Table of Contents of StrategyQuant review
Introduction: What is StrategyQuant?
StrategyQuant is one of the best algo-trading/strategy development platforms available. I have decided to write an extended StrategyQuant review because more than 90% of all strategies I run on my MT4 are developed using this single tool. After many years of programming and testing for myself and also for other traders, I am 100% sure this is the proper way to go. So, what is so special about StrategyQuant? The idea is simple: StrategyQuant is based on an automatic (random or pre-configured) strategy generation using custom or standard chart indicators. For example,during configuration, when you define that you want to find and test all possible trading strategies using “Moving Average,” “RSI” and “Bollinger Bands” indicators, StrategyQuant will start generating and testing all possible combinations of all those indicators. This is just a simple example, since in the latest version (StrategyQuant X), it is also possible to add your own custom indicators and trading signals; the number of possibilities is simply unlimited.
StrategyQuant X (new 2023 version!) options are:
- Automatic generation of strategies based on most common trading logic-blocks and also using standard and external indicators
- Random strategy generation or generation by genetic evolution
- Advanced strategy testing and optimization, to avoid curve fitting
- Walk forward optimization
- Automatic source code generation. No programming skills are required
- External tick-data support (Including Dukascopy 99% accurate tickdata)
- Multi currency backtesting
- Multi strategy testing
- Multi-core CPU support!
- Optimization of existing strategies
- All great features of SQ 3 + more new great features
- Multi-market and multi-timeframe strategy generation
- Automatic historical data import (download tick data with one push of a button)
- Extendable with your own indicators and strategies
- Displays trades on chart
- Built in strategy editor (algo-wizard)
- Improved user interface
- Stock-picking strategies!
- Many more…
The figure below represents a basic typical workflow of StrategyQuant:
1. Strategy generation
In this step strategies are generated. StrategyQuant has several options of trading system generation like: random or genetic.
Each option can be preconfigured with manually selected indicators and price-action and/or logical operators. In this step users can define desired trading symbols and timeframe. The historical data can be loaded directly from a MT4/5 export or (in new SQ X) automatically downloaded from Dukascopy servers.
2. Strategy validation and ranking
In this design step the historical data is divided into two regions: in-sample for strategy design and out-of-sample for strategy validation on “unseen” data. But that’s not all. Users can also define their own validation pass/fail criteria, used to select only the best and most stable strategies. The most interesting criteria are: Return to DrawDown ratio and also profit-stability. For example if any generated strategy passes those two criteria it will be automatically placed in a local databank for further analysis. Moreover users can define many other validation checks to take into account only the top-best generated strategies. Remember that not all generated strategies will eventually be stable enough for live-testing. You need to generate at least a few hundred strategy candidates during this step.
3. Retesting step
This step is one of the most important steps in the design process. Here all strategies need to be validated against several different robustness tests. Note that robustness is all that matters in auto-trading (not the highest profits as many would think). In the end we need to have a portfolio of several different strategies that will remain stable in the long term. The most common robustness test (one of many possible tests) is the Monte-Carlo analysis (= strategy input parameters sweep). In this step the SQ platform will randomly ‘jiggle’ all input parameters for each generated strategy and will generate a Monte-Carlo output graph. See figures below for an example of poor and good strategy robustness:
Example of good robustness.
In the end we need to save only the most robust strategies that will pass all of the defined robustness tests: Monte-Carlo, start-bar/date sweep, skipped trades test, randomizing trader order test etc…
4. Strategy export
After the strategy design process the selected strategies and their corresponding settings can be exported directly as MQL source-code to an MT4 or MT5 or Ninja Trader platform. This option allows direct import of your strategy to your favorite trading platform.
5. Forward testing
This step is the most important part of the whole process. Here we can find out if our strategies are making money or not.
This step requires some patience and discipline but also a proper monitoring tool. During this step we need to continuously compare our live/demo forward results with the results as seen in our backtests (in and out-of-sample period). Typically all systems will use minimal Lot size (e.g.: 0.01) or will run a a demo account, until proven to be profitable. The goal is to build a portfolio of several different strategies running on different symbols and timeframes. If we observe something unusual like a DrawDown that is significantly larger than seen during a backtest, we will shut down the corresponding system and go back to the ‘drawing board’ to see where our design process has failed us.
StrategyQuant review with results
In this section I’ll also try to ‘quickly’ demonstrate the power of StrategyQuant by showing one example of strategy design on EURUSD H1. Note that this is just a ‘top of the iceberg’ to quickly demonstrate the capabilities of the SQ platform. Before considering buying this tool you need to know one thing: finding profitable strategies is like trying to find a needle in a haystack. It takes many hours of generating, testing and validating before you will find a good robust strategy. StrategyQuant is not ‘push button and make money’ product! It is meant for professional traders, quants and for people who are really interested in algorithmic trading. That being said the following example is for EDUCATIONAL PURPOSES ONLY.
STEP 1. Strategy generation
For this example we will concentrate only on strategies based on the following few standard indicators: Williams % range, TEMA, Bollinger Bands Width Ratio, ATR. Additionally we will use standrard arithmetic operators like: “+,-,/,*,=,!=, etc..” and a few price action blocks: open, close, high, low etc…
The EURUSD H1 historical data comes from Alpari broker (directly exported from the MT4 history center), and the original data range is from 2004.01.01 to 2018.10.19. In order to speed up strategy generation we will only concentrate on data between 2005 to (and including) 2018. However in the first step we will split this data into two ranges: ‘in-sample’ range: 01.2005 to 02.2016 and ‘out-of-sample’ 02.2016 to 12.2017.
– we will start from 2005 in order to also include the financial crisis period during 2007/2008.
– we will not use 2004 and 2018 for now, this period is reserved for additional validation during the strategy retesting step.
We will use standard genetic generation mode (where strategies will be randomly generated). The Lot Size is set to 0.1 and start capital is $1000. Furthermore all standard trade management modes will be used for strategy generation: StopLoss, TakeProfit, Breakeven, TrailingStop, ATR based stop, close after X bars, etc…
Money management settings
STEP 2. Strategy validation and ranking
This step is done automatically during strategy generation. Each strategy that passes all defined tests during the ‘in-sample’ period is also automatically retested and validated during the ‘out-of-sample’ period. This great feature allows to pre-filter the good strategies from the bad already during the generation period. The resulting good (interesting) strategies are automatically stored in internal databank for further analysis.
STEP 3. Strategy retesting
During retesting we will concentrate on the following characteristics: strategy robustness, Monte-Carlo analysis of selected parameters, the resulting Return to DrawDown ratio and overall stability of equity curve rise. This is the last step in our design flow, here we need to select interesting candidates which have the best chances of making profit during the forward test period.
Monte carlo test 1 settings
For example the following strategy has shown stable results across all stability checks and also profitability in ‘out-of-sample’ period. Also during 2018, which has not been used for strategy generation at all.
Robustness test graph
The strategy presented above is just an example of several good strategies found during this one generation run.
If you consider purchasing StrategyQuant and you use my personal 20% discount, you will get all SQ
settings and generated strategies from this particular example. Read more in the next section.
StrategyQuant trading results:
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If you have ever considered automated trading, just forget other expensive commercial EA’s and create your own profitable trading strategy!
And yes, you can test StrategyQuant for free!
Moreover if you consider buying this great tool, you can get up to 20% discount by using my discount
coupon code: COENSIO-20-OFF-ZSHXY. I must admit, this tool is not cheap, but it is really worth every single penny. Imagine how much money you can save by designing your own profitable EA and stop spending money on third-party strategies that almost never work 😉 You can get your discount just by using the following button:
Coensio deal package:
If you purchase StrategyQuant using my coupon code, you will get:
- StrategyQuant X versions (standard deal).
- My free ebook explaining how I build my profitable strategies on micro-futures.
- Access to our Coensio forum and private discord, where we can exchange trading ideas and learn from each other.
Join our StrategyQuant Discord