Using Python Lambda Function in Trading – Part II

Articles From: QuantInsti
Website: QuantInsti

See Part I for an overview of lambda function.

Python lambda in trading

With Python’s lambda function, we can write several codes for different trade related inputs. Let us see the examples for trading below.

# Lambda construct with a single argument
x = [23,34,55]
func = lambda x: max(x)
print (func(x))

# Lambda construct with multiple arguments
price,volume = 60,20
func = lambda price,volume: price/volume
print(func(price,volume))

# Lambda construct with logical operators
signal = "SELL"
func = lambda x: x>50 and signal == "BUY"
func(65)

# Lambda construct with conditional expressions like the if..else statement and comparison operators
func = lambda x: "BUY" if x > 45 else "SELL"
signal = func(65)
print(signal)

# We can also construct lambda with multiple if..else statements in the following manner
func = lambda x: "BUY" if x <=  30 else "SELL" if x>= 70 else "None"
signal = func(65)
print(signal)

# The filter function is used to extract each element in the iterable object
# for which the function returns True. In this case, we will define the function
# using the lambda construct and apply the filter function

Signal = ['Buy','Sell', None, 'Sell', 'Sell', 'Sell']
list(filter(lambda x: x == 'Buy' or x == 'Sell', Signal))

# The reduce function is a unique function which reduces the input list to a single value
# by calling the function provided as part of the argument.  The reduce function by default
# starts from the first value of the list and passes the current output along the next item
# from the list.

from functools import reduce
reduce((lambda x,y: x+y),[2,1.35,-2.4,3])
Python_lambda_trading.py hosted with ❤ by GitHub

Benefits of lambda function in trading

There are quite a few benefits of using lambda function, even for trading. Let us see those benefits below.

Continuous scaling

Lambda precisely manages the scaling of your functions (or application) by running codes parallelly for different trading inputs, and processes each input individually.

Helps in time management

Lambda frees up your programming resources by taking over the time consumed by defining several functions. With lambda, you do not need to define every function and hence, you can save lot of time.

Modernises your trading business

Lambda enables you to use functions with pre-trained machine learning models to include artificial intelligence in your trade easily. A single application programming interface (API) request can classify images, analyze videos, convert speech to text, perform natural language processing, and more.


Conclusion

Lambda function is a useful function for traders who code with the help of Python. Once known by the programmer, it is the preferred function since it helps manage time, is quick while coding and has advanced operations.

Visit QuantInsti for additional insight on this topic: https://blog.quantinsti.com/using-python-lambda-function-in-trading/.

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This material is from QuantInsti and is being posted with its permission. The views expressed in this material are solely those of the author and/or QuantInsti and Interactive Brokers is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to buy or sell any security. It should not be construed as research or investment advice or a recommendation to buy, sell or hold any security or commodity. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

Disclosure: API Examples Discussed

Throughout the lesson, please keep in mind that the examples discussed are purely for technical demonstration purposes, and do not constitute trading advice. Also, it is important to remember that placing trades in a paper account is recommended before any live trading.