Advanced NumPy for Algorithmic Trading & Quantitative Analysis 21/100 Days

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Advanced NumPy tutorial, NumPy broadcasting, NumPy advanced indexing, NumPy performance optimization, Multidimensional arrays NumPy, NumPy matrix operations, Complex array manipulations, NumPy slicing techniques, Efficient NumPy coding, NumPy memory management, High-performance computing with NumPy, NumPy array tricks, Advanced NumPy functions, NumPy vectorization, NumPy array filtering

Day 21: Python List vs Numpy Array – What is the real difference between Algo Trading?

If you want to learn Algorithmic Trading in Python, then one of the most important topics is – the real difference between Python List and Numpy Array. Knowing this difference can be a game changer for you, especially when you are creating crypto trading strategies in the USA or doing quantitative analysis for trading in Singapore.

Why is Numpy Array important for Algorithmic Trading?

Numpy is designed in such a way that it uses memory efficiently and executes high-performance operations very quickly. This is the reason why most of the best algorithmic trading software in the USA and quantitative trading firms in Singapore use Numpy Array instead of Python List.

Python List vs Numpy Array – How is data stored in memory?

When you use a Python List, each item is stored at a different memory address.

For example:

If there are 1, 2, 3, 4 in a list

Python stores a separate address for each element.

Whereas in Numpy array, all the values ​​are stored in a continuous block of memory and hence mathematical operations can be performed faster.

Data Type Consistency – Flexibility of Python List becomes a Weakness

The specialty of Python List is that you can put any data type in it – integer, float, string, everything together.

But in Algorithmic Trading, this flexibility becomes a weakness.

  • If your list contains mixed data types and you perform any numerical operation, it may give an error.
  • For large datasets (such as 14 years of stock price data) it may show unexpected behavior.

All elements in a Numpy array are of the same data type

Numpy automatically ensures that its array is made up of the same type of data — and that is why it is fast and reliable.

How does Numpy array give an edge in performance?

Numpy array is many times faster than Python List.

Why?

  • Python List does not support vectorized operations.
  • So you have to use loops, which are slow in Python.
  • On the other hand, in Numpy you can perform mathematical operations on the entire array at once.

Example:
If you have Apple stock price data from 2010 to 2024, then it can be a dataset of millions of rows.

With Python List, computation in it will be very slow,

While Numpy array can process this data in just a few milliseconds.

Power of C Language – Why is Numpy Faster?

The backend of Numpy is written in C language, which is many times faster than Python.

This means that when you perform any operation on Numpy Array,

it runs at the speed of C code, not Python code.

That is why Numpy is also called “Python’s High-Speed ​​Library”.

Advanced NumPy

Practical Example – Difference in Execution Time

Python List:

my_list = [i for i in range(1000000)]

result = [x * 2 for x in my_list]

Numpy Array:

import numpy as np
my_array = np.arange(1000000)

result = my_array * 2

In the above two examples, the operation on Numpy Array will be executed many times faster than Python List.

Watch this Day 21 video tutorial

Day 21: Advanced Numpy

1. Which of the following is a major advantage of NumPy arrays over Python lists in algorithmic trading?

2. Given the need to handle large datasets in algorithmic trading, why would you prefer NumPy arrays over Python lists?

3. How would you use Boolean indexing to select all positive returns from an array returns?
“`python
positive_returns = returns[________]
“`

4. Given an array prices, how do you use fancy indexing to select elements at indices 2, 4, and 6?
“`python
selected_prices = prices[________]
“`

5. If you have an array prices of shape (10,) and you want to add a constant value 5 to each element, how does broadcasting work in this case?
“`python
adjusted_prices = prices + 5
“`

6. How does broadcasting work when adding a 1D array of shape (3,) to a 2D array of shape (3, 3)?
“`python
result = array_2d + array_1d
“`

7. How do you calculate the exponential moving average (EMA) of an array prices with a smoothing factor alpha?
“`python
ema = np.zeros_like(prices)
ema[0] = prices[0]
for t in range(1, len(prices)):
ema[t] = alpha * prices[t] + (1 – alpha) * ema[t-1]
“`

8. How would you use NumPy to compute the standard deviation of daily returns in an array daily_returns?
“`python
std_dev = np.________(daily_returns)
“`

9. What is the correct NumPy implementation of the sigmoid function applied to an array x?
“`python
def sigmoid(x):
return np.________(1 / (1 + np.exp(-x)))
“`

10. Why is the sigmoid function useful in algorithmic trading models, particularly in logistic regression?

11. How do you calculate the Mean Squared Error (MSE) between predicted and actual returns in NumPy?
“`python
mse = np.________((predicted_returns – actual_returns)**2)
“`

12. Why is the Mean Squared Error important in evaluating algorithmic trading models?

13. How can you replace missing values (NaNs) in an array data with the mean of the non-missing values?
“`python
mean_value = np.nanmean(data)
data[np.isnan(data)] = mean_value
“`

14. Which function is used to identify missing values in a NumPy array?
“`python
missing_values = np.________(data)
“`

15. Which library is commonly used alongside NumPy for plotting graphs in Python?

16. How would you plot a time series of stock prices stored in a NumPy array prices using Matplotlib?
“`python
import matplotlib.pyplot as plt
plt.plot(________)
plt.show()
“`

17. How do you perform element-wise addition of two arrays a and b with broadcasting, given that a has shape (3, 1) and b has shape (1, 3)?
“`python
result = a + b
“`

18. In algorithmic trading, how can you efficiently calculate the daily returns from a time series array prices using NumPy?
“`python
daily_returns = np.diff(prices) / prices[:-1]
“`

19. Given an array weights representing the weights of a portfolio, how do you normalize them to sum to 1 using NumPy?
“`python
normalized_weights = weights / np.sum(weights)
“`

20. How can you use advanced indexing to select specific rows from a 2D array data where the first column is greater than a threshold value?
“`python
selected_rows = data[data[:, 0] > threshold]
“`






 

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