Supertrend is a simple ATR-based trend-following strategy that identifies the current trend and mages the risk dynamically with ATR-based trailing stop-loss. In this blog, we will learn how to implement the Supertrend Algomojo trading strategy using Algomojo and yfinance python library.
Algomojo is a cloud-based trading platform that provides APIs to connect to different brokers and exchanges. yfinance is a Python library that provides historical price data from Yahoo Finance. We will use both Algomojo and yfinance to implement the Supertrend trading strategy.
Requirements
1)Access to Algomojo Free API + Free Algo Platform
2)Python 3.x installed with Visual Studio Code Editor
3)Install Python Libraries – Algomojo, yfinance
pip install algomojo
pip install yfinanceCopy
4)Knowlege of using PlaceSmartOrder Algomojo Function
5)Knowledge of Handling pandas dataframe
yfinance python Library
The yfinance Python library is a popular and widely used package for retrieving financial data from Yahoo Finance. yfinance is a user-friendly and well-documented library that provides an easy way to access financial data, making it an ideal choice for beginners. yfinance is an open-source library and is free to use, making it a cost-effective option for automated trading.
While yfinance is a widely used library, the data it retrieves from Yahoo Finance may not always be reliable, and it may not be suitable for professional trading purposes. For professional trading it is highly recommended to use Authentic data vendors API or Broker API
Importing the Libraries
First, let’s import the necessary libraries.
from algomojo.pyapi import *
from datetime import datetime
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
import time
import threading
Setting Trading and Indicator Parameters
Next, we will set the necessary parameters for our trading strategy.
#set the StrategyName, broker code, Trading symbol, exchange, product and quantity
strategy = "Supertrend Python"
broker = "an"
api_key = "46ad9245cca3dfe957deb235a39d02a3"
api_secret = "30ca108cfb91f458babd2681fb6d0817"
symbol = "RELIANCE-EQ" #Trading Symbol
exchange = "NSE"
product="MIS"
quantity = 10
#yfinance datafeed settings
yfsymbol = "RELIANCE.NS" #Datafeed Symbol
period = "1d"
timeframe = "1m"
#supertrend indicator inputs
atr_period = 5
atr_multiplier = 1.0Here, we are setting the strategy name, broker code, api_key, api_secret, symbol of the security we want to trade, exchange on which the security is listed, product type, quantity of the security we want to trade, yfsymbol for the Yahoo Finance datafeed, period for the historical data, timeframe for the interval of the data, atr_period and atr_multiplier for the Supertrend indicator.
Setting API Key and API secret
To make use of the Supertrend algorithm to trade with Algomojo, we will need to create an API object and specify our API Key and API Secret Key.
algomojo = api(api_key=api_key, api_secret=api_secret)Building Supertrend Function
Next, we need to define a function that will calculate the Supertrend based on a dataframe of historical price data. We will be using yfinance to obtain the price data. Code is inspired by Yong Hong Tan blog
def Supertrend(df, atr_period, multiplier):
high = df['High']
low = df['Low']
close = df['Close']
# calculate ATR
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
# default ATR calculation in supertrend indicator
atr = true_range.ewm(alpha=1/atr_period,min_periods=atr_period).mean()
# df['atr'] = df['tr'].rolling(atr_period).mean()
# HL2 is simply the average of high and low prices
hl2 = (high + low) / 2
# upperband and lowerband calculation
# notice that final bands are set to be equal to the respective bands
final_upperband = upperband = hl2 + (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# initialize Supertrend column to True
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i-1
# if current close price crosses above upperband
if close[curr] > final_upperband[prev]:
supertrend[curr] = True
# if current close price crosses below lowerband
elif close[curr] < final_lowerband[prev]:
supertrend[curr] = False
# else, the trend continues
else:
supertrend[curr] = supertrend[prev]
# adjustment to the final bands
if supertrend[curr] == True and final_lowerband[curr] < final_lowerband[prev]:
final_lowerband[curr] = final_lowerband[prev]
if supertrend[curr] == False and final_upperband[curr] > final_upperband[prev]:
final_upperband[curr] = final_upperband[prev]
# to remove bands according to the trend direction
if supertrend[curr] == True:
final_upperband[curr] = np.nan
else:
final_lowerband[curr] = np.nan
return pd.DataFrame({
'Supertrend': supertrend,
'Final Lowerband': final_lowerband,
'Final Upperband': final_upperband
}, index=df.index)
The Supertrend function takes in a dataframe of historical price data as well as the ATR period and multiplier used in the Supertrend algorithm. It then calculates the ATR and the upper and lower bands, and uses these to determine the Supertrend for each point in the dataframe.
Next, we define our main trading function, which will loop continuously, checking the Supertrend at every iteration and placing trades accordingly.
Main Strategy Function
def supertrend_strategy():
# Get historical data for the stock using Yahoo Finance API
stock = yf.Ticker(yfsymbol)
# Set initial values for variables
position = 0
# Main loop
while True:
# Get historical price data from 5min timeframe
df = stock.history(period=period, interval=timeframe)
close = df.Close.round(2)
supertrend = Supertrend(df, atr_period, atr_multiplier)
# Calculate Supertrend
is_uptrend = supertrend['Supertrend']
# Determine the crossover point
longentry = is_uptrend[-2] and (not is_uptrend[-3])
shortentry = is_uptrend[-3] and (not is_uptrend[-2])
# Place an order if there's a crossover and we don't already have a position
if longentry and position<=0:
# Update position variable
position = quantity
# Place Smart market buy order
response = algomojo.PlaceSmartOrder(broker=broker ,
strategy=strategy,
exchange=exchange,
symbol=symbol,
action="BUY",
product=product,
pricetype="MARKET",
quantity=quantity,
price="0",
position_size=position,
trigger_price="0",
disclosed_quantity="0",
amo="NO",
splitorder="NO",
split_quantity="1")
print ("Buy Order Response :",response)
# Close position if there's a crossover and we already have a position
elif shortentry and position>=0:
# Update position variable
position = quantity*-1
# Place Smart market sell order
response = algomojo.PlaceSmartOrder(broker=broker,
strategy=strategy,
exchange=exchange,
symbol=symbol,
action="SELL",
product=product,
pricetype="MARKET",
quantity=quantity,
price="0",
position_size=position,
trigger_price="0",
disclosed_quantity="0",
amo="NO",
splitorder="NO",
split_quantity="1")
print ("Sell Order Response :",response)
# Print current position and price data
print("Position:", position)
print("LTP:", close[-1])
print("Supertrend:", supertrend['Supertrend'][-2])
print("LowerBand:", supertrend['Final Lowerband'][-2].round(2))
print("UpperBand:", supertrend['Final Upperband'][-2].round(2))
print("longentry:", longentry)
print("shortentry:", shortentry)
# Wait for 15 seconds before checking again
time.sleep(15)The complete Supertrend Python strategy (supertrend.py)
#For Complete Algomojo Python Documentation Visit - https://pypi.org/project/algomojo/
#Coded by Rajandran R - www.marketcalls.in / www.algomojo.com
from algomojo.pyapi import *
from datetime import datetime
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
import time
import threading
#set the StrategyName, broker code, Trading symbol, exchange, product and quantity
strategy = "Supertrend Python"
broker = "an"
api_key = "46ad9245cca3dfe957deb235a39d02a3"
api_secret = "30ca108cfb91f458babd2681fb6d0817"
symbol = "RELIANCE-EQ" #Trading Symbol
exchange = "NSE"
product="MIS"
quantity = 10
#yfinance datafeed settings
yfsymbol = "RELIANCE.NS" #Datafeed Symbol
period = "1d"
timeframe = "1m"
#supertrend indicator inputs
atr_period = 5
atr_multiplier = 1.0
# Set the API Key and API Secret key obtained from Algomojo MyAPI Section
algomojo = api(api_key=api_key, api_secret=api_secret)
def Supertrend(df, atr_period, multiplier):
high = df['High']
low = df['Low']
close = df['Close']
# calculate ATR
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
# default ATR calculation in supertrend indicator
atr = true_range.ewm(alpha=1/atr_period,min_periods=atr_period).mean()
# df['atr'] = df['tr'].rolling(atr_period).mean()
# HL2 is simply the average of high and low prices
hl2 = (high + low) / 2
# upperband and lowerband calculation
# notice that final bands are set to be equal to the respective bands
final_upperband = upperband = hl2 + (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# initialize Supertrend column to True
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i-1
# if current close price crosses above upperband
if close[curr] > final_upperband[prev]:
supertrend[curr] = True
# if current close price crosses below lowerband
elif close[curr] < final_lowerband[prev]:
supertrend[curr] = False
# else, the trend continues
else:
supertrend[curr] = supertrend[prev]
# adjustment to the final bands
if supertrend[curr] == True and final_lowerband[curr] < final_lowerband[prev]:
final_lowerband[curr] = final_lowerband[prev]
if supertrend[curr] == False and final_upperband[curr] > final_upperband[prev]:
final_upperband[curr] = final_upperband[prev]
# to remove bands according to the trend direction
if supertrend[curr] == True:
final_upperband[curr] = np.nan
else:
final_lowerband[curr] = np.nan
return pd.DataFrame({
'Supertrend': supertrend,
'Final Lowerband': final_lowerband,
'Final Upperband': final_upperband
}, index=df.index)
def supertrend_strategy():
# Get historical data for the stock using Yahoo Finance API
stock = yf.Ticker(yfsymbol)
# Set initial values for variables
position = 0
# Main loop
while True:
# Get historical price data from 5min timeframe
df = stock.history(period=period, interval=timeframe)
close = df.Close.round(2)
supertrend = Supertrend(df, atr_period, atr_multiplier)
# Calculate Supertrend
is_uptrend = supertrend['Supertrend']
# Determine the crossover point
longentry = is_uptrend[-2] and (not is_uptrend[-3])
shortentry = is_uptrend[-3] and (not is_uptrend[-2])
# Place an order if there's a crossover and we don't already have a position
if longentry and position<=0:
# Update position variable
position = quantity
# Place Smart market buy order
response = algomojo.PlaceSmartOrder(broker=broker ,
strategy=strategy,
exchange=exchange,
symbol=symbol,
action="BUY",
product=product,
pricetype="MARKET",
quantity=quantity,
price="0",
position_size=position,
trigger_price="0",
disclosed_quantity="0",
amo="NO",
splitorder="NO",
split_quantity="1")
print ("Buy Order Response :",response)
# Close position if there's a crossover and we already have a position
elif shortentry and position>=0:
# Update position variable
position = quantity*-1
# Place Smart market sell order
response = algomojo.PlaceSmartOrder(broker=broker,
strategy=strategy,
exchange=exchange,
symbol=symbol,
action="SELL",
product=product,
pricetype="MARKET",
quantity=quantity,
price="0",
position_size=position,
trigger_price="0",
disclosed_quantity="0",
amo="NO",
splitorder="NO",
split_quantity="1")
print ("Sell Order Response :",response)
# Print current position and price data
print("Position:", position)
print("LTP:", close[-1])
print("Supertrend:", supertrend['Supertrend'][-2])
print("LowerBand:", supertrend['Final Lowerband'][-2].round(2))
print("UpperBand:", supertrend['Final Upperband'][-2].round(2))
print("longentry:", longentry)
print("shortentry:", shortentry)
# Wait for 15 seconds before checking again
time.sleep(15)
# Create and start a new thread to run the strategy
t = threading.Thread(target=supertrend_strategy)
t.start()
