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

1 Comment / algo trading, Python, Quizzes
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

If you want to master algorithmic trading in Python, understanding the real difference between Python lists and Numpy arrays is crucial. Especially for quantitative traders and those using crypto trading strategies, it’s essential to know why the best algorithmic trading software in the USA and quantitative analysis for trading in Singapore rely on Numpy. This video will explain Numpy’s power, its memory efficiency, and high-performance capabilities in detail.

It will give you a deep insight into the python list and numpy array it will also give you an edge over the others that you know the real difference that why we are using the numpy array in memory here we are storing the addresses like the address of one will be stored here the address of two will be stored here the address of three will be stored here the address of four will be stored here correct on those we are applying mathematical operations so.

It becomes really really crucial to optimize all the things and we cannot do that that with the python list if you’re going with the python list then you will be losing all your money good morning friends this is a beautiful Sunday morning in Singapore and with that let’s start one of the most important topic and python that is the difference between Python list and Nayer is because if you understand this topic it will give you a deep insight into the python list and nump AR it will also give you an edge over the others that you know the real difference that why.

We are using the numpy array why there was a need of numpy array and why we are using numpy arrays in the algo trading not the python list so often for the large data sets right so please Focus for just 15 minutes and I’ll make sure that you have a complete Crystal Clear idea of why we are using nump arrays and that will really make difference in your understanding of the code so with that being said let’s get started for the day 21 of the 100 days of hell with the python algo trading so the first difference is the data type consistency why because.

Advanced NumPy
Advanced NumPy
Advanced NumPy6

We know that the python list can store elements of different data types right this flexibility is useful for certain applications but it comes at the cost of performance and memory efficiency we know that python List have a flexibility that it can store different type of data types in a list but that comes at the cost of the performance and the memory efficiency right again in algo trading you might end up with list containing mixed data types which can lead to unexpected Behavior or errors during numerical operations because in algo trading we have large data sets and inside that.

If we have different kind of data types then sometime it might behave unexpectedly and it might throw error and which can lead to so many losses right you know that so let’s say we have a list which contains all the integers but somehow there is a string between that and it can cause the error correct now let’s check for the numpy array so numpy array require all elements to be of the same data type because numi forces that all the elements should be of same data type because of that it already knows that it is consisting only single data type and it no need to worry that it might throw an error right so this restriction enables numai to perform operations more efficiently because.

It knows the exact type of each element and can optimized C base operation this is very important because we know that numai array was created in the C so so it can optimize using the cbas operations because we know that c is significantly faster than the python so it is using that particular advantage of C over the python list and this is crucial in algo trading where performance is the key.

We know that in algo trading performance is the key we need to optimize everything we need to optimize the code we need to optimize the servers we need to optimize the network we need to optimize the databases and everything should be optimized to get the best performance of any trading system right so integers and here you can see that we have an nump array which consist all the data types of same and it gives us a performance advantage over the python list the next is the performance python list are slower for large scale numerical operations because each element can be of any type right and the list itself doesn’t support vectorized operations this means that numerical operations often require explicit Loops which are slower in Python because we know that python list can have any data type like.

If we have a data from let’s say a price data of the 7even years of any stock let’s say the price data for the Apple from 2010 to 2024 and it is 14 years and it becomes a large data set and when if we are using python list we know that it will be slower why because it might consisting of different data types or because it is also storing the information of the data type of each element and also the reference the address of that particular element so it is definitely slower than the nump array I’ll show you all these examples in the program don’t worry for now just focus on the theory that’s important your concept should be very clear so you can see that we are creating a list comprehension here and now we are calculating the time the start time and here the end time and between that we are applying a loop because in list we have to apply the loops so what.

We are doing we are applying on the 1 million random data and we multiplying that with the 1.01 so for that operation we have to use the loop and Loops are always slower we have already studied that right we always want to avoid the loops and when it comes to the numpy array numpy arrays are designed for numerical operations and support vectorized operation because we know that numai arrays are specially designed in C language and that is significantly faster and nump AR can also support the vectorized operations what is the meaning of vectorized operation here we need to use the loops in Python list but here what it can do it can directly create an array with any random data sets right and after that it can multiply in vectorized operation that means I’ll just show you after this topic the broadcasting in that you will understand that how vectorized operation works but for now you just understand that.

It can directly multiply this 1.01 to all the elements of this numpy array directly right so it has become significantly faster than the python list we’ll just implement this program in the code also just wait wait for a few minutes first let’s complete the theory okay so the first we have seen data type consistency second is performance third is memory efficiency as we already know that the elements of the Python list are stored scatteredly in memory right so first the elements are stored and then the python list is consisting of the address of those elements from the memory and also the information of that which type of data the element is so means it is storing three things the address of the element or the refence we can say and again the information of the data type of that element but in Num array python already knows that it is consisting of one data type it can be anything so it doesn’t need to store the data type information.

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]
“`






 

1 thought on “Advanced NumPy for Algorithmic Trading & Quantitative Analysis 21/100 Days”

Comments are closed.