About the Authors
Dr. Efrat Levy and Hen Grinberg co-author founders of E.G. Indicators
Dr. Efrat is a Computer scientist with MSc in Quantum computing. She is also a cyber security expert with over 15 years in research experience, including offensive security, authentication methods, and adversarial machine learning for anomaly detection and explainable AI techniques. Her research interests include day trading, machine learning and algorithmic trading. Hen Grinberg is a data scientist with an MSc in AI from, department of Industrial Engineering; at BGU. Hen Grinberg specializes in unsupervised learning, time series and algorithm trading with several years of experience https://egindicators.com/
With E.G. Indicators, extraction of noisy futures data is simplified, enhancing the ability to identify critical resistance and support systems, apply knowledge, and improve an accurate trading decision.
Understanding signals could be difficult for non-technical individuals in a challenging futures market filled with complexity and fluctuations. The navigation of ups and downs in extremely large data, poses a threat to potential analysts. E.G.Indicators unravels signals from noise, thus, providing a great guide to understanding futures data.
The signal concept, terminology relating to the futures market, implies all the relevant information or patterns within the futures data that can help identify trends, potential entry and exit points, and overall market sentiment. Noise, conversely, stands as the random fluctuations and irrelevant data that influence accurate trade analysis. It includes market volatility, news events and speculative trading.
One of the E.G.Indicators AI-Based Tools for extracting signals from noise is the Filtering technique. This technique is best applied to cushion the effect of noise distortion on the futures market. Various filtering methods, such as moving averages or exponential smoothing, can help smooth out the noise and reveal underlying trends. Technical analysis indicators: Utilizing technical indicators, such as the Relative Strength Index (RSI) or Moving Average Convergence Divergence (MACD), can assist in identifying meaningful signals amidst the noise. AI-based indicators employ machine learning methods to reduce noise and extract signals.
Applying the signal extraction technique makes it easier to identify the support and resistance levels from noisy data. At these levels, Support and resistance levels are price levels where historical buying or selling pressure has caused significant price reversals or pauses. We can better identify these crucial levels by extracting signals from noisy data.
Note: The example provided in this article is for illustrative purposes only and does not reflect actual asset recommendations.
The signal extraction chart above provides an illustrative example of the noise (represented by the smaller fluctuations), the support level (green line) and the resistance level (red line). Despite the noise distortion, there was a significant emergence of the support and resistance level. These are highly important for traders and investors due to the insight it avails.
This guide helps understanding the twist in overwhelming futures data. It is critical for non-technical users in the extraction of valuable signals. While futures data can be noisy, with filtering techniques and application of the technical indicators unravelled from the data analysis, support and resistance level concepts will offer adequate knowledge to help traders and investors indecision-making capabilities while navigating the complexities of the future domain.
Disclaimer: The information provided in this article is for educational purposes only and should not be considered as financial or investment advice. Therefore, consultation with a financial professional and thorough research should be conducted before making any trading decisions since stock market trading involves risk.
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Dr. Efrat Levy and Hen Grinberg co-authored the E.G. Indicators
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