Expert Advisor Studio Crack
Charolette Antosh
Traders often face challenges in using good trading tools, as they are typically not free. Even when open source tools are available, traders may need advanced technical skills, struggle to find good expert advisors, and face integration challenges.
This EA code generator is easy to use; no documentation or tutorial is required. However, understanding how to generate EA source code is not sufficient to create a good expert advisor. Knowledge of the SDK and how to call APIs is crucial. Refer to our tutorial series: Expert Advisor Tutorial, and check our SDK document for further reference.
For sophisticated traders interested in Arbitrage Trading or HFT (High-Frequency Trading) related to FIX API, Fintechee integrates its Expert Advisor Studio with the FIX API trading platform individual version. Learn more about:
The EA is an expert advisor for scalping and averaging in on the M5 chart of XAUUSD. This powerful tool continuously scans the market, identifying high-quality trading setups based on specific trading hours / trading sessions. The entry model is a collaboration of both candlestick patterns and indicators.
Through extensive testing and a 5 year Dukascopy data optimization, the EA has shown an impressive strike rate of 100%. In the rare event that a trade goes against us, the EA can employ a highly intelligent averaging in management system to mitigate potential losses. Our advanced profit factor system ensures that the EA strives to exit with a higher profit when averaging in is utilized.
Tailor the EA to suit your trading preferences many customizable filter parameters. Such as implement stop losses to breakeven, trailing stop loss, protection against weekend slippage, set maximum equity stopout, and more. These versatile settings allow you to fine-tune the EA according to your specific requirements.
There are a range of very powerful data mining programs available in the market place such as Expert Advisor Studio (EA Studio), Strategy Quant X and Adaptrade Builder which allow the user to quickly generate thousands of algorithms using random and genetic generation methods. Each of these technologies have particular strengths and weaknesses and it is not the intent of this post to make a personal recommendation about which to use. In fact we use multiple solutions personally ourselves as any of these options are excellent value for money and each provide some unique extra bells and whistles which you may want to use to augment your own workflow processes and take data mining to the next level. For the purposes of this post however, we will be showcasing EA Studio.
It is important to note from the outset that we engage a different workflow process than is traditionally used with data mining models.The reason for this departure from more traditional data mining methods and in particular the robustness testing measures adopted, is due to our desire to target divergent as opposed to convergent strategies in our data mining efforts.
This therefore necessitates that our processes seek individual trading strategies that trade relatively infrequently, have relatively low sample sizes and visually display very stepped and volatile equity curve profiles that are representative of divergent equity curve signatures. The stepped nature of the divergent equity curve can be attributed to the occasional rapid growth of the equity curve during divergent market conditions inter-dispersed with long periods of inactivity between divergent phases which incur building drawdowns and periods of extended stagnation.
To ensure that our data mining operations are directed towards finding suitable divergent strategies we first need to strictly define the logic space within which strategies are randomly or genetically defined. This first step to the workflow process is essential as this is where the broad fundamental principles of divergence are established to ensure that all strategies have these broad fundamental principles embedded in them. These essential fundamental principles common to all divergent strategies are as follows:
The minimum and maximum pips defined for the stop and trail condition are broadly defined by eyeballing the chart for the appropriate instrument and timeframe in MT4 and using the cross-hair tool to define a realistic trailing stop range.
Establishing an open profit condition associated with a trailing stop helps to select those strategies in the data mining process that possess positive skew. We eliminate all generated strategies that possess negative skew given their ability to significantly compromise the risk exposure of the consolidated portfolio.
We use a preset entry rule which is applied to the reactor for all strategies to ensure that we are more likely to be entering our trades within divergent market phases. In this example for the D1 timeframe on any instrument we use a 200 SMA condition that must be met for all strategies.
In addition to the preset entry condition we need at least 1 additional entry rule and 1 exit rule. Ideally we are after a maximum of 2-3 entry conditions including the preset SMA condition and a single exit condition for both long and short symmetrical strategies. Strategies with too many variables significantly reduce the overall robustness of a strategy to handle variable market conditions and tend to be specifically configured to a single market condition. We therefore opt for those strategies possessing fewer strategy variables.
We restrict the indicators used for data mining divergence to traditional trend following and momentum indicators and signals that are easily understood and commonly applied by trend traders such as Average True Range, Donchian Channels, Moving averages and Moving Average Crossovers, MACD signals.
Here is a screen shot of the selection of indicators used for entry and exit signals. We have simply excluded those indicators which we feel do not significantly add value in detecting trend following/momentum breakout strategies.
When selecting a data horizon for data mining strategies for a particular instrument and timeframe we always select the greatest available date range from available data. For robustness purposes the intent is to ensure all validated strategies generated have navigated as many different market conditions available as possible.
Preferably you would also test strategies using the multi-market feature available to the data mining platform, however this is only possible if individual instrument volatility is standardized using measures such as fixed fractional position sizing using the ATR. Unfortunately, EA studio is currently limited to using a standard lot sizing per instrument which restricts our ability to use the multi-market feature.
When using data mining programs, multi-market testing is a very powerful feature to assist robustness testing. For data mining purposes, different instruments simply represent different market conditions. It is all just data. As a result, if you have the ability to test across multi-markets using volatility adjusted position sizing methods, then it is strongly advised as this effectively significantly extends the data horizon for testing purposes.
Furthermore, given that we monitor live trade results against test results as an ongoing workflow process once we are trading live, using a common broker data source helps to ensure that material differences in data source between live and walk forward results are eliminated or significantly reduced.
Given that we are realistically targeting divergent strategies with only a weak edge, it is essential that we reduce where possible the frictional costs of trading such as spread and slippage. The material impact of spread and slippage on performance of a trading strategy is significantly higher as you progress to the smaller timeframes as trade frequency from scale variance increases. To reduce this impact we stick to the higher timeframes of H4 and above.
Over-trading a single instrument is a significant obstacle to the systematic trend/momentum trader as divergent market conditions are few and far between. To overcome this impediment, we therefore elect to diversify across many different instruments which therefore allows us to increase our total trade frequency at the global portfolio level. With say 3-5 trades per year per instrument, when trading a portfolio of say 200 discreet return streams, this therefore lifts total trade frequency at the portfolio level to say 600-1000 trades per year. Diversification is therefore essential to data mine for infrequent and unpredictable divergent market conditions.
We do not use the Walk Forward Testing and Optimisation given the stepped nature of divergent strategies with extended periods of stagnation. Under divergent data mining techniques, the underlying strategy performance directly responds to market conditions. If market conditions are divergent n nature, then strategies generated should perform well, whereas during convergent or noisy market conditions, strategies should either be inactive or stagnating with not too much deterioration of capital.
We have attached a set file for EURUSD D1 that is applied to the majority of instruments in our trading universe. The only differences to this generic set file which is applied to each instrument relate to chosen min pips and max pips used for stop and trailing stop definition based on individual instrument volatility. Note that this set file relates to the date range used for Pepperstone data between 1st Jan 2000 up to 31st Dec 2015 and includes a 30% OOS component.
Now that we are armed with a virtually unlimited swathe of different divergent systems, we also use market diversification as a means to spread our systems far and wide across asset classes to search for divergent market conditions.
The overall result of diversifying across 25 markets with approximately 30 unique divergent systems for each instrument means that the result at the portfolio level is heavily diversified with over 750 unique long range return streams to compile at the portfolio level.
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