4 modes: building, re-testing, improvement, optimization
Build a stragegy, re-test it for a different market or timeframe, improve it by adding other conditions and optimize it.
Use of Monte Carlo testing to test quality of the strategies
Monte Carlo testing allows you to find out how good the quality of the strategy is and if it has potential for a stable profit when trading real money.
Wide options for filtering strategies
It is easy to determine what you expect from the strategy. How much it should earn, what the maximum risk is, profit factor and other indicators.
Built-in walk-forward optimizer and cluster analysis tools
This tool allows you to find out what results the strategy has when optimized regularly and finds the ideal time period for which to optimize the parameters.
It includes more than 40 indicators, patterns, etc.
It icludes indicators, candlestick patterns, time values, it allows to filter strategies into timeframes and many others.
4 entry orders, 7 output orders and intelligent stop loss
Enter at Market, Stop, Limit, Reverse. Adaptive SL and PT, Trailing stops
Fast and accurate backtests on tick data
StrategyQuant includes a backtester that can perform testing very quickly and accurately. It supports tick data and allows to use multiple processor cores simultaneously.
In sample and out of sample testing
With StrategyQuant you have the ability to divide the history data to In Sample and Out of Sample parts.
To have a good quality strategy that is profitable in the future as well, it has to be built on in sample data and it should also include an out of sample period when the strategy is just run and thus tested if the setting works for future use as well. StrategyQuant allows very flexible setting of these periods.
Walk-forward optimization and cluster analysis
The optimizer supports the typical optimization, walk-forward optimization and cluster analysis. Cluster analysis which is referred to as walk-forward matrix in the software allows to find out how robust the strategy is when regularly optimized and therefore if its history says something relevant about its future development. This allows to find out if regular optimizations of the parameters improve the results and to find an ideal optimization period.
It supports many kinds of data and timeframes
StrategyQuant works with practically any data: forex, stocks, futures, ETF, energy… It supports timeframes M1, M5, M15, M3, H1, H4 and D1.
It allows to import data in any text format, when a usual text format is used it's able to determine the structure of the data, in other cases it's easy to define it.
It also allows to import tick data.
During optimization 3D charts are available that let you see how the strategy behaves with different parameters. This makes it easy to identify areas where the strategy is most stable and to set correct parameters.
In the editor you can create your own strategy and then backtest it, improve it and optimize it. The user can choose to define the boundaries for desired risk:reward ratio to produce strategies that match these boundaries.
Export to MetaTrader Expert Advisor (in MQL4), NinjaTrader NinjaScript strategy (in C#) and Tradestation EasyLanguage strategy
Generated strategies can be exported as strategies in full source code to selected platforms. There are no hidden or protected parts, it contains fully readable source code.
Export to pseudo-code
As an additional option, the trading strategy rules can be exported to pseudo-code – human readable description of trading rules which allows you to trade the strategy manually, or implement it in a language of your choice.
The strategy database stores all strategies found by StrategyQuant. You can choose its size, whether you want to store 100, 500, or even 4968 best results.
Two methods of development
In this method the first population of strategies is built and then the strategies are developed using knowledge of evolution. For a trader this is an easy procedure to get the best results possible. The entire process runs automatically and your task is only to evaluate the results.
A very interesting mode that takes randomly different building blocks and creates strategies from them. It can take more time to find an interesting strategy than using the genetic evolution method but the main advantage of this approach is that it isn't limited in any way and therefore can find practically anything.
Division of data into 3 parts
With this function you can (but you don't have to) divide your data into 3 parts: 2 sets of in-sample data and a set of out-of-sample data. It gives you a better comparison of equity and for example those with one loss part can be automatically removed from the development. This makes the whole process faster, more efficient and it saves you a lot of time.
Supported Indicators and Building Blocks *
- Simple Moving Average
- Exponential Moving Average
- Triple Exponential Moving Average
- Weighted Moving Average
- Commodity Channel Index (CCI)
- Relative Strength Index (RSI)
- Bollinger Bands
- Keltner Channel
- * Custom Indicators
- Williams % Range
- Parabolic SAR
- Linear Regression
- Qualitative Quantitative Estimation (QQE)
- Average Directional Movement Index (ADX)
- Average True Range (ATR)
- True Range
- Price Difference
- Highest, Lowest
- Open Daily
- High Daily
- Today Open
- Heiken Ashi Open
- Heiken Ashi High
- Heiken Ashi Low
- Heiken Ashi Close
- Low Daily
- Close Daily
- This Bar Open
- Bullish Engulfing
- Shooting Star
- Dark Cloud Cover
- Piercing Line
- Bearish Engulfing
- Crosses Above
- Crosses Below
- Closes Below
- Addition (+)
- Subtraction (-)
- Multiplication (*)
- Not Equals
- Closes Above
- Close Above/Below Bollinger Band
- Close Above/Below Parabolic SAR
- Short Term RSI Above/Below 50
- Short Term Stochastic Above/Below 50
- Short Term CCI Above/Below 0
- MACD Above/Below 0
- Volume Above/Below Average
- Long Term RSI Above/Below 50
- Long Term Stochastic Above/Below 50
- Long Term CCI Above/Below 0
- Volume Above/Below Average
- Day of Week