Parabolic SAR Calculation and Close-Based Trend Signals
Summary
The document implements the Parabolic Stop and Reverse indicator as a recursive calculation over price data. It initializes the trend, stop level, extreme point, and acceleration factor, then updates them bar by bar. The stop advances toward the extreme point, is constrained by recent highs or lows, and can switch sides when price crosses it. The acceleration factor starts at 0.02, increases by 0.02 when a new extreme is reached, and is capped at 0.2. The code notes that different starting acceleration settings may be used for equities.
A signal layer marks a long position when the calculated SAR is below the close and derives entries or exits from changes in that position. A chart example downloads EA prices over a stated two-year period and plots the close, SAR, and signal markers. This is an indicator and signal demonstration rather than a performance study: it provides no return, transaction-cost, or risk statistics. Its output depends on the recursive initialization and the chosen parameters, and the position rule does not demonstrate a complete short-selling or execution model.
Key ideas
- Parabolic SAR updates its stop using the prior stop, acceleration factor, and current extreme point.
- Recent highs or lows constrain the calculated stop, while a price crossing can reverse the trend state.
- The example raises the acceleration factor when a new extreme is reached, up to a stated cap.
- It turns the SAR position relative to the close into signals based on changes in position.
- The plotted example does not report trading performance or account for transaction costs.
Tags
Full text
# Parabolic SAR backtest.py
```py
# coding: utf-8
# In[1]:
#parabolic stop and reverse is very useful for trend following
#sar is an indicator below the price when its an uptrend
#and above the price when its a downtrend
#it is very painful to calculate sar, though
#and many explanations online including wiki cannot clearly explain the process
#hence, the good idea would be to read info on wikipedia
#and download an excel spreadsheet made by joeu2004
#formulas are always more straight forward than descriptions
#links are shown below
# https://en.wikipedia.org/wiki/Parabolic_SAR
# https://www.box.com/s/gbtrjuoktgyag56j6lv0
import matplotlib.pyplot as plt
import numpy as np
import fix_yahoo_finance as yf
import pandas as pd
# In[2]:
#the calculation of sar
#as rules are very complicated
#plz check the links above to understand more about it
def parabolic_sar(new):
#this is common accelerating factors for forex and commodity
#for equity, af for each step could be set to 0.01
initial_af=0.02
step_af=0.02
end_af=0.2
new['trend']=0
new['sar']=0.0
new['real sar']=0.0
new['ep']=0.0
new['af']=0.0
#initial values for recursive calculation
new['trend'][1]=1 if new['Close'][1]>new['Close'][0] else -1
new['sar'][1]=new['High'][0] if new['trend'][1]>0 else new['Low'][0]
new.at[1,'real sar']=new['sar'][1]
new['ep'][1]=new['High'][1] if new['trend'][1]>0 else new['Low'][1]
new['af'][1]=initial_af
#calculation
for i in range(2,len(new)):
temp=new['sar'][i-1]+new['af'][i-1]*(new['ep'][i-1]-new['sar'][i-1])
if new['trend'][i-1]<0:
new.at[i,'sar']=max(temp,new['High'][i-1],new['High'][i-2])
temp=1 if new['sar'][i]<new['High'][i] else new['trend'][i-1]-1
else:
new.at[i,'sar']=min(temp,new['Low'][i-1],new['Low'][i-2])
temp=-1 if new['sar'][i]>new['Low'][i] else new['trend'][i-1]+1
new.at[i,'trend']=temp
if new['trend'][i]<0:
temp=min(new['Low'][i],new['ep'][i-1]) if new['trend'][i]!=-1 else new['Low'][i]
else:
temp=max(new['High'][i],new['ep'][i-1]) if new['trend'][i]!=1 else new['High'][i]
new.at[i,'ep']=temp
if np.abs(new['trend'][i])==1:
temp=new['ep'][i-1]
new.at[i,'af']=initial_af
else:
temp=new['sar'][i]
if new['ep'][i]==new['ep'][i-1]:
new.at[i,'af']=new['af'][i-1]
else:
new.at[i,'af']=min(end_af,new['af'][i-1]+step_af)
new.at[i,'real sar']=temp
return new
# In[3]:
#generating signals
#idea is the same as macd oscillator
#check the website below to learn more
# https://github.com/je-suis-tm/quant-trading/blob/master/MACD%20oscillator%20backtest.py
def signal_generation(df,method):
new=method(df)
new['positions'],new['signals']=0,0
new['positions']=np.where(new['real sar']<new['Close'],1,0)
new['signals']=new['positions'].diff()
return new
# In[4]:
#plotting of sar and trading positions
#still similar to macd
def plot(new,ticker):
fig=plt.figure()
ax=fig.add_subplot(111)
new['Close'].plot(lw=3,label='%s'%ticker)
new['real sar'].plot(linestyle=':',label='Parabolic SAR',color='k')
ax.plot(new.loc[new['signals']==1].index,new['Close'][new['signals']==1],marker='^',color='g',label='LONG',lw=0,markersize=10)
ax.plot(new.loc[new['signals']==-1].index,new['Close'][new['signals']==-1],marker='v',color='r',label='SHORT',lw=0,markersize=10)
plt.legend()
plt.grid(True)
plt.title('Parabolic SAR')
plt.ylabel('price')
plt.show()
# In[5]:
def main():
#download data via fix yahoo finance library
stdate=('2016-01-01')
eddate=('2018-01-01')
ticker=('EA')
#slice is used for plotting
#a two year dataset with 500 variables would be too much for a figure
slicer=450
df=yf.download(ticker,start=stdate,end=eddate)
#delete adj close and volume
#as we dont need them
del df['Adj Close']
del df['Volume']
#no need to iterate over timestamp index
df.reset_index(inplace=True)
new=signal_generation(df,parabolic_sar)
#convert back to time series for plotting
#so that we get a date x axis
new.set_index(new['date'],inplace=True)
#shorten our plotting horizon and plot
new=new[slicer:]
plot(new,ticker)
#how to calculate stats could be found from my other code called Heikin-Ashi
# https://github.com/je-suis-tm/quant-trading/blob/master/heikin%20ashi%20backtest.py
# In[6]:
if __name__ == '__main__':
main()
```Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.