Predicting digital asset trends through automated technical analysis
Automated trading signal generation supports in dealing with Digital Asset Exchanges in particular, not only because of their particular nature in terms of the difficulty of making gains, but also for the reason that continuing losses are illogical and unjustified to most investors. It is then a necessity to use modern software techniques to determine the relevant method of dealing and trading to avoid losses.
When we eliminate the human emotion - which is usually the main reason for achieving “consecutive” losses that erode the capital - and rely on the machine to analyze the data and nomination of assets without any human intervention; consistent performance in profitable trading is the logical consequence.
Previous historical data of digital asset prices are used to predict future price trends, and the prediction model usually uses two layers to analyze the data. Technical analysis in the first layer and then a second layer of comprehending based on machine learning. In addition to fund management strategy that makes use of the recommendations made by the model to determine the course of capital invested. It builds a portfolio of entry and exit signals resulting from the model, and concludes how far the forecasting model is relative to the performance of the whole market.
Predicting the direction of future highs and lows is a topic that has been widely studied in many fields in trading, finance, statistics and computer science. The fundamental motivation for sure is to make gains, and professional traders usually use basic analysis and technical analysis to analyze markets and make investment decisions.
The basic analysis is the traditional approach of studying the fundamentals of companies such as revenues and expenses, market positioning, annual growth percentage, traded asset technical potential, and so on. Technical analysis, on the other hand, only examines historical price fluctuations and variations. Technical analysis experts study historical prices to define price action patterns using data at different time intervals in an attempt to predict future price movements. Thus there is an inherent correlation between price and the traded asset, which can be used to determine the times of entry and exit for each asset.
In finance, statistics, computer science, and most traditional models; statistical models and neural network models are used. Being derived from the price data of the forecast, the dominant strategy in computer science uses spectacular algorithms, Neural networks, or a combination of both (advanced neural networks) where different values of technical indicators are compiled and their future results classified according to the most profitable models and fed to the machine enabling it to match those indices and extract them if they are formed again on the same assets. This is definitely the beauty of AI (Artificial Intelligence). The more information becomes available over time, the more it adapts and learns, and the better it performs. The fruitful outcome of this process; is the investment strategy that can and will be used in identifying markets for trading and investing in terms of profitability through simulating a virtual wallet using trade signals generated by the adopted investment strategy.
With the introduction of RedCab’s platform in Q3 2019, and with REDC tokens becoming mainstream with massive demand from the network, and governed capped supply from Proof-of-Driving and Proof-of-Marketing token generation algorithms; it’s crucial to automate the trading strategies to balance the bid and ask market depth and appreciate the token exchange rate overtime with consistent and steady growth rate month over month.