█ OVERVIEW K-means is a clustering algorithm commonly used in machine learning to group data points into distinct clusters based on their similarities. While K-means is not typically used directly for identifying support and resistance levels in financial markets, it can serve as a tool in a broader analysis approach. Support and resistance levels are price...
What is a Cluster Filter? One of the approaches to determining a useful signal (trend) in stream data. Small filtering (smoothing) tests applied to market quotes demonstrate the potential for creating non-lagging digital filters (indicators) that are not redrawn on the last bars. Standard Approach This approach is based on classical time series smoothing...
Variety MA Cluster Filter is one method of creating a low-lag digital filter. This is done by calculating two moving averages and then comparing their output to the past value of the combined output and then choosing the max and min between the two moving averages to then determine the combined output. I've included standard deviation filtering for smoothing. ...
Fɪʙᴏɴᴀᴄᴄɪ Exᴛᴇɴᴛɪᴏɴ / Rᴇᴛʀᴀᴄᴍᴇɴᴛ / Pɪᴠᴏᴛ Pᴏɪɴᴛꜱ This study combines various Fibonacci concepts into one, and some basic volume and volatility indications █ Pɪᴠᴏᴛ Pᴏɪɴᴛꜱ — is a technical indicator that is used to determine the levels at which price may face support or resistance. The Pivot Points indicator consists of a pivot point (PP) level and several...
This Indicator operates similarly to the Cluster Algo marketed elsewhere. The key difference is the integration of Bollinger Bands, giving us clear indications. Buy - When the signal line goes above the Bollinger basis line and is GREEN Sell - When the signal line goes below the Bollinger basis line and is RED Consider closing the trade when the signal line...
Description: A Function that returns cluster centers for given data (X,Y) vector points. Inputs: _X: Array containing x data points.¹ _Y: Array containing y data points.¹ _number_of_clusters: number of clusters. Note: ¹: _X and _Y size must match. Outputs: _centers_x: Array containing x data points. _centers_y: Array...
Volume clusters created from candlestick volumes. See also "Poor man's volume profile" . The code is generated using a template. To change the settings, you may need to regenerate the code. The code has a link to the repository with the template.
SMA and EMA endings to identify support and resistance with a good chart overview. Unfortunately the scaling of TradingView in intraday charts is not good.
Plots 4 EMAs of your chosen length. Easy to manage multiple EMAs and saves on your allotted indicators for smaller tradingview plans.
With the Dynamic Price Cluster you can find areas of high commercial interest in any time frame. Dynamic Price Cluster (DPC) dynamically represent different areas of high commercial interest and it are composed of 3 lines or levels The set, group or cluster of levels will be maintained as long as the market conditions satisfy or it are in accordance with the...
I was reading about Fibonacci Clusters on investopedia (www.investopedia.com) and couldn't find a script for it on tradingveiw. Apparently some people use it successfully but I found it a little chaotic. This script will mark the retracements in a window's length, and you can set this for six windows. This script isn't very pretty because it doesn't seem obviously...
EXPERIMENTAL: Fibonacci rate levels based on price advance/decline, can be used to make visualizations of fib clusters or for cycles.