Discussing the article: "Build Self Optimizing Expert Advisors in MQL5 (Part 4): Dynamic Position Sizing"
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Check out the new article: Build Self Optimizing Expert Advisors in MQL5 (Part 4): Dynamic Position Sizing.
Successfully employing algorithmic trading requires continuous, interdisciplinary learning. However, the infinite range of possibilities can consume years of effort without yielding tangible results. To address this, we propose a framework that gradually introduces complexity, allowing traders to refine their strategies iteratively rather than committing indefinite time to uncertain outcomes.
Fig 1: An image of our Bollinger Band Strategy in action
Our trading strategy is based on following the trading signals proposed by John Bollinger. The original rules of the strategy are satisfied if we sell whenever price levels breach the top Bollinger Band, and we will buy if price levels fall beneath the lower band.
Generally speaking, we can extend these rules to also serve as our exit conditions. As to say that whenever price levels appear above the uppermost band, we will close any buy trades that may be open in addition to opening our sell trades. These sets of rules are enough to create a self-managing system that knows when to open and close its positions on its own.
We will test our trading strategy on the GBPUSD Pair from 1 January 2022 until 30 December 2024 on the M15 time frame.
Author: Gamuchirai Zororo Ndawana