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Quant ML SP500

Live monitoring: https://www.mql5.com/en/signals/2252642

Quant ML SP500 is a Bot for S&P 500 related instruments (H1 Timeframe):

  • .US500Cash CFD
  • ESUxx futures,
  • SPY CFD,
  • anything with an underlying S&P500 index in it (exact instrument name could differ broker-by-broker).

The bot has been generated and validated by machine learning algorithms with an emphasis on stability of the strategy:

  • Uses only market entries with fixed ATR-based SL and TP levels.
  • No trailing stops, no breakevens, no martingale, no grid, not adding additional positions when the first position goes in red. 
  • Has been tested and validated both on in sample (IS) data, as well as out out sample (OOS) data.
  • Additionally has been forward tested and validated manually on a data outside of both IS and OOS. 

The idea behind the strategy is for it simply to keep working year after year.


    

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The original price is $399. The $300 promotion ends in 24 hours! Gold has risen 25% in about a year, which is crazy, so I manually placed a long Sell order on August 23. This trade once brought a profit of $15, but I thought it was possible to get a bigger profit, so I chose to hold it. Gold continued to rise, but I would not change my judgment that gold would return to $2,400. Don't be confused, this is a manual order, EA's orders usually stop loss at $1--3, and rarely may trigger a maximum st
Boring Pips MT5
Thi Thu Ha Hoang
4.71 (28)
Have you ever wondered why most expert advisors are not effective in live trading, despite their perfect backtest performance? The most likely answer is   Over-fitting . Many EAs are created to ‘learn’ and adapt perfectly to the available historical data, but they fail to predict the future due to a lack of generalizability in the constructed model. Some developers simply don't know about the existence of over-fitting, or they know but don't have a way to prevent it. Others exploit it as a tool
GOLD CatBoost Distans MA 3 ST V1   EA works on the Catboost algorithm. The technology described in my articles is used, which you can read below: Experiments with neural networks (Part 1): Revisiting geometry   https://www.mql5.com/en/articles/11077 Experiments with neural networks (Part 2): Smart neural network optimization   https://www.mql5.com/en/articles/11186 Experiments with neural networks (Part 3): Practical application   https://www.mql5.com/en/articles/11949 Experiments with
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