January 12, 2024 alon@alphaoverbeta.net

Pattern Recognition SaaS for Detection of Key Support Price Levels

Multiple time frames Analysis

In this post, I will use our pattern recognition algorithm to detect key support areas for stock prices in multiple time frames.
The strategy of employing multiple timeframes is a trading approach that entails examining an asset’s price chart across different periods
to identify optimal moments for entering and exiting trades.
This method involves integrating various timeframes into the analysis of an asset before making trading decisions.
In this context, timeframes denote the standard intervals used in charting platforms to represent trading sessions.
Common timeframes typically range from 1 minute to 1 month, for example, 1-hour, 1-day, 1-week, and 1-month intervals.
Multiple timeframe analysis adopts a top-down approach where traders assess longer-term trends using higher timeframes before pinpointing optimal
entry points using smaller timeframes. This analytical method is particularly beneficial for short-term traders, including scalpers, day traders,
and swing traders, but can also prove valuable for long-term and position traders.
Despite the availability of many such timeframes for asset analysis, traders often select three or four suitable timeframes for their evaluations,
we will use three: 1-hour, 1-day, 1-week.
The general guideline when choosing timeframes for analysis is to maintain a ratio of about 1:5 when transitioning between timeframes.
For instance, a swing trader analyzing three timeframes might choose the weekly, 1-day, and hourly intervals.
In this scenario, with the 1-day chart as the primary trading timeframe, the trader can use the weekly chart for a more general market view,
shift to the 1-day timeframe to identify trading opportunities, and then move to the hourly timeframe to determine the most reasonable entry point.
Similarly, a day trader assessing the day’s trend on an hourly chart might transition to the 10-minute chart (1:6) for suitable entry points.
The 10-minute chart serves to highlight short-term developments, while the hourly chart allows ongoing monitoring of the trade’s progress.
In essence, the concept of multiple timeframe analysis is straightforward: analyze charts across various timeframes to identify optimal trading opportunities
with a higher likelihood of success. Many day traders begin by examining the daily timeframe for the long-term trend and then progressively move down to the
four-hour chart, hourly chart, and 5-minute chart for a more detailed analysis.

In our analysis we will try to find stocks (in the mega caps category) that broke key support levels in at least two of the three timeframes, thus signaling an opportunity to enter a short position
we do that using the service offered by our financial service API to detect price patterns.
The API service detects stock price support using machine learning and involves utilizing algorithms to analyze historical price data and identify key support levels. Machine learning models, such as KNN, neural networks, or ensemble methods, are trained on our servers to recognize patterns and trends in stock prices indicative of support levels.
These Trained algorithms offer many pattern recognition methods for example historical price movements, trading volumes, and technical indicators, with the lable being the support areas for the specific historical time window.
Our models predict potential support zones where stocks may experience selling interest, helping traders make informed decisions.
Implementing machine learning in this context enhances the ability to identify and respond to dynamic market conditions for more effective support level detection.

The code (Python)

symbol = 'TSLA'
period = '3mo'
interval = '1h'
support_d, status_code = Support(symbol=symbol, period=period, interval=interval, key='DEMO', secret='DEMO', last=False)

The code is very simple and short, sending the right parameters to the API results in a list of key support areas, clean and optimal.
The entire example can be found in our GIT repo with many other examples of the usage of our financial API

Sample Use Case: Disney

the longer time period is the weekly on a 2-year historical time window revealing the 90$ support broken last week with the next long-term targets at 85$ and 80$
So the indication here is the long-term horizon has shifted to a more bearish outlook

the medium time period is the daily on a 6-month historical time window revealing the 90$ support broken recently with next medium-term targets at 85$ and 80$, same as the longer-term outlook
So the indication here is the medium-horizen has shifted to a more bearish outlook

the short-term time period is the 1-hour on a 3-month historical time window revealing the 90$ support broken recently with next medium-term targets at 85$ and 80$, the same as the medium-term outlook
So the indication here is the short time horizon has shifted to a more bearish outlook

It’s quite rare for all timeframes to agree on one direction for the stock but the conclusion for DIS is a bearish outlook with 85$ than 80$ price targets.

In conclusion, the integration of machine learning for pattern recognition in stock price analysis revolutionizes decision-making. By decoding complex market trends, these algorithms empower investors with sharper insights, fostering a dynamic approach to trading and enhancing the potential for informed, strategic investment choices.

Trade Smart,

Alon (Api.AlphaOverBeta.Net)