Module: TechnicalAnalysis::MovingAverage

Defined in:
lib/calculation_helpers.rb,
lib/technical_analysis/moving_average.rb,
lib/technical_analysis/moving-average/base.rb,
lib/technical_analysis/moving-average/ka-ma.rb,
lib/technical_analysis/moving-average/exp-ma.rb,
lib/technical_analysis/moving-average/simple-ma.rb,
lib/technical_analysis/moving-average/weighted-ma.rb

Defined Under Namespace

Classes: Base, EMA, ExpMA, KAMA, KaMA, SMA, SimpleMA, WMA, Wma

Class Method Summary collapse

Class Method Details

.ema(current_value, data, period, prev_ema) ⇒ Object

Calculates the exponential moving average (EMA) for the data over the given period en.wikipedia.org/wiki/Moving_average#Exponential_moving_average

z = Symbols::Futures.mini_dax.eod( duration: ‘30 d’).each e= nil ema= z.map{|y| e= TechnicalAnalysis::MovingAverage.ema( y.close, z.map(&:close), 30, e ) }

or

EMA = Struct.new :time, :ema e = nil ema = z.map do |y|

EMA.new y.time, 
        e = TechnicalAnalysis::MovingAverage.ema y.close, z.map(&:close), 30, e

end



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# File 'lib/calculation_helpers.rb', line 44

def self.ema current_value, data, period, prev_ema
  if prev_ema.nil?
    data.sum / data.size.to_f # Average
  else
    (current_value - prev_ema) * (2.0 / (period + 1.0)) + prev_ema
  end
end

.kama(current_value = nil, kama_source = [], period = 10, fast = 2, slow = 30, prev_kama = nil) ⇒ Object

corporatefinanceinstitute.com/resources/knowledge/trading-investing/kaufmans-adaptive-moving-average-kama/



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# File 'lib/technical_analysis/moving_average.rb', line 49

def self.kama current_value=nil, kama_source=[], period=10, fast=2, slow= 30, prev_kama= nil
			# kama_source: Array of values (length: period)
  #              They are used to calculate trend and volatility

  # build the SmootingFactor (alpha)
  # fast = 2 / GDperiod(fast) +1 ;  slow = 2 / GDperiod(slow) +1 

  # kama_source is updated. 

  smoothConst_fast = 2 / (fast +1).to_f
  smoothConst_slow = 2 / (slow +1).to_f

  if current_value.present? 
    kama_source.push current_value 
  else
    current_value = kama_source.last
  end
  if  prev_kama.nil?
    kama_source.sum / kama_source.size.to_f			# return Average --> sma
  else
    kama_source = kama_source[ -period , period ] if kama_source.size > period
    # define the Effiency Ratio to be used by the "kaufmans Adaptive Moving Average" :  kama 
    # ER is calculated by dividing the absolute difference between the
    # current price and the price at the beginning of the period by the sum
    # of the absolute difference between each pair of closes during the
    # period.
      #				| x(t) - x(t-n) |
      # er = ----------------------------
      #			 sum | x(i) - x(i-1) |
      er=	(kama_source.first - kama_source.last).abs  /  
        (1..kama_source.size-1).map{|x| (kama_source[x] - kama_source[x-1]).abs }.sum # rescue 1
      alpha = (er  * ( smoothConst_fast - smoothConst_slow ) + smoothConst_slow ) ** 2
      prev_kama + alpha * (current_value - prev_kama)        
  end
end

.sma(current_value = nil, data = [], period = 15) ⇒ Object

calculates the simple moving average of the current value using the data as reference

if current_value is



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# File 'lib/technical_analysis/moving_average.rb', line 37

def self.sma current_value=nil, data=[], period=15
  sma_source = if current_value.present?
                 data.push  current_value  #  add it as last element
               else
                 data
               end
  sma_source = sma_source[ -period , period ] if sma_source.size > period
  sma_source.sum / sma_source.size .to_f  

end

.wma(current_value = nil, wma_source = [], period = 0) ⇒ Object

Calculates the weighted moving average

Parameter is the data-array.

Takes an optional Block. Specify a method name that returns the data item

Symbols::Futures.mini_dax.eod( duration: ‘30 d’).map do | z |

TechnicalAnalysis::MovingAverage.wma( z ){ :close }

end



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# File 'lib/calculation_helpers.rb', line 61

def self.wma(data)
  intermediate_values = []
  data.each_with_index do |datum, i|
    datum = datum.send yield  if block_given?
    intermediate_values << datum * (i + 1) / (data.size * (data.size + 1) / 2).to_f
  end
  intermediate_values.sum
end