When to use holy grail system?

<p id="isPasted">The answer is simple. Instead of trying to build the perfect strategy that most profitably fits the historical data, take a step back, relax, and contemplate the big picture of how markets statistically tend to move. After all, as the holy grail is surely a robustly profitable trading strategy that never stops working, logic holds that it has to take advantage of a permanent and persistent “flaw” or phenomenon in the market. So forget about candlesticks and indicators for the time being, and think about speculative markets. What phenomena do they exhibit that might be exploited by the trader? There are two that are common and repetitive:</p><p>Mean Reversion – after the price pulls away from a longer-term average price, sooner or later it always returns to the average price, which is another way of saying “what goes up, must come down”. Fat Tails within the Returns Distribution Curve – in plain language, markets tend to overreact, rising and falling excessively due to the human sentiments of greed and fear acting upon market participants.</p><p>Can either of these phenomena be exploited? Looking at mean reversion first, it is possible but problematic, as stop losses may need to be very wide and profits are by definition limited. I cannot see this as the basis for a holy grail. The overreaction of markets and their tendency to produce excessive returns on a statistical basis is the holy grail, or rather, provides the basis for a holy grail: a methodology that will make effortless profits over time.</p><p>The best way this can be explained is to imagine taking a handful of salt grains and throwing them up in the air. Suppose you were then able to measure the distance of each grain of salt from the throwing point. You would find that most of them would be relatively close to you, with a few outliers that had travelled further away. If you make a graph showing the distribution of the results, the graph would look like a bell curve, which is a typical and “normal” distribution:</p><p class="forexqa-img-container"><img src="https://prod-forexqna.s3.amazonaws.com/uploads/froala_editor/images/HG.png" style="width: 365px;" class="fr-fic fr-dib fr-draggable forexqa-img fr-fil"></p><p id="isPasted">The bottom axis shows the distance travelled by each grain of salt. The percentages show how many grains travelled each given distance.</p><p>Now suppose that you were constantly buying and selling randomly in the Forex market, and you measured and recorded the maximum possible gain of each trade over thousands of trades and thousands of days. If you constructed a version of the above graph with those results and superimposed it upon the earlier graph, the result would look something like this, with the dotted lines representing the market’s returns distributions:</p><p class="forexqa-img-container"><img src="https://prod-forexqna.s3.amazonaws.com/uploads/froala_editor/images/HG%202.png" style="width: 290px;" class="fr-fic fr-dib fr-draggable forexqa-img fr-fil"></p><p>So, it can be established that speculative markets such as the Forex market produce more excessive returns, both positive and negative than can be expected from a “normal” returns distributions model. A greater number of excessive price events happen than would normally be produced by simple randomness. In plain language, the market offers more big winners and losers than it really should.<br></p>