Step by Step Guide to Python For Finance And Algorithmic Trading

Each day, newer technology springs up, compelling traditional financial institutions to evolve into fintech startups and technology firms. Computers help push the finance world by using mathematical algorithms to bring about speed and innovation in problem-solving processes. 

Algorithmic trading, sometimes called Quantitative trading, refers to the process of a sophisticated part of finance that involves developing and designing trading strategies. These strategies are, however, based on statistical and mathematical analyses. 

Programming languages like C++, R, C#, and Java are among the most-used languages for finance, but none does financial analysis like Python. In this article, you’ll learn how to use Python for financial analysis and algorithmic trading.

Table Of Contents

• Step by Step Guide to Python For Finance And Algorithmic Trading
• What Is Algorithmic Trading?
• What Is Python?
• What Is Financial Trading?
• The Role That The Python Language Plays In The Finance Industry
• What Information Can Be Imported/Exported With Python?
• How Do You Choose a Programming Language For Algorithmic Trading?
• Here’s Why People Use Python For Finance and Algorithmic Trading
• Financial Analysis and Trading Strategy In Python
• Conclusion

Step by Step Guide to Python For Finance And Algorithmic Trading

Top programmers often advise newcomers keen on financial markets and algorithmic trading to rely on Python. For starters, Python is a popular computer language that investors and institutions use daily for various tasks, such as quantitative research, prototyping, testing, and the execution of trading algorithms.

While this interesting language in algorithmic trading may have been restricted to just the big, rich, and technical industry players, times have changed a lot. With several steps outlined in this guide, you can sit in the comfort of your home and use Python for finance and algorithmic trading. First, let’s define some really important terms.

What Is Algorithmic Trading?

Algorithmic Trading is sometimes called black-box trading or automated trading, and it uses a computer program that obeys a defined set of instructions/algorithms to place trades. Such trades done via algorithms can generate profits at frequencies and speeds that a human trader will find impossible.

Besides, the defined instructions of algorithmic trading are generally based on price, mathematical models, quantity, and timing. Algorithmic trading makes the market more liquid while keeping trading more systematic, and it does this by removing the impact of human emotions regarding trading activities.

The computerization of trading systems in the American financial markets in the 1970s saw the increase of algorithms in trading. In 1976, the NYSE took things up a notch and introduced the Designated Order Turnaround system (DOT).

It used DOT to route orders from traders to specialists on the exchange floor. As of 2009, computers executed more than 60% of trades in the U.S., with exchanges accepting computers in trading.

What Is Python?

Python is a hugely popular choice among algorithmic traders in recent times for several reasons. It is open-source, and all of its packages are free and available for commercial use, making it one of the top and natural choices.

It is an interpreted, high-level programming language with dynamic semantics. Python’s high-level built-in data structures, dynamic binding, and dynamic typing make it an attractive choice for Rapid Application Development. It is also great for scripting or glue language, connecting existing components. 

Python is favored among programmers for its easy-to-learn syntax that emphasizes readability while reducing the cost of program maintenance. Meanwhile, Python supports packages and modules while encouraging code reuse and program modularity and is a solid choice for financial trading.

What Is Financial Trading?

Financial trading simply refers to the buying and selling of financial assets or instruments to make a profit. These instruments could be shares, foreign exchange (FOREX), derivatives, futures, and options. You can either do financial trading over the counter or via an exchange. Financial trades are done in the financial market where millions of individuals, companies, and governments trade at once. 

While some traders prefer to stick to a specific instrument or asset, others lean towards a more diverse portfolio after conducting research and observing trends and charts. One thing about financial trading is that it carries, and risk is a key concept to profitability in this line of work, irrespective of the instrument involved. This is one of the reasons why concepts like algorithmic trading exist and why Python language plays a significant role in finance today.

Financial Analysis and Trading Strategy In Python

Stock trading is the process of buying and selling both previously issued and existing stocks. The major determinant of the ever-fluctuating price of stocks is supply and demand in the share market. A company’s action and performance may move the stock price upwards and down, but this movement isn’t limited to its actions alone. 

Stock traders pay money in exchange for ownership within a chosen company with hopes of making profitable trades and selling the stocks at a higher price. There are several strategies that traders can use to make a profit. Quantitative traders at investment banks and hedge funds often develop some of these trading strategies and the framework for testing them.

However, developing these strategies requires programming experience and expertise and a solid understanding of the programming languages they need to build a specific strategy. 

Here’s where Python comes in. Python is undoubtedly one of the most popular languages in programming. Here are some quick tips to creating a strategy in Python stock trading:

• Select the product you want to trade.
• Choose and install the software.
• Create and Set up your account with a broker.
• Understand your strategy and set up your MT4.
• Understand the bits and intricate parts of an MT4 trading algorithm.
• Code all the rules your strategy needs for entering and exiting trades.
• Try running a historical test with your algorithm.
• Run it live on fake money.

The Role That The Python Language Plays In The Finance Industry

In recent times, more algorithmic traders are adopting Python for finance due to Python’s large community, third-party support, and easy syntax. It is also being adopted across several other domains, including data science and algorithmic trading. There are so many broad ranges of applications of Python to the finance world. These are some of the important roles that the Python language plays in the finance industry:

• Banking Software: In the finance world, finance organizations use python to build payment solutions and tons of online banking platforms, including Robinhood and Venmo. This is all thanks to the programming language’s flexibility and simplicity. It is also useful in developing automated teller machine (ATM) software that makes payment processing straightforward.

• Analytics Tools In Quantitative Finance: Python is popularly used in quantitative finance. It offers resources to create solutions to process and analyze large financial data. What’s more, libraries like Pandas or Scikit help make the process of data visualization simple while permitting complex statistical calculations.

• Python Stock Trading Strategy: The Python language is also useful in the stock markets when used to build a stock trading strategy. Naturally, stock markets turn out lots of financial data that require analysis. However, developers can create solutions that recognize the best stock trading strategies with Python. In addition, they can offer predictive analytical views regarding the condition of specific markets. Nowadays, fintech products companies use it in algorithmic trading, getting access to some of the best trading strategies Python has to offer. 

• Cryptocurrency: Businesses that sell crypto require tools to carry out their crypto market data analysis to obtain predictions and insights. Anaconda, Python’s data science ecosystem, helps developers retrieve crypto pricing and analyze it while visualizing financial data. Most web apps like Dash and ZeroNet, that deal with cryptocurrency analysis, leverage Python.

What Information Can Be Imported/Exported With Python?

As you run Python programs, you may need to use datasets for data analysis. Python has different modules that help users import or export external data in various file formats to a Python program. Here’s a look at how to import data of different formats to a Python program:

#1. Using Pandas

Panda library can handle most file types, one of which is the CSV file. Here’s how the pandas library handles excel files via the read_excel module.

import pandas as pd




df = pd.ExcelFile("E:\\customers.xlsx")

data=df.parse("customers")

print(data.head(10))

#2. Importing csv file

With the csv module, you can read each row in the file while using a comma to act as a delimiter. The first thing to do is open the file in read-only mode. Next, assign the delimiter and finally use a for loop to read every row from your CSV file.

import csv




with open("E:\\customers.csv",'r') as custfile:

rows=csv.reader(custfile,delimiter=',')

for r in rows:

print(r)

#3. When Using pyodbc

Python users can also connect to database servers using a module named pyodbc. This helps you import data from relational sources while using a SQL query. However, there’s the need to define the connection details to the db before passing on the query.

import pyodbc

sql_conn = pyodbc.connect("Driver={SQL Server};Server=serverName;UID=UserName;PWD=Password;Database=sqldb;")

data_sql = pd.read_sql_query(SQL QUERY’, sql_conn)

data_sql.head()

How Do You Choose a Programming Language For Algorithmic Trading?

One of the first things you need to become an algorithmic trader is the knowledge of popular programming languages. However, as technology advances, it proves even more difficult and unnecessary to try and learn all programming languages to make a choice. So how do you choose which programming language to use for your algorithmic trading? While there are no perfect languages, you should consider the following concepts in the entire trading process before you make a choice:

• Performance
• Resiliency
• Modularity 
• Ease of maintenance
• Cost of maintenance
• Ease of installation
• Documentation of the programming language
• The activities of a programming language’s community

Every programming language has its advantages and disadvantages regarding its applications in financial analysis. The trick is in finding a balance between the great and not-so-great features based on your preferred trading system. This is important because most organizations or individuals will go for a programming language based on their type of business and culture.

Here’s Why People Use Python For Finance and Algorithmic Trading

Python stock trading allows users to easily build statistical models while using sufficient scientific libraries made available. For most algorithmic trading developers, choosing a programming language is usually a trade-off between a language being proprietary tech or an open-source tech. Here’s a look at some of the benefits of Python and why it might be preferred for financial analysis:

• Python makes writing and evaluating algorithm trading structures easier. This is thanks to Python’s functional programming approach since programmers can extend their codes to dynamic algorithms for trading.
• Python has great computational power and parallelization that gives the trading portfolio scalability.
• Compared to languages like C++ or C, Python trumps them when it comes to easily and quickly developing great trading platforms.
• It is easier to revert new modules to Python language while making it expansive in trading.
• Python is great for users who prefer to be pioneers with dynamic Algo trading platforms.
• Its code is a good start for algorithmic trading newbies because it is readily accessible and easily readable.
• The availability of extensive Python libraries means you need fewer lines of code when using python for trading.
• Python’s modules make it convenient for Algo traders to share their functionality among various programs. They do this by decomposing the functionality into individual modules that apply to different trading architectures.
• With python, coding is relatively easier when trading. For instance, Quant traders get to skip different steps that languages such as C++ or C might request. What’s more, Python brings down the maintenance cost for the trading system.
• Finally, Python has a wide range of scientific libraries that allows algorithmic traders to perform almost any type of data analysis and at high speed, only compiled languages like C++ can boast of or offer.

These are some of the reasons why Python for finance is a great choice. However, that is not to say there aren’t some drawbacks to using python for financial analysis and algorithmic trading. For one, every variable in Python is an object, and what this means is that every variable stores unnecessary info such as reference pointer, value, and size.

So when you store variables running into millions, if your memory management isn’t top tier and done effectively, you might have a memory leak or performance bottleneck. But despite this, the benefits of using python for financial analysis and algorithmic trading outweigh the cons/disadvantages.

Steps To Using Python For Finance and Algorithmic Trading 

For finance professionals who are new to the Python programming language and Python for financial analysis and algorithmic trading, these are some steps to getting started:

Step #1. Sign Up For a Commission Free Brokerage Platform

First, create an account with a brokerage platform that allows trading through an API (Application Programming Interface). After doing so, you will get an API Key ID and a private key that you will reference in the Python script. It will create a bridge to automate all your trading strategies.

Step #2. Leverage a Cloud Service 

Using a cloud service such as Google means that you don’t need to manually run your script or worry about your computer staying on at the correct time daily. Google Cloud Platform or GCP is a top-level cloud computing service that offers tons of cloud products, but you may need just two for your Python for finance project.

Step #3. Python Script

Next, you need to structure the Python script, attach your API, and send mail notifications. Your Python script needs to have only one function, and that function will be called in Google Cloud, and it is important in automating the script. After doing this, access and connect your API. 

def pairs_trading_algo(self):

    
    '''All the code necessary to connect to API, trading logic and sending email notification will go in here'''


    return done

Step #4. Connect Your API

For this example, we use a code that uses Alpaca Resources as it’s brokerage platform. Get your API keys from your dashboard. Next, you need to import these packages os, and alpaca_trade_api as tradeapi. 

Go through with Alpaca’s documentation on how users can download alpaca_trade_api. Here is a look at the code obtained from the GitHub Repo. 

The os.environ section will permit you to lay down what environment you want to connect to, whether live trading or paper trading. 

You will place your API Key ID at APCA_API_KEY_ID, and your secret key at  APCA_API_SECRET_KEY. Know that the account variable is there to make sure you have an active account with the brokerage platform Alpaca. 

import os

import alpaca_trade_api as tradeapi

def pairs_trading_algo(self):

    # Specify paper trading environment

    os.environ['APCA_API_BASE_URL'] = 'https://paper-api.alpaca.markets'




    # Insert API Credentials

    api = tradeapi.REST('APCA_API_KEY_ID', 'APCA_API_SECRET_KEY', api_version='v2')

    account = api.get_account()



    return done

Step #5. Create A New Email Account and Add An Email Notification Functionality To Python Function

You need to add email notifications to your trading script to help you know when your script is running. It also helps you know the outcomes that are based on the trading strategy. You may need to create a new email account for security reasons. Then turn on access to less secure sites.

The code below carries the packages needed to send emails. Note that the email’s subject will be your trading strategy, allowing you to run multiple strategies as you stay up-to-date with their successes. 

import os

import alpaca_trade_api as tradeapi

import smtplib

from email.mime.multipart import MIMEMultipart

from email.mime.text import MIMEText





def pairs_trading_algo(self):

    # Specify paper trading environment

    os.environ['APCA_API_BASE_URL'] = 'https://paper-api.alpaca.markets'




    # Insert API Credentials

    api = tradeapi.REST('APCA_API_KEY_ID', 'APCA_API_SECRET_KEY', api_version='v2')

    account = api.get_account()




    # The mail addresses and password

    sender_address = 'example@domain.com'

    sender_pass = 'P4ssw0rd'

    receiver_address = 'example@domain.com'




    # Setup MIME

    message = MIMEMultipart()

    message['From'] = 'Trading Bot'

    message['To'] = receiver_address

    message['Subject'] = 'Pairs Trading Algo'  # The subject line




    return done

Step #6. Build a Trading Strategy Into The Python Script and Input Certain Messages To Email

Below is an example that uses ADBE and AAPL. More so, this code is available in the GitHub Repo.

        # Selection of stocks

    days = 1000

    stock1 = 'ADBE'

    stock2 = 'AAPL'




    # Put Hisrorical Data into variables

    stock1_barset = api.get_barset(stock1, 'day', limit=days)

    stock2_barset = api.get_barset(stock2, 'day', limit=days)

    stock1_bars = stock1_barset[stock1]

    stock2_bars = stock2_barset[stock2]




    # Grab stock1 data and put in to a array

    data_1 = []

    times_1 = []

    for i in range(days):

        stock1_close = stock1_bars[i].c

        stock1_time = stock1_bars[i].t

        data_1.append(stock1_close)

        times_1.append(stock1_time)




    # Grab stock2 data and put in to an array

    data_2 = []

    times_2 = []

    for i in range(days):

        stock2_close = stock2_bars[i].c

        stock2_time = stock1_bars[i].t

        data_2.append(stock2_close)

        times_2.append(stock2_time)




    # Putting them together

    hist_close = pd.DataFrame(data_1, columns=[stock1])

    hist_close[stock2] = data_2




    # Current Spread between the two stocks

    stock1_curr = data_1[days-1]

    stock2_curr = data_2[days-1]

    spread_curr = (stock1_curr-stock2_curr)




    # Moving Average of the two stocks

    move_avg_days = 5




    # Moving averge for stock1

    stock1_last = []

    for i in range(move_avg_days):

        stock1_last.append(data_1[(days-1)-i])




    stock1_hist = pd.DataFrame(stock1_last)




    stock1_mavg = stock1_hist.mean()




    # Moving average for stock2

    stock2_last = []

    for i in range(move_avg_days):

        stock2_last.append(data_2[(days-1)-i])

    stock2_hist = pd.DataFrame(stock2_last)

    stock2_mavg = stock2_hist.mean()




    # Sread_avg

    spread_avg = min(stock1_mavg - stock2_mavg)




    # Spread_factor

    spreadFactor = .01

    wideSpread = spread_avg*(1+spreadFactor)

    thinSpread = spread_avg*(1-spreadFactor)




    # Calc_of_shares_to_trade

    cash = float(account.buying_power)

    limit_stock1 = cash//stock1_curr

    limit_stock2 = cash//stock2_curr

    number_of_shares = int(min(limit_stock1, limit_stock2)/2)

The Next step should involve adding your trading logic with the desired text for the email. Know that the portfolio variable exists to see what your current position is. Doing this is crucial to the trading algo logic.

    # Trading_algo

    portfolio = api.list_positions()

    clock = api.get_clock()




    if clock.is_open == True:

        if bool(portfolio) == False:

            # detect a wide spread

            if spread_curr > wideSpread:

                # short top stock

                api.submit_order(symbol=stock1, qty=number_of_shares, side='sell', type='market', time_in_force='day')




                # Long bottom stock

                api.submit_order(symbol=stock2, qty=number_of_shares, side='buy', type='market', time_in_force='day')

                mail_content = "Trades have been made, short top stock and long bottom stock"




            # detect a tight spread

            elif spread_curr < thinSpread:

                # long top stock

                api.submit_order(symbol=stock1, qty=number_of_shares, side='buy', type='market', time_in_force='day')




                # short bottom stock

                api.submit_order(symbol=stock2, qty=number_of_shares, side='sell', type='market', time_in_force='day')

                mail_content = "Trades have been made, long top stock and short bottom stock"

        else:

            wideTradeSpread = spread_avg * (1+spreadFactor + .03)

            thinTradeSpread = spread_avg * (1+spreadFactor - .03)

            if spread_curr <= wideTradeSpread and spread_curr >= thinTradeSpread:

                api.close_position(stock1)

                api.close_position(stock2)

                mail_content = "Position has been closed"

            else:

                mail_content = "No trades were made, position remains open"

                pass

    else:

        mail_content = "The Market is Closed"




    # The body and the attachments for the mail

    message.attach(MIMEText(mail_content, 'plain'))




    # Create SMTP session for sending the mail

    session = smtplib.SMTP('smtp.gmail.com', 587)  # use gmail with port

    session.starttls()  # enable security




    # login with mail_id and password

    session.login(sender_address, sender_pass)

    text = message.as_string()

    session.sendmail(sender_address, receiver_address, text)

    session.quit()




    done = 'Mail Sent'




    return done

Observe that we write mail_content throughout our trading algorithm. This is so that it catches whatever occurs depending on the day. Besides, when we place it in the email, the information for the log-in is fulfilled, and the email is sent.

Step #7. Create a Google Cloud Function

The next step involves opening a Google cloud console. Make sure you’re in an algorithmic trading project and find your way to Cloud Functions on your left panel. Create a function by clicking “create function.” Name the function but keep the preset settings, although you may need to change the Runtime to Python 3.7.

Now copy and paste the Python script you made into main.p.y while putting the name of your function in the Function to Execute box. Next, add the necessary packages in the requirements.txt. to make the cloud know the packages you require for your Python script to run and then deploy the function. 

Step #8. Create A Google Scheduler

Finally, go to Google scheduler under Tools on the left panel and create a new job. Name it but change the frequency to every weekday at 8:30 AM or an hour after the market resumes. Next, paste the URL copied from the Cloud Function. After doing this, it should run as specified, and you can test the job by clicking on Run. If it is successful, you should receive an email.  

Conclusion

Computer software and tech have become indispensable assets in the ever-changing world of finance. Some of the biggest financial institutions are drawn to the frequency and speed of Python for finance and the large data volumes it processes.

After doing this, it should run as specified, and you can test the job by clicking on Run. If it is successful, you should receive an email.