a model-based strategy Tradecision creates a Strategy Performance Report. The report provides detailed information about your strategy, including return on account, drawdowns, risk ratios, number of profitable/losing/outlier trades, as well as timing analysis. To ensure that the figures of the Strategy Performance Report are as close to reality as possible, you can factor in broker’s commissions and slippage.
For better performance, your strategy parameters can be optimized using Genetic Algorithms (GA) or exhaustive search.
Visit Trading Systems page for more details about strategy optimization.
Analyze model quality
With Model Performance Report you can analyze the quality of neural model forecasting using statistical ratios and graphs. You can also identify the reason of bad performance of your trading system. If the report ratios are good, you should rather change your strategy settings. If the report ratios are bad, you should improve your neural model.
The Model Performance Reports provides separate figures for training and walk-forward testing periods. The figures for walk-forward testing are much more important since they represent the model quality you’ll have when you use the model with new data. Usually, model performance with training period is better than with the testing one. A good model should have good performance with both periods. 신용카드현금화
Prevent curve-fitting
Curve-fitting is a dangerous shortcoming of neural network over-training. When a network over-trained it doesn’t “learn” price patterns or develop the ability to generalize, but simply memorizes historical price data or, in other words, fits its output to the price curve without learning internal dependencies.
An over-trained network can produce good results with a training set, but performs unexpectedly badly when used with new data.
Tradecision uses a special algorithm to identify a clear trend of over-training. It monitors validation error dynamics and training progress from different points of view.
