The financial markets are relatively idiosyncratic, and coupled with its objective complexity very difficult to effectively predict by retail traders, and by extension profit from by them. Retail traders whom are mostly directional traders are present in large numbers and these traders tend to leverage trends that have already occurred, capturing the last breathes of such movements to secure profit and miserably executing trades, rarely exiting into a move until a retrace has occurred and exiting at a loss through a stop loss.
However all market participants do not operate in isolation, almost omniscient in their involvement are the Institutional traders. With greater, and almost unlimited access to available technology, information (both insider and publicly available) and market access, these traders could be seen to have an almost insurmountable trading advantage. Using these advantages in the zero sum game of the financial markets, the apex of this being expressed through Wall Street’s algorithms created to model these markets. Simultaneously leveraging technology, information and market access in one synthetic swoop. Utilising both algorithms and different trading instruments the Institutions are able to interact within the market to augment the direction and nature of the trends the directional traders are operating within.
The directional trader seeks to profit in the purest of ways, that is by predicting a move within a timeframe. Truth be told, the large institutions are concentrated on exploiting an inefficiency, creating a dynamic or finding a dynamic between various instruments to an all-out complicated degree in attempt at finding an edge that is bigger than the cost of executing the trade or trades that follow it. It is our opinion that as a directional trader you need to have the same approach as these institutions. Every market that is liquid and traded frequently will have traders getting in and out around the price, that means price can be predicted by anticipating and understand the dynamic between those groups within the timeframe of each group. In the end you need an environment that isn’t too diluted, after all you are looking for factors that have a relationship, that play against, compound or change one another, then you can reason and fathom which can then lead onto a trade that has a positive expectancy.
When it comes to predicting price, it is extremely difficult to get positive results and any model that comes at it at an angle will surely breakdown eventually. It is close to impossible to know most of the factors and the relationships between them. The relationships are key and that is because any small offset can go on to create larger influences quickly which then get reiterated into itself increasing the complexity considerably. When complexity increases the unknown factors increase and when that happens it increases randomness, which is the same thing as luck. In the case of financial markets, the luck factor is high as the complexity is through the roof.
Looking at price for answers we think is a mistake because price is just the result, it is also the result of the entire complexity that generated itself, so it can’t be reversed engineered or re used and placed in a model because the price was so situational to the situation that was occurring at the time. The factors behind it are also going to be exponentially large.
The key to predicting price is knowing traders take this approach and that they trade based on factors or relationships between factors they know or think they know; therefore, the edge is in predicting the behaviour of various groups of traders not price itself.
