Experiment 4

This week we started the next experiment in the series.  The results of Experiment 3 were inconclusive, and now we’re setting out to investigate additional capabilities of the EAs.

Up until now we were placing trades at a constant 0.1 lot size.  For Experiment 4 we’ll use the EA’s predicted standard deviation envelopes to determine the lot size.  In order to accomplish this, we’ll hold the bet amount at a constant $100.  This means that when the SL (set at 1 SD) is larger, the lot size will be smaller and on the flip size, we’ll bet larger lots for a smaller SL.  This results in larger bets as the EAs have more confidence in a move.

We’re also increasing the PN entry trigger to 0.3 to try and reduce false entries.  In addition, we’re increasing the limit of open orders to 5,000, to be closed either by TP, SL or change of bias.  Other than that, the EAs are set up consistent with Experiment 3 as shown:

Where:

Mode A is the original operational mode previously studied.  The entry trigger is PN=0.3, and statistics are segregated by session and day of the week in addition to indicator states
Mode B is the same as A, but there is no day of week segregation.  Only the session data is kept separate
Mode C uses no segregation between day of week or session.  The only data bins are those defined by the indicator states.

This experiment is now complete.  Detailed results are here.  It indicated that MACD-A was the most profitable setup.  It also indicated that the most profit was realized for MACD-A with an entry PN = [4.5 , 5.5].

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