In the previous post, I discussed the importance of Multiple time frames Analysis, this post continues the discussion of the topic by analyzing several stocks in the crucial junction of support (resistance analysis will follow shortly after …)
Support and resistance levels represent significant junctures where the interplay of supply and demand forces occurs. Technical analysts consider these levels pivotal in understanding market psychology and supply-and-demand dynamics. The breach of these support or resistance levels implies a shift in the underlying forces, leading to the probable establishment of new support and resistance levels.
Support is the level at which demand is strong enough to stop the stock from falling any further. Most times when price reaches the support level, it has difficulty penetrating that level. The rationale is that as the price drops and approaches support, buyers (demand) become more inclined to buy and sellers (supply) become less willing to sell. Thus a breach of support levels is considered a major sell signal
XOM – Breaking Support in all time frames
XOM has broker the long term support at around 100$ , the chart above is the weekly timeframe analysis, the peak was at 120$ and since then the stock is in decline which range traded for a while but now seems to continue the downward move, next level is the 80$ price level, almost -20% decline
This is the medium-term analysis price analysis showing weakness on the daily chart breaking the 97$ support level which is also the lowest price in the last 6 weeks
and finally, the 1-hour chart
A more detailed analysis of XOM showing the break of the 97$ price level
AOB API Service
We achieve this through the utilization of our financial service API, which is designed to identify price patterns. Our API service employs machine learning to detect stock price support, utilizing algorithms that analyze historical price data to pinpoint crucial support levels. Various machine learning models, including KNN, neural networks, and ensemble methods, are trained on our servers to recognize patterns and trends in stock prices indicative of support levels.
These trained algorithms incorporate diverse pattern recognition techniques, such as historical price movements, trading volumes, and technical indicators, with the label denoting support areas within specific historical time windows. The models predict potential support zones, indicating areas where stocks might encounter selling interest, thereby empowering traders to make well-informed decisions.
The implementation of machine learning in this context enhances our capacity to identify and respond to dynamic market conditions, leading to more effective detection of support levels.
Technical analysis serves as a method to predict future security or market prices. Certain investors opt for a combined approach utilizing fundamental and technical analyses. Fundamental analysis guides their decisions on what to buy, while technical analysis aids in determining the suitable timing for those purchases.