Support Resistance Algorithm - Technical analysis [closed] - algorithm

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I have an intra-day chart and I am trying to figure out how to calculate
support and resistance levels, anyone knows an algorithm for doing that, or a good starting point?

Answer 1

Yes, a very simple algorithm is to choose a timeframe, say 100 bars, then look for local turning points, or Maxima and Minima. Maxima and Minima can be computed from a smoothed closing price by using the 1st and second derivative (dy/dx and d^2y/dx). Where dy/dx = zero and d^y/dx is positive, you have a minima, when dy/dx = zero and d^2y/dx is negative, you have a maxima.
In practical terms this could be computed by iterating over your smoothed closing price series and looking at three adjacent points. If the points are lower/higher/lower in relative terms then you have a maxima, else higher/lower/higher you have a minima. You may wish to fine-tune this detection method to look at more points (say 5, 7) and only trigger if the edge points are a certain % away from the centre point. this is similar to the algorithm that the ZigZag indicator uses.
Once you have local maxima and minima, you then want to look for clusters of turning points within a certain distance of each other in the Y-Direction. this is simple. Take the list of N turning points and compute the Y-distance between it and each of the other discovered turning points. If the distance is less than a fixed constant then you have found two "close" turning points, indicating possible support/resistance.
You could then rank your S/R lines, so two turning points at $20 is less important than three turning points at $20 for instance.
An extension to this would be to compute trendlines. With the list of turning points discovered now take each point in turn and select two other points, trying to fit a straight line equation. If the equation is solvable within a certain error margin, you have a sloping trendline. If not, discard and move on to the next triplet of points.
The reason why you need three at a time to compute trendlines is any two points can be used in the straight line equation. Another way to compute trendlines would be to compute the straight line equation of all pairs of turning points, then see if a third point (or more than one) lies on the same straight line within a margin of error. If 1 or more other points does lie on this line, bingo you have calculated a Support/Resistance trendline.
No code examples sorry, I'm just giving you some ideas on how it could be done. In summary:
Inputs to the system
Lookback period L (number of bars)
Closing prices for L bars
Smoothing factor (to smooth closing price)
Error Margin or Delta (minimum distance between turning points to constitute a match)
Outputs
List of turning points, call them tPoints[] (x,y)
List of potential trendlines, each with the line equation (y = mx + c)
EDIT: Update
I recently learned a very simple indicator called a Donchian Channel, which basically plots a channel of the highest high in 20 bars, and lowest low. It can be used to plot an approximate support resistance level. But the above - Donchian Channel with turning points is cooler ^_^

Answer 2

I am using a much less complex algorithm in my algorithmic trading system.
Following steps are one side of the algorithm and are used for calculating support levels. Please read notes below the algorithm to understand how to calculate resistance levels.
Algorithm
Break timeseries into segments of size N (Say, N = 5)
Identify minimum values of each segment, you will have an array of minimum values from all segments = :arrayOfMin
Find minimum of (:arrayOfMin) = :minValue
See if any of the remaining values fall within range (X% of :minValue) (Say, X = 1.3%)
Make a separate array (:supportArr)
add values within range & remove these values from :arrayOfMin
also add :minValue from step 3
Calculating support (or resistance)
Take a mean of this array = support_level
If support is tested many times, then it is considered strong.
strength_of_support = supportArr.length
level_type (SUPPORT|RESISTANCE) = Now, if current price is below support then support changes role and becomes resistance
Repeat steps 3 to 7 until :arrayOfMin is empty
You will have all support/resistance values with a strength. Now smoothen these values, if any support levels are too close then eliminate one of them.
These support/resistance were calculated considering support levels search. You need perform steps 2 to 9 considering resistance levels search. Please see notes and implementation.
Notes:
Adjust the values of N & X to get more accurate results.
Example, for less volatile stocks or equity indexes use (N = 10, X = 1.2%)
For high volatile stocks use (N = 22, X = 1.5%)
For resistance, the procedure is exactly opposite (use maximum function instead of minimum)
This algorithm was purposely kept simple to avoid complexity, it can be improved to give better results.
Here's my implementation:
public interface ISupportResistanceCalculator {
/**
* Identifies support / resistance levels.
*
* #param timeseries
* timeseries
* #param beginIndex
* starting point (inclusive)
* #param endIndex
* ending point (exclusive)
* #param segmentSize
* number of elements per internal segment
* #param rangePct
* range % (Example: 1.5%)
* #return A tuple with the list of support levels and a list of resistance
* levels
*/
Tuple<List<Level>, List<Level>> identify(List<Float> timeseries,
int beginIndex, int endIndex, int segmentSize, float rangePct);
}
Main calculator class
/**
*
*/
package com.perseus.analysis.calculator.technical.trend;
import static com.perseus.analysis.constant.LevelType.RESISTANCE;
import static com.perseus.analysis.constant.LevelType.SUPPORT;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Date;
import java.util.LinkedList;
import java.util.List;
import java.util.Set;
import java.util.TreeSet;
import com.google.common.collect.Lists;
import com.perseus.analysis.calculator.mean.IMeanCalculator;
import com.perseus.analysis.calculator.timeseries.ITimeSeriesCalculator;
import com.perseus.analysis.constant.LevelType;
import com.perseus.analysis.model.Tuple;
import com.perseus.analysis.model.technical.Level;
import com.perseus.analysis.model.timeseries.ITimeseries;
import com.perseus.analysis.util.CollectionUtils;
/**
* A support and resistance calculator.
*
* #author PRITESH
*
*/
public class SupportResistanceCalculator implements
ISupportResistanceCalculator {
static interface LevelHelper {
Float aggregate(List<Float> data);
LevelType type(float level, float priceAsOfDate, final float rangePct);
boolean withinRange(Float node, float rangePct, Float val);
}
static class Support implements LevelHelper {
#Override
public Float aggregate(final List<Float> data) {
return Collections.min(data);
}
#Override
public LevelType type(final float level, final float priceAsOfDate,
final float rangePct) {
final float threshold = level * (1 - (rangePct / 100));
return (priceAsOfDate < threshold) ? RESISTANCE : SUPPORT;
}
#Override
public boolean withinRange(final Float node, final float rangePct,
final Float val) {
final float threshold = node * (1 + (rangePct / 100f));
if (val < threshold)
return true;
return false;
}
}
static class Resistance implements LevelHelper {
#Override
public Float aggregate(final List<Float> data) {
return Collections.max(data);
}
#Override
public LevelType type(final float level, final float priceAsOfDate,
final float rangePct) {
final float threshold = level * (1 + (rangePct / 100));
return (priceAsOfDate > threshold) ? SUPPORT : RESISTANCE;
}
#Override
public boolean withinRange(final Float node, final float rangePct,
final Float val) {
final float threshold = node * (1 - (rangePct / 100f));
if (val > threshold)
return true;
return false;
}
}
private static final int SMOOTHEN_COUNT = 2;
private static final LevelHelper SUPPORT_HELPER = new Support();
private static final LevelHelper RESISTANCE_HELPER = new Resistance();
private final ITimeSeriesCalculator tsCalc;
private final IMeanCalculator meanCalc;
public SupportResistanceCalculator(final ITimeSeriesCalculator tsCalc,
final IMeanCalculator meanCalc) {
super();
this.tsCalc = tsCalc;
this.meanCalc = meanCalc;
}
#Override
public Tuple<List<Level>, List<Level>> identify(
final List<Float> timeseries, final int beginIndex,
final int endIndex, final int segmentSize, final float rangePct) {
final List<Float> series = this.seriesToWorkWith(timeseries,
beginIndex, endIndex);
// Split the timeseries into chunks
final List<List<Float>> segments = this.splitList(series, segmentSize);
final Float priceAsOfDate = series.get(series.size() - 1);
final List<Level> levels = Lists.newArrayList();
this.identifyLevel(levels, segments, rangePct, priceAsOfDate,
SUPPORT_HELPER);
this.identifyLevel(levels, segments, rangePct, priceAsOfDate,
RESISTANCE_HELPER);
final List<Level> support = Lists.newArrayList();
final List<Level> resistance = Lists.newArrayList();
this.separateLevels(support, resistance, levels);
// Smoothen the levels
this.smoothen(support, resistance, rangePct);
return new Tuple<>(support, resistance);
}
private void identifyLevel(final List<Level> levels,
final List<List<Float>> segments, final float rangePct,
final float priceAsOfDate, final LevelHelper helper) {
final List<Float> aggregateVals = Lists.newArrayList();
// Find min/max of each segment
for (final List<Float> segment : segments) {
aggregateVals.add(helper.aggregate(segment));
}
while (!aggregateVals.isEmpty()) {
final List<Float> withinRange = new ArrayList<>();
final Set<Integer> withinRangeIdx = new TreeSet<>();
// Support/resistance level node
final Float node = helper.aggregate(aggregateVals);
// Find elements within range
for (int i = 0; i < aggregateVals.size(); ++i) {
final Float f = aggregateVals.get(i);
if (helper.withinRange(node, rangePct, f)) {
withinRangeIdx.add(i);
withinRange.add(f);
}
}
// Remove elements within range
CollectionUtils.remove(aggregateVals, withinRangeIdx);
// Take an average
final float level = this.meanCalc.mean(
withinRange.toArray(new Float[] {}), 0, withinRange.size());
final float strength = withinRange.size();
levels.add(new Level(helper.type(level, priceAsOfDate, rangePct),
level, strength));
}
}
private List<List<Float>> splitList(final List<Float> series,
final int segmentSize) {
final List<List<Float>> splitList = CollectionUtils
.convertToNewLists(CollectionUtils.splitList(series,
segmentSize));
if (splitList.size() > 1) {
// If last segment it too small
final int lastIdx = splitList.size() - 1;
final List<Float> last = splitList.get(lastIdx);
if (last.size() <= (segmentSize / 1.5f)) {
// Remove last segment
splitList.remove(lastIdx);
// Move all elements from removed last segment to new last
// segment
splitList.get(lastIdx - 1).addAll(last);
}
}
return splitList;
}
private void separateLevels(final List<Level> support,
final List<Level> resistance, final List<Level> levels) {
for (final Level level : levels) {
if (level.getType() == SUPPORT) {
support.add(level);
} else {
resistance.add(level);
}
}
}
private void smoothen(final List<Level> support,
final List<Level> resistance, final float rangePct) {
for (int i = 0; i < SMOOTHEN_COUNT; ++i) {
this.smoothen(support, rangePct);
this.smoothen(resistance, rangePct);
}
}
/**
* Removes one of the adjacent levels which are close to each other.
*/
private void smoothen(final List<Level> levels, final float rangePct) {
if (levels.size() < 2)
return;
final List<Integer> removeIdx = Lists.newArrayList();
Collections.sort(levels);
for (int i = 0; i < (levels.size() - 1); i++) {
final Level currentLevel = levels.get(i);
final Level nextLevel = levels.get(i + 1);
final Float current = currentLevel.getLevel();
final Float next = nextLevel.getLevel();
final float difference = Math.abs(next - current);
final float threshold = (current * rangePct) / 100;
if (difference < threshold) {
final int remove = currentLevel.getStrength() >= nextLevel
.getStrength() ? i : i + 1;
removeIdx.add(remove);
i++; // start with next pair
}
}
CollectionUtils.remove(levels, removeIdx);
}
private List<Float> seriesToWorkWith(final List<Float> timeseries,
final int beginIndex, final int endIndex) {
if ((beginIndex == 0) && (endIndex == timeseries.size()))
return timeseries;
return timeseries.subList(beginIndex, endIndex);
}
}
Here are some supporting classes:
public enum LevelType {
SUPPORT, RESISTANCE
}
public class Tuple<A, B> {
private final A a;
private final B b;
public Tuple(final A a, final B b) {
super();
this.a = a;
this.b = b;
}
public final A getA() {
return this.a;
}
public final B getB() {
return this.b;
}
#Override
public String toString() {
return "Tuple [a=" + this.a + ", b=" + this.b + "]";
};
}
public abstract class CollectionUtils {
/**
* Removes items from the list based on their indexes.
*
* #param list
* list
* #param indexes
* indexes this collection must be sorted in ascending order
*/
public static <T> void remove(final List<T> list,
final Collection<Integer> indexes) {
int i = 0;
for (final int idx : indexes) {
list.remove(idx - i++);
}
}
/**
* Splits the given list in segments of the specified size.
*
* #param list
* list
* #param segmentSize
* segment size
* #return segments
*/
public static <T> List<List<T>> splitList(final List<T> list,
final int segmentSize) {
int from = 0, to = 0;
final List<List<T>> result = new ArrayList<>();
while (from < list.size()) {
to = from + segmentSize;
if (to > list.size()) {
to = list.size();
}
result.add(list.subList(from, to));
from = to;
}
return result;
}
}
/**
* This class represents a support / resistance level.
*
* #author PRITESH
*
*/
public class Level implements Serializable {
private static final long serialVersionUID = -7561265699198045328L;
private final LevelType type;
private final float level, strength;
public Level(final LevelType type, final float level) {
this(type, level, 0f);
}
public Level(final LevelType type, final float level, final float strength) {
super();
this.type = type;
this.level = level;
this.strength = strength;
}
public final LevelType getType() {
return this.type;
}
public final float getLevel() {
return this.level;
}
public final float getStrength() {
return this.strength;
}
#Override
public String toString() {
return "Level [type=" + this.type + ", level=" + this.level
+ ", strength=" + this.strength + "]";
}
}

Answer 3

I put together a package that implements support and resistance trendlines like what you're asking about. Here are a few examples of some examples:
import numpy as np
import pandas.io.data as pd
from matplotlib.pyplot import *
gentrends('fb', window = 1.0/3.0)
Output
That example just pulls the adjusted close prices, but if you have intraday data already loaded in you can also feed it raw data as a numpy array and it will implement the same algorithm on that data as it would if you just fed it a ticker symbol.
Not sure if this is exactly what you were looking for but hopefully this helps get you started. The code and some more explanation can be found on the GitHub page where I have it hosted: https://github.com/dysonance/Trendy

Answer 4

I have figured out another way of calculating Support/Resistance dynamically.
Steps:
Create a list of important price - The high and low of each candle in your range is important. Each of this prices is basically a probable SR(Support / Resistance).
Give each price a score.
Sort the prices by score and remove the ones close to each other(at a distance of x% from each other).
Print the top N prices and having a mimimum score of Y. These are your Support Resistances. It worked very well for me in ~300 different stocks.
The scoring technique
A price is acting as a strong SR if there are many candles which comes close to this but cannot cross this.
So, for each candle which are close to this price (within a distance of y% from the price), we will add +S1 to the score.
For each candle which cuts through this price, we will add -S2(negative) to the score.
This should give you a very basic idea of how to assign scores to this.
Now you have to tweak it according to your requirements.
Some tweak I made and which improved the performance a lot are as follows:
Different score for different types of cut. If the body of a candle cuts through the price, then score change is -S3 but the wick of a candle cuts through the price, the score change is -S4. Here Abs(S3) > Abs(S4) because cut by body is more significant than cut by wick.
If the candle which closes close the price but unable to cross is a high(higher than two candles on each side) or low(lower than 2 candles on each side), then add a higher score than other normal candles closing near this.
If the candle closing near this is a high or low, and the price was in a downtrend or a uptrend (at least y% move) then add a higher score to this point.
You can remove some prices from the initial list. I consider a price only if it is the highest or the lowest among N candles on both side of it.
Here is a snippet of my code.
private void findSupportResistance(List<Candle> candles, Long scripId) throws ExecutionException {
// This is a cron job, so I skip for some time once a SR is found in a stock
if(processedCandles.getIfPresent(scripId) == null || checkAlways) {
//Combining small candles to get larger candles of required timeframe. ( I have 1 minute candles and here creating 1 Hr candles)
List<Candle> cumulativeCandles = cumulativeCandleHelper.getCumulativeCandles(candles, CUMULATIVE_CANDLE_SIZE);
//Tell whether each point is a high(higher than two candles on each side) or a low(lower than two candles on each side)
List<Boolean> highLowValueList = this.highLow.findHighLow(cumulativeCandles);
String name = scripIdCache.getScripName(scripId);
Set<Double> impPoints = new HashSet<Double>();
int pos = 0;
for(Candle candle : cumulativeCandles){
//A candle is imp only if it is the highest / lowest among #CONSECUTIVE_CANDLE_TO_CHECK_MIN on each side
List<Candle> subList = cumulativeCandles.subList(Math.max(0, pos - CONSECUTIVE_CANDLE_TO_CHECK_MIN),
Math.min(cumulativeCandles.size(), pos + CONSECUTIVE_CANDLE_TO_CHECK_MIN));
if(subList.stream().min(Comparator.comparing(Candle::getLow)).get().getLow().equals(candle.getLow()) ||
subList.stream().min(Comparator.comparing(Candle::getHigh)).get().getHigh().equals(candle.getHigh())) {
impPoints.add(candle.getHigh());
impPoints.add(candle.getLow());
}
pos++;
}
Iterator<Double> iterator = impPoints.iterator();
List<PointScore> score = new ArrayList<PointScore>();
while (iterator.hasNext()){
Double currentValue = iterator.next();
//Get score of each point
score.add(getScore(cumulativeCandles, highLowValueList, currentValue));
}
score.sort((o1, o2) -> o2.getScore().compareTo(o1.getScore()));
List<Double> used = new ArrayList<Double>();
int total = 0;
Double min = getMin(cumulativeCandles);
Double max = getMax(cumulativeCandles);
for(PointScore pointScore : score){
// Each point should have at least #MIN_SCORE_TO_PRINT point
if(pointScore.getScore() < MIN_SCORE_TO_PRINT){
break;
}
//The extremes always come as a Strong SR, so I remove some of them
// I also reject a price which is very close the one already used
if (!similar(pointScore.getPoint(), used) && !closeFromExtreme(pointScore.getPoint(), min, max)) {
logger.info("Strong SR for scrip {} at {} and score {}", name, pointScore.getPoint(), pointScore.getScore());
// logger.info("Events at point are {}", pointScore.getPointEventList());
used.add(pointScore.getPoint());
total += 1;
}
if(total >= totalPointsToPrint){
break;
}
}
}
}
private boolean closeFromExtreme(Double key, Double min, Double max) {
return Math.abs(key - min) < (min * DIFF_PERC_FROM_EXTREME / 100.0) || Math.abs(key - max) < (max * DIFF_PERC_FROM_EXTREME / 100);
}
private Double getMin(List<Candle> cumulativeCandles) {
return cumulativeCandles.stream()
.min(Comparator.comparing(Candle::getLow)).get().getLow();
}
private Double getMax(List<Candle> cumulativeCandles) {
return cumulativeCandles.stream()
.max(Comparator.comparing(Candle::getLow)).get().getHigh();
}
private boolean similar(Double key, List<Double> used) {
for(Double value : used){
if(Math.abs(key - value) <= (DIFF_PERC_FOR_INTRASR_DISTANCE * value / 100)){
return true;
}
}
return false;
}
private PointScore getScore(List<Candle> cumulativeCandles, List<Boolean> highLowValueList, Double price) {
List<PointEvent> events = new ArrayList<>();
Double score = 0.0;
int pos = 0;
int lastCutPos = -10;
for(Candle candle : cumulativeCandles){
//If the body of the candle cuts through the price, then deduct some score
if(cutBody(price, candle) && (pos - lastCutPos > MIN_DIFF_FOR_CONSECUTIVE_CUT)){
score += scoreForCutBody;
lastCutPos = pos;
events.add(new PointEvent(PointEvent.Type.CUT_BODY, candle.getTimestamp(), scoreForCutBody));
//If the wick of the candle cuts through the price, then deduct some score
} else if(cutWick(price, candle) && (pos - lastCutPos > MIN_DIFF_FOR_CONSECUTIVE_CUT)){
score += scoreForCutWick;
lastCutPos = pos;
events.add(new PointEvent(PointEvent.Type.CUT_WICK, candle.getTimestamp(), scoreForCutWick));
//If the if is close the high of some candle and it was in an uptrend, then add some score to this
} else if(touchHigh(price, candle) && inUpTrend(cumulativeCandles, price, pos)){
Boolean highLowValue = highLowValueList.get(pos);
//If it is a high, then add some score S1
if(highLowValue != null && highLowValue){
score += scoreForTouchHighLow;
events.add(new PointEvent(PointEvent.Type.TOUCH_UP_HIGHLOW, candle.getTimestamp(), scoreForTouchHighLow));
//Else add S2. S2 > S1
} else {
score += scoreForTouchNormal;
events.add(new PointEvent(PointEvent.Type.TOUCH_UP, candle.getTimestamp(), scoreForTouchNormal));
}
//If the if is close the low of some candle and it was in an downtrend, then add some score to this
} else if(touchLow(price, candle) && inDownTrend(cumulativeCandles, price, pos)){
Boolean highLowValue = highLowValueList.get(pos);
//If it is a high, then add some score S1
if (highLowValue != null && !highLowValue) {
score += scoreForTouchHighLow;
events.add(new PointEvent(PointEvent.Type.TOUCH_DOWN, candle.getTimestamp(), scoreForTouchHighLow));
//Else add S2. S2 > S1
} else {
score += scoreForTouchNormal;
events.add(new PointEvent(PointEvent.Type.TOUCH_DOWN_HIGHLOW, candle.getTimestamp(), scoreForTouchNormal));
}
}
pos += 1;
}
return new PointScore(price, score, events);
}
private boolean inDownTrend(List<Candle> cumulativeCandles, Double price, int startPos) {
//Either move #MIN_PERC_FOR_TREND in direction of trend, or cut through the price
for(int pos = startPos; pos >= 0; pos-- ){
Candle candle = cumulativeCandles.get(pos);
if(candle.getLow() < price){
return false;
}
if(candle.getLow() - price > (price * MIN_PERC_FOR_TREND / 100)){
return true;
}
}
return false;
}
private boolean inUpTrend(List<Candle> cumulativeCandles, Double price, int startPos) {
for(int pos = startPos; pos >= 0; pos-- ){
Candle candle = cumulativeCandles.get(pos);
if(candle.getHigh() > price){
return false;
}
if(price - candle.getLow() > (price * MIN_PERC_FOR_TREND / 100)){
return true;
}
}
return false;
}
private boolean touchHigh(Double price, Candle candle) {
Double high = candle.getHigh();
Double ltp = candle.getLtp();
return high <= price && Math.abs(high - price) < ltp * DIFF_PERC_FOR_CANDLE_CLOSE / 100;
}
private boolean touchLow(Double price, Candle candle) {
Double low = candle.getLow();
Double ltp = candle.getLtp();
return low >= price && Math.abs(low - price) < ltp * DIFF_PERC_FOR_CANDLE_CLOSE / 100;
}
private boolean cutBody(Double point, Candle candle) {
return Math.max(candle.getOpen(), candle.getClose()) > point && Math.min(candle.getOpen(), candle.getClose()) < point;
}
private boolean cutWick(Double price, Candle candle) {
return !cutBody(price, candle) && candle.getHigh() > price && candle.getLow() < price;
}
Some Helper classes:
public class PointScore {
Double point;
Double score;
List<PointEvent> pointEventList;
public PointScore(Double point, Double score, List<PointEvent> pointEventList) {
this.point = point;
this.score = score;
this.pointEventList = pointEventList;
}
}
public class PointEvent {
public enum Type{
CUT_BODY, CUT_WICK, TOUCH_DOWN_HIGHLOW, TOUCH_DOWN, TOUCH_UP_HIGHLOW, TOUCH_UP;
}
Type type;
Date timestamp;
Double scoreChange;
public PointEvent(Type type, Date timestamp, Double scoreChange) {
this.type = type;
this.timestamp = timestamp;
this.scoreChange = scoreChange;
}
#Override
public String toString() {
return "PointEvent{" +
"type=" + type +
", timestamp=" + timestamp +
", points=" + scoreChange +
'}';
}
}
Some example of SR created by the code.

Answer 5

Here's a python function to find support / resistance levels
This function takes a numpy array of last traded price and returns a
list of support and resistance levels respectively. n is the number
of entries to be scanned.
def supres(ltp, n):
"""
This function takes a numpy array of last traded price
and returns a list of support and resistance levels
respectively. n is the number of entries to be scanned.
"""
from scipy.signal import savgol_filter as smooth
# converting n to a nearest even number
if n % 2 != 0:
n += 1
n_ltp = ltp.shape[0]
# smoothening the curve
ltp_s = smooth(ltp, (n + 1), 3)
# taking a simple derivative
ltp_d = np.zeros(n_ltp)
ltp_d[1:] = np.subtract(ltp_s[1:], ltp_s[:-1])
resistance = []
support = []
for i in xrange(n_ltp - n):
arr_sl = ltp_d[i:(i + n)]
first = arr_sl[:(n / 2)] # first half
last = arr_sl[(n / 2):] # second half
r_1 = np.sum(first > 0)
r_2 = np.sum(last < 0)
s_1 = np.sum(first < 0)
s_2 = np.sum(last > 0)
# local maxima detection
if (r_1 == (n / 2)) and (r_2 == (n / 2)):
resistance.append(ltp[i + ((n / 2) - 1)])
# local minima detection
if (s_1 == (n / 2)) and (s_2 == (n / 2)):
support.append(ltp[i + ((n / 2) - 1)])
return support, resistance
SRC

Answer 6

The best way I have found to get SR levels is with clustering. Maxima and Minima is calculated and then those values are flattened (like a scatter plot where x is the maxima and minima values and y is always 1). You then cluster these values using Sklearn.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import AgglomerativeClustering
# Calculate VERY simple waves
mx = df.High_15T.rolling( 100 ).max().rename('waves')
mn = df.Low_15T.rolling( 100 ).min().rename('waves')
mx_waves = pd.concat([mx,pd.Series(np.zeros(len(mx))+1)],axis = 1)
mn_waves = pd.concat([mn,pd.Series(np.zeros(len(mn))+-1)],axis = 1)
mx_waves.drop_duplicates('waves',inplace = True)
mn_waves.drop_duplicates('waves',inplace = True)
W = mx_waves.append(mn_waves).sort_index()
W = W[ W[0] != W[0].shift() ].dropna()
# Find Support/Resistance with clustering
# Create [x,y] array where y is always 1
X = np.concatenate((W.waves.values.reshape(-1,1),
(np.zeros(len(W))+1).reshape(-1,1)), axis = 1 )
# Pick n_clusters, I chose the sqrt of the df + 2
n = round(len(W)**(1/2)) + 2
cluster = AgglomerativeClustering(n_clusters=n,
affinity='euclidean', linkage='ward')
cluster.fit_predict(X)
W['clusters'] = cluster.labels_
# I chose to get the index of the max wave for each cluster
W2 = W.loc[W.groupby('clusters')['waves'].idxmax()]
# Plotit
fig, axis = plt.subplots()
for row in W2.itertuples():
axis.axhline( y = row.waves,
color = 'green', ls = 'dashed' )
axis.plot( W.index.values, W.waves.values )
plt.show()

Answer 7

Here is the PineScript code for S/Rs. It doesn't include all the logic Dr. Andrew or Nilendu discuss, but definitely a good start:
https://www.tradingview.com/script/UUUyEoU2-S-R-Barry-extended-by-PeterO/
//#version=3
study(title="S/R Barry, extended by PeterO", overlay=true)
FractalLen=input(10)
isFractal(x) => highestbars(x,FractalLen*2+1)==-FractalLen
sF=isFractal(-low), support=low, support:=sF ? low[FractalLen] : support[1]
rF=isFractal(high), resistance=high, resistance:=rF ? high[FractalLen] : resistance[1]
plot(series=support, color=sF?#00000000:blue, offset=-FractalLen)
plot(series=resistance, color=rF?#00000000:red, offset=-FractalLen)
supportprevious=low, supportprevious:=sF ? support[1] : supportprevious[1]
resistanceprevious=low, resistanceprevious:=rF ? resistance[1] : resistanceprevious[1]
plot(series=supportprevious, color=blue, style=circles, offset=-FractalLen)
plot(series=resistanceprevious, color=red, style=circles, offset=-FractalLen)

Answer 8

I'm not sure if it's really "Support & Resistance" detection but what about this:
function getRanges(_nums=[], _diff=1, percent=true) {
let nums = [..._nums];
nums.sort((a,b) => a-b);
const ranges = [];
for (let i=0; i<nums.length; i+=1) {
const num = nums[i];
const diff = percent ? perc(_diff, num) : _diff;
const range = nums.filter( j => isInRange(j, num-diff, num+diff) );
if (range.length) {
ranges.push(range);
nums = nums.slice(range.length);
i = -1;
}
}
return ranges;
}
function perc(percent, n) {
return n * (percent * 0.01);
}
function isInRange(n, min, max) {
return n >= min && n <= max;
}
So let's say you have an array of close prices:
const nums = [12, 14, 15, 17, 18, 19, 19, 21, 28, 29, 30, 30, 31, 32, 34, 34, 36, 39, 43, 44, 48, 48, 48, 51, 52, 58, 60, 61, 67, 68, 69, 73, 73, 75, 87, 89, 94, 95, 96, 98];
and you want to kinda split the numbers by an amount, like difference of 5 (or 5%), then you would get back a result array like this:
const ranges = getRanges(nums, 5, false) // ranges of -5 to +5
/* [
[12, 14, 15, 17]
[18, 19, 19, 21]
[28, 29, 30, 30, 31, 32]
[34, 34, 36, 39]
[43, 44, 48, 48, 48]
[51, 52]
[58, 60, 61]
[67, 68, 69]
[73, 73, 75]
[87, 89]
[94, 95, 96, 98]
]
*/
// or like
//const ranges = getRanges(nums, 5, true) // ranges of -5% to +5%
therefore the more length a range has, the more important of a support/resistance area it is.
(again: not sure if this could be classified as "Support & Resistance")

Answer 9

I briefly read Jacob's contribution. I think it may have some issues with the code below:
# Now the min
if min1 - window < 0:
min2 = min(x[(min1 + window):])
else:
min2 = min(x[0:(min1 - window)])
# Now find the indices of the secondary extrema
max2 = np.where(x == max2)[0][0] # find the index of the 2nd max
min2 = np.where(x == min2)[0][0] # find the index of the 2nd min
The algorithm does try to find secondary min value outside given window, but then the position corresponding to np.where(x == min2)[0][0] may lie inside the the window due to possibly duplicate values inside the window.

Answer 10

If you are looking for horizontal SR lines, I would rather want to know the whole distribution. But I think it is also a good assumption to just take the max of your histogram.
# python + pandas
spy["Close"][:60].plot()
hist, border = np.histogram(spy["Close"][:60].values, density=False)
sr = border[np.argmax(hist)]
plt.axhline(y=sr, color='r', linestyle='-')
You might need to tweak the bins and eventually you want to plot the whole bin not just the lower bound.
lower_bound = border[np.argmax(hist)]
upper_bound = border[np.argmax(hist) + 1]
PS the underlying "idea" is very similar to #Nilendu's solution.

Answer 11

Interpretations of Support & Resistance levels is very subjective. A lot of people do it different ways. […] When I am evaluating S&R from the charts, I am looking for two primary things:
Bounce off - There needs to be a visible departure (bounce off) from the horizontal line which is perceived to define the level of support or resistance.
Multiple touches - A single touch turning point is not sufficient to indicate establish support or resistance levels. Multiple touches to the same approximately level should be present, such that a horizontal line could be drawn through those turning points.