Support Resistance Algorithm - Technical analysis
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?
Solution 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.
I hope this helps. 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 ^_^
Solution 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 + "]";
}
}
Solution 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