Technical indicators for energy market trading

Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks

“…Here, each tree is constructed separately from the others. The data is then classified using a majority vote across all trees, with Gini impurity serving as the function to measure the quality of the split at each node [32]. The Gini impureness at a given node N is defined as: 𝑮(𝑵) = 𝟏 − (𝑷 𝟏 ) 𝟐 − (𝑷 −𝟏 ) 𝟐 (10) where Pi is the proportion of the population with class label i.…”

“…( 5), where R is the traffic flow for the most recent period, D is the smoothing constant equal to 2/(nu+1), nu is the number of traffic flows in the SMA estimated by EMA, and EMAu is the EMA for traffic flows in the past, by using the equation 5 below: 𝐸𝑀𝐴 = (𝑅 − 𝐸𝑀𝐴 𝑢 ) * 𝐷 + 𝐸𝑀𝐴 𝑢 (5) • Relative strength index (RSI): The normalized current flow is a percentage between (0-100). The name of this oscillator is deceptive because it does not make comparisons between instruments; rather, it depicts the current flow in terms of how it compares to pieces that have been produced within the chosen lookback window length [32]. The equation for the RSI is (6).…”

Navigating Energy and Financial Markets: A Review of Technical Analysis Used and Further Investigation from Various Perspectives

“…Although technical analysis is widely employed in stock trading, its relevance extends to other financial markets, including energy commodities like crude oil, natural gas, and electricity [5,6]. Despite the complexity of energy and financial markets, which are subject to influences such as geopolitical shifts, supply-demand imbalances, weather variations, and technological innovations, technical analysis remains a valuable tool for deciphering market patterns [7] and guiding strategic trading choices [8].…”