Algorithmic Trading using Python

Algorithmic Trading is the use of algorithms in the financial market to make trading decisions. JP Morgan Chase & Co. is one of the businesses that use Algorithmic Trading for investment decisions. So, if you want to learn how to implement an algorithmic trading strategy, this article is for you. In this article, I will take you through an Algorithmic Trading strategy using Python.

What is Algorithmic Trading?

Algorithmic Trading means using algorithms in buying and selling decisions in the financial market. In an algorithmic trading strategy, a set of predefined rules are used to determine when to buy a financial instrument and when to sell it.

In simple words, Algorithmic Trading is a way of buying and selling automatically and efficiently, which is always better than trading manually.

I hope you have understood what Algorithmic Trading means. In the section below, I will show an implementation of the momentum strategy in Algorithmic Trading using the Python programming language.

Algorithmic Trading using Python

In this section, I will implement an Algorithm Trading strategy known as the momentum strategy on stock price data using Python. In the momentum strategy, we buy the stocks when the momentum is positive and sell the stocks when the momentum is negative.

So let’s import the necessary Python libraries and collect the stock price data of Apple using the yfinance API:

import pandas as pd
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import plotly.express as px
import yfinance as yf

# Get Apple's stock data from yahoo finance
stock = yf.Ticker("AAPL")
data = stock.history(period="1y")
print(data.head())
                                 Open        High         Low       Close  \
Date                                                                        
2022-01-21 00:00:00-05:00  163.471266  165.370249  161.363504  161.472870   
2022-01-24 00:00:00-05:00  159.096635  161.363477  153.807326  160.687393   
2022-01-25 00:00:00-05:00  158.062630  161.820817  156.113949  158.858017   
2022-01-26 00:00:00-05:00  162.556536  163.441400  156.909320  158.768524   
2022-01-27 00:00:00-05:00  161.512596  162.894575  157.366661  158.301239   

                              Volume  Dividends  Stock Splits  
Date                                                           
2022-01-21 00:00:00-05:00  122848900        0.0           0.0  
2022-01-24 00:00:00-05:00  162294600        0.0           0.0  
2022-01-25 00:00:00-05:00  115798400        0.0           0.0  
2022-01-26 00:00:00-05:00  108275300        0.0           0.0  
2022-01-27 00:00:00-05:00  121954600        0.0           0.0  

Now let’s implement the momentum strategy in Algorithmic Trading using Python:

# Calculation of momentum
data['momentum'] = data['Close'].pct_change()

# Creating subplots to show momentum and buying/selling markers
figure = make_subplots(rows=2, cols=1)
figure.add_trace(go.Scatter(x=data.index, 
                         y=data['Close'], 
                         name='Close Price'))
figure.add_trace(go.Scatter(x=data.index, 
                         y=data['momentum'], 
                         name='Momentum', 
                         yaxis='y2'))

# Adding the buy and sell signals
figure.add_trace(go.Scatter(x=data.loc[data['momentum'] > 0].index, 
                         y=data.loc[data['momentum'] > 0]['Close'], 
                         mode='markers', name='Buy', 
                         marker=dict(color='green', symbol='triangle-up')))

figure.add_trace(go.Scatter(x=data.loc[data['momentum'] < 0].index, 
                         y=data.loc[data['momentum'] < 0]['Close'], 
                         mode='markers', name='Sell', 
                         marker=dict(color='red', symbol='triangle-down')))

figure.update_layout(title='Algorithmic Trading using Momentum Strategy',
                  xaxis_title='Date',
                  yaxis_title='Price')
figure.update_yaxes(title="Momentum", secondary_y=True)
figure.show()
Algorithmic trading using momentum strategy

So this is how we can implement an Algorithmic Trading strategy using the momentum strategy. In the above graph, the buy and sell signals are indicated by green triangle-up and red triangle-down markers respectively.

Summary

Algorithmic Trading means using algorithms in buying and selling decisions in the financial market. In an algorithmic trading strategy, a set of predefined rules are used to determine when to buy a financial instrument and when to sell it. I hope you liked this article on Algorithmic Trading using Python. Feel free to ask valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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