Scrptx EurJpy H1 S4
The trading robot has been created by an artificial intelligence and is adapted for trading EURJPY on the H1 timeframe. It uses a set of classical indicators and a filter - the author's version of BBands which is based on classical Bollinger Bands but is drawn as a line. This filter allowed to achieve a significant reduction in the number of false entries and helped to significantly optimize the exit point.
Features of the Trading RobotThe following results were obtained driing tests:
- The average profitable trade in testing exceeded the average losing one 2 times;
- Number of profitable trades ~50%;
- Expected payoff over 100 points (5 digit) per trade;
- SL/TP for every trade;
- NO martingale;
- NO averaging of losing positions;
- Backtests with 99% of modeling quality and variable market spread.
- Forward testing results on a real trading account are similar to backtests.
- For trading on real accounts, I strongly recommend using high-performance low-delay VPS and low-spread brokers;
- It is recommended to use a fixed risk in USD, which does not exceed 5% of your trading capital per trade.
- It is not recommended to use the EA on other currency pairs and other time frames.
- RiskInPercent - risk per trade, as percent of stop loss to deposit amount;
- MaximumLots - maximum allowable lot value per trade;
- UseFixedMoney - true/false using a fixed risk per trade (stop loss in currency is set in the RiskPerTrade_USD parameter), or use a variable risk (deposit based stop loss as a relative value is set in RiskInPеrcent);
- RiskPerTrade_USD - stop loss in currency (absolute);
- MaxSlippage - maximum allowable slippage in points (4th decimal place for 5-diti quotes), above which the deal will be skipped;
- CustomComment - a comment to a trade;
- MagicNumber - the magic number, based on which the EA will control and manage positions;
- EmailNotificationOnTrade - true/false - using email notifications about trading operations.
About the developer
A team of professional developers that has been engaged in the creation of artificial intelligence for the last 10 years, with the active participation of the risk manager of a large hedge fund, has adapted the existing self-learning algorithm for automatic search and testing of trading ideas in the financial markets. The developed software allows you to search and automatically perform classical backtests for over 15 years at a speed of more than 5000 tests per minute, which greatly (by tens of thousands time) exceeds the classical methods of trading strategy testing.
The risk manager suggested over 1000 different source instruments as the basis for finding strategies. The combination of these instruments on different timeframes with different parameters provides a large selection of unique trading strategies.