Python Sets & Dictionaries in Algorithmic Trading & Crypto Strategies 6/100 Days

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Welcome to Day 6: 100 Days of Hell with Python Algo Trading

Today we will take a closer look at two of the most important data types in Python—Sets and Dictionaries. Whether you are into Quantitative Trading or Crypto Strategies, mastering both of these concepts is a must. Today’s session started at 3:30 am in Singapore, and this is how we learn the true meaning of consistency.

If you are feeling demotivated, remember: success is another name for hard work and consistency.

What are Sets?

Sets are an unordered collection in which every element is unique and immutable.

However, the set itself is mutable, meaning you can add or remove new items from it.

Features of Sets:

  • Unordered (no fixed order)
  • No duplicates allowed
  • Mutable in nature

Elements must be immutable (mutable elements like lists will not work)

Set Operations

1. Union

Combining two sets to create a new set containing all unique elements.

a = {‘Apple’, ‘Google’, ‘Tesla’}

b = {‘Tesla’, ‘Amazon’, ‘Microsoft’}

u = a.union(b)

# Output: {‘Apple’, ‘Google’, ‘Tesla’, ‘Amazon’, ‘Microsoft’}

Duplicate elements like ‘Tesla’ will appear only once.

2. Intersection

Shows the common elements present in both sets.

i = a.intersection(b)

# Output: {‘Tesla’}

3. Difference

Removing one set from another.

a – b:

a.difference(b)

# Output: {‘Apple’, ‘Google’}

b – a:

b.difference(a)

# Output: {‘Amazon’, ‘Microsoft’}

4. Symmetric Difference

The set of unique elements remaining after removing common elements from both sets.

a.symmetric_difference(b)

# Output: {‘Apple’, ‘Google’, ‘Amazon’, ‘Microsoft’}

Additional Set Properties
Disjoint Sets
If two sets do not have any common element, then they are called disjoint.

a = {‘Apple’, ‘Google’}
b = {‘Amazon’, ‘Microsoft’}

a.isdisjoint(b) # Output: True

But our main sets (a and b) had ‘Tesla’ in common, so:

a.isdisjoint(b) # Output: False

Subset and Superset
Subset:
If all elements of set A are also present in set B:

a = {‘Apple’, ‘Google’}

b = {‘Apple’, ‘Google’, ‘Amazon’}

a.issubset(b) # Output: True

Python Sets & Dictionaries

Superset:
If all elements of A are present in set B:

b.issuperset(a) # Output: True

Uniqueness Test
If a set has duplicate values, then it will not be called unique.

b = {‘Apple’, ‘Apple’, ‘Google’}
len(b) # Output: 2, because duplicate is removed

Quick Set Examples for Practice

# Set Creation
stocks_a = {‘Apple’, ‘Google’, ‘Tesla’}
stocks_b = {‘Tesla’, ‘Amazon’, ‘Microsoft’}

#Union
print(stocks_a | stocks_b)

#Intersection
print(stocks_a & stocks_b)

#difference
print(stocks_a – stocks_b)
print(stocks_b – stocks_a)

# Symmetric Difference
print(stocks_a^stocks_b)

Watch this Day 6 video tutorial

Day 6: Python Sets and Dictionaries

1. What is the primary advantage of using a set to store unique trade identifiers in algorithmic trading?

2. Which operation is used to find common elements between two sets of stock symbols traded on different days?

3. How do you efficiently check if a stock symbol is part of the trading set?

4. Which set method is used to add multiple items from a list of stock symbols?

5. In a scenario where you need to remove symbols no longer in play without causing an error if they aren’t present, which method would you use?

6. What is the best way to store and access large datasets of stock prices where the keys are dates and values are prices?

7. How can dictionaries enhance performance when managing real-time data feeds in algo trading?

8. When using a dictionary to track the highest trading volume for each stock symbol, which method updates the volume only if the new volume is higher than the existing one?

9. Which of the following is a valid way to iterate over keys and values in a dictionary storing stock prices, printing each stock and its price?

10. In Python dictionaries, what does the popitem() method do, and how can it be used in algo trading?

11. How would you use a Python dictionary to ensure no duplicate processing of trade identifiers seen in a trading session?

12. For a dictionary trade_volumes with stock symbols as keys and volumes as values, what does trade_volumes.setdefault(‘AAPL’, 0) achieve?

13. What is the purpose of using the update() method in a dictionary when processing real-time trade data?

14. Which Python data structure should be used to implement a FIFO (First In, First Out) queue mechanism for trade orders using only standard libraries?

15. When might you use a nested dictionary in algorithmic trading?

16. How do sets differ from dictionaries in Python?

17. What happens when you attempt to access a non-existing key in a dictionary without using get()?

18. Which dictionary method is most suitable for removing a key and getting its value simultaneously during trade error handling?

19. In the context of algo trading, why might a trader use a set instead of a list to store the days on which trades are allowed?

20. What would be the result of the following dictionary operation {‘AAPL’: 200, ‘GOOG’: 800}.get(‘MSFT’, 500)?