We were searching for a strategy that
makes profits independent of market
situation.
We were hardworking. And we created one.
Repetitions of certain market actions are usually hard to read. We created an advanced backtest with support of artificial intelligence for this purpose.
Knowledge of negative market sentiment
Identification of specific market cycles
Testing of hypothesis and deep learning from results
Future decisions are set up in an algorithm
In-depth research of market trends helped us discover patterns that reflect the behavior of everyday trading activities. The outcome of this research is a trading strategy that offers the opportunity to stand on the edge of the market.
Psychology in financial markets drives the downward movements of markets.
It affects and creates patterns that
repeat around the clock.
Almost all market situations bring some levels of uncertainty and volatility.
Uncertainty occurs when the market anticipates significant movement in overall market prices and can potentially lead to serious market shifts, usually downward movements.
FUD comes from human psychology. Over the course of market cycles, downward movements take less time than upward movements. Fear and uncertainty are the strongest catalysts for prices to go down fast.
Market cycles are a result of the evolution of the price of an underlying asset. Cycles we focus on in neural trading are affected by market sentiment that changes cyclically throughout time. Cyclical patterns of different types and scales can be found in every single type of liquid market, mostly in stock, forex, crypto and commodity markets.
Market cycles can be either regular or irregular. For our trading strategy, the best events to work with are regular cycles so we can build a plan that is easy to follow.
For the best rate of successful trades, it is necessary to recognize patterns that demonstrate the best probability of desired outcomes and to evaluate financial risk with thoughtful consideration.
Probability of profits is the key part of a successful trading strategy. If we need to achieve a certain level of confidence using our plan, we need to apply our algorithm to the past.
Backtesting is a powerful tool to identify patterns in market price development. The purpose is to perform an accurate analysis of market trends and patterns with the support of innovative automated tools to study relevant data.
Past data is used to analyze historical price movements and check for specific market patterns to estimate future price movements. Probability models provide a way to help make the optimal decisions based on analysis of past data.
The most important feature of backtesting results is a level of accuracy. This is usually higher with a longer followed period.
There are many possible combinations of parameters in every trading decision tree. The goal is to choose such combinations of parameters that fulfill maximization of the outcome of suggested hypothesis.
After successful testing of such a system, it can be used as an effective tool to mitigate risk and provide higher investment returns.
Automated trading systems make it possible to analyze thousands or millions of data points in a matter of seconds. Algorithms are free of any emotions and human-like obstacles like having second thoughts. Decisions are preprogrammed in an algorithm that follows the plan reliably. Sticking to the plan is the most challenging requirement for any person or business that decides to jump into the world of trading.
Trading algorithms may never completely replace the role of human beings in financial markets, but the need for rational and objective tools are essential and cannot be replaced.