I Dont Trade – my algo does

Python key concepts for Algo Trading

Written by

RJ

in

If you are starting in algo trading and want to learn python you do not need to be an expert or advanced programmer in python.

Here I have listed some key concepts you should know, and rest can be learned as you start programming.

Most of these concepts can be learnt by googling or asking your favorite AI tool to give you some examples along with code.

Then you can take these bits of code and put it into a free google colab notebook and try to run and understand it

1. Basic Python Syntax and Data Types

a. Variables and Data Types

  • Variables: Containers for storing data values.
  • Data Types: Understand different types such as integers (int), floating-point numbers (float), strings (str), and booleans (bool).

Example:

python
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stock_symbol = "AAPL"  # String
shares = 100           # Integer
price = 150.75         # Float
is_tradeable = True    # Boolean

b. Operators

  • Arithmetic Operators: +, ,, /, //, %, *
  • Comparison Operators: ==, !=, >, <, >=, <=
  • Logical Operators: and, or, not

Example:

python
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total_cost = shares * price  # Multiplication
is_profitable = total_cost > 1000  # Comparison

2. Control Flow

a. Conditional Statements (if, elif, else)

Used to execute code blocks based on certain conditions.

Example:


if is_tradeable:
    print("Executing trade.")
elif shares > 50:
    print("Large trade.")
else:
    print("No action needed.")

b. Loops (for, while)

Used to iterate over sequences (like lists or DataFrames).

Example:


for stock in tradeable_stocks:
    print(stock)

while shares > 0:
    shares -= 1

3. Functions

a. Defining and Calling Functions

Functions allow you to encapsulate reusable code blocks.

Example:

def calculate_total_cost(shares, price):
    return shares * price

total = calculate_total_cost(100, 150.75)
print(total)

b. Parameters and Return Values

Understand how to pass data to functions and receive results.

Example:


def buy_order(stock, quantity, price_target, stop_loss):
    # Execute buy order logic
    return True  # Indicates success

success = buy_order("AAPL", 100, 155.00, 145.00)

4. Data Structures

a. Lists

Ordered collections that are mutable.

Example:


tradeable_stocks = ["AAPL", "AMZN", "GOOGL"]

b. Dictionaries

Unordered collections of key-value pairs, useful for mappings.

Example:


stock_mapping = {
    "AAPL": "AAPU",
    "AMZN": "AMZN",
    "GOOGL": "GOOG"
}

c. Pandas DataFrames

Tabular data structures for data analysis.

Example:


import pandas as pd

data = {
    "Stock_Symbol": ["AAPL", "AMZN"],
    "Signal": ["buy", "sell"],
    "Timeframe": ["daily", "daily"]
}
df = pd.DataFrame(data)
print(df)

5. Modules and Packages

a. Importing Modules

Bringing in external libraries or your own modules to extend functionality.

Example:


import pandas as pd
from datetime import datetime
import logging

b. Creating and Using Custom Modules

Organizing your code into separate files for better manageability.

Example: Assume you have a file named create_order.py with a class AccountsTrading.


from create_order import AccountsTrading

trading_client = AccountsTrading()

6. Error Handling

a. Try-Except Blocks

Handling potential errors gracefully without crashing the program.

Example:


try:
    total_cost = shares * price
except Exception as e:
    logging.error(f"Error calculating total cost: {e}")

b. Raising Exceptions

Manually triggering errors when necessary.

Example:


if shares < 1:
    raise ValueError("Number of shares must be at least 1.")

7. File Input/Output (I/O)

a. Reading Files

Loading data from external files like JSON credentials.

Example:


import json

with open('credentials.json', 'r') as file:
    credentials = json.load(file)

b. Writing to Files

Saving data to external files if needed.

Example:


with open('log.txt', 'w') as file:
    file.write("Trade executed successfully.")

8. Working with Dates and Times

a. The datetime Module

Handling dates and times effectively.

Example:


from datetime import datetime

current_time = datetime.now()
print(current_time.strftime('%Y-%m-%d %H:%M:%S'))

b. Timezones with zoneinfo

Managing timezone-aware datetime objects.

Example:


from zoneinfo import ZoneInfo

eastern = ZoneInfo('America/New_York')
current_time_eastern = datetime.now(eastern)
print(current_time_eastern)

9. Logging

a. Using the logging Module

Recording information, warnings, and errors for monitoring and debugging.

Example:


import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)

logging.info("Trade executed successfully.")
logging.error("Failed to execute trade.")

10. Interacting with External Services and APIs

a. Using APIs

Sending requests to external services (like your trading platform or Google Sheets) and handling responses.

Example:


response = trading_client.get_stock_quote("AAPL", fields="regular")
if response:
    print(response)
else:
    logging.error("Failed to retrieve stock quote.")

b. Google Sheets with gspread

Reading from and writing to Google Sheets for logging purposes.

Example:


import gspread
from oauth2client.service_account import ServiceAccountCredentials

scope = ['<https://spreadsheets.google.com/feeds>', '<https://www.googleapis.com/auth/drive>']
creds = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', scope)
client = gspread.authorize(creds)

spreadsheet = client.open_by_key('SPREADSHEET_KEY')
worksheet = spreadsheet.worksheet('TestTab')
worksheet.append_row(['AAPL', 'AAPU', 100, 'Buy', '2024-04-27 10:00:00', 155.00, 145.00])

c. Email Handling

Fetching and processing emails from Yahoo to extract trading signals.

Example:


emails = read_yahoo_emails(YAHOO_EMAIL, APP_PASSWORD, mailbox="INBOX", num_emails=10, local_tz=local_timezone)

11. Object-Oriented Programming (OOP)

a. Classes and Objects

Organizing code into reusable and modular components.

Example: Assuming AccountsTrading is a class that handles trading operations.


from create_order import AccountsTrading

trading_client = AccountsTrading()
response = trading_client.get_stock_quote("AAPL")

b. Methods

Functions defined within classes to perform actions.

Example:


class AccountsTrading:
    def get_stock_quote(self, symbol, fields):
        # Implementation to get stock quote
        pass

    def create_order(self, order_payload, account_number):
        # Implementation to create an order
        pass

12. Time Management and Throttling

a. The time Module

Pausing the script to avoid overwhelming APIs or to manage execution flow.

Example:


import time as time_module

# Pause for 5 seconds
time_module.sleep(5)

13. String Manipulation

a. String Methods

Modifying and cleaning string data.

Example:

stock_symbol = " aapl "
clean_symbol = stock_symbol.strip().upper()  # "AAPL"
signal = " Buy ".strip().lower()  # "buy"

14. Iterating Through DataFrames

a. Looping with Pandas

Processing each row in a DataFrame to execute trades.

Example:


for index, row in filtered_df.iterrows():
    stock_symbol = row['Stock_Symbol'].upper()
    signal = row['Signal'].strip().lower()
    # Execute trade based on signal

15. Best Practices

a. Writing Readable Code

  • Comments: Use comments to explain what different parts of the code do.
  • Meaningful Variable Names: Use descriptive names for variables and functions.

Example:


# Good variable name
stock_symbol = "AAPL"

# Bad variable name
s = "AAPL"

b. Modular Code

  • Separation of Concerns: Keep different functionalities in separate functions or modules.

Example:

  • Email Processing: Handled by read_yahoo_emails and process_emails.
  • Order Execution: Handled by buy_order and other related functions.

c. Error Handling

  • Graceful Failures: Use try-except blocks to handle potential errors without crashing.

Example:


try:
    # Potential error-causing code
    total_cost = shares * price
except Exception as e:
    logging.error(f"Error calculating total cost: {e}")

16. Practical Tips for Beginners

a. Start Small and Build Up

  • Practice Basic Concepts: Before diving into complex scripts, ensure you understand basic Python syntax and data structures.
  • Incremental Learning: Gradually incorporate more advanced topics like OOP and API interactions as you become comfortable.

b. Utilize Resources

c. Hands-On Practice

  • Build Projects: Continue developing projects like your trading script to apply what you’ve learned.
  • Experiment: Try modifying existing scripts to see how changes affect functionality.

d. Debugging Skills

  • Use Print Statements: Simple print() functions can help you understand what your code is doing.
  • Learn to Read Error Messages: They provide insights into what’s going wrong.
  • Use Debuggers: Tools like Python’s built-in pdb can help step through code.

17. Summary of Key Concepts to Learn

Here’s a summarized list of Python concepts and topics you should focus on:

  1. Basic Syntax and Data Types
    • Variables, integers, floats, strings, booleans
  2. Control Flow
    • Conditional statements (if, elif, else)
    • Loops (for, while)
  3. Functions
    • Defining and calling functions
    • Parameters and return values
  4. Data Structures
    • Lists
    • Dictionaries
    • Pandas DataFrames
  5. Modules and Packages
    • Importing standard and custom modules
    • Installing and using external libraries
  6. Error Handling
    • Try-except blocks
    • Raising exceptions
  7. File I/O
    • Reading from and writing to files
  8. Dates and Times
    • Using datetime and zoneinfo for timezone-aware operations
  9. Logging
    • Configuring and using the logging module
  10. Object-Oriented Programming (OOP)
    • Classes and objects
    • Methods and attributes
  11. API Interactions
    • Making requests to external APIs
    • Handling responses
  12. Time Management
    • Using the time module for pauses
  13. String Manipulation
    • Methods like strip(), upper(), lower()
  14. Iterating Through DataFrames
    • Looping with pandas
  15. Best Practices
    • Writing clean and readable code
    • Modularizing code
    • Effective error handling

18. Moving Forward

Now that you’ve identified the key concepts, here’s a suggested roadmap to guide your learning journey:

  1. Master the Basics:
    • Syntax and Data Types: Ensure you’re comfortable with variables, data types, and basic operations.
    • Control Flow: Practice writing scripts with conditional statements and loops.
  2. Deep Dive into Functions and Modules:
    • Functions: Learn how to write reusable functions.
    • Modules: Understand how to organize code into modules and import them.
  3. Explore Data Structures:
    • Lists and Dictionaries: Manipulate and iterate through these structures.
    • Pandas DataFrames: Start with basic DataFrame operations like creating, reading, filtering, and iterating.
  4. Learn Error Handling:
    • Practice using try-except blocks to handle potential errors in your code.
  5. Work with Dates and Times:
    • Understand how to create, manipulate, and format datetime objects, including timezone management.
  6. Implement Logging:
    • Set up and use the logging module to track your script’s behavior.
  7. Understand OOP Basics:
    • Learn how to define classes, create objects, and use methods to encapsulate functionality.
  8. Interact with External APIs:
    • Practice making API calls, handling responses, and integrating them into your scripts.
  9. Integrate with Google Sheets:
    • Learn how to authenticate and interact with Google Sheets using libraries like gspread.
  10. Build and Refine Projects:
    • Continue enhancing your trading script.
    • Start new projects that challenge you to apply what you’ve learned.
  11. Seek Feedback and Collaborate:
    • Share your code with communities for feedback.
    • Collaborate on projects to gain different perspectives and solutions.

19. Additional Learning Resources

Here are some resources to help you delve deeper into the concepts mentioned:

Another very important resource is the Python Resources page which has my favorite books and courses.

20. Final Thoughts

Embarking on learning Python through a real-world project like an automated trading system is commendable. By systematically understanding and mastering the concepts outlined above, you’ll not only enhance your ability to manage and improve your current script but also empower yourself to tackle more complex and diverse projects in the future.

Remember:

  • Consistency is Key: Regular practice solidifies your understanding.
  • Don’t Fear Mistakes: Errors are learning opportunities.
  • Stay Curious: Explore new libraries and frameworks as you grow.

Congratulations on reaching at the end of this post…very few people do… and best of luck on your Python learning journey!

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