Plotting Charts & Special NumPy Functions for Algorithmic Trading 22/100 Days

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Python data visualization, NumPy sorting functions, NumPy array manipulation, plotting financial data Python, Python charting libraries, NumPy advanced functions, Python NumPy plotting, Python graphing techniques, NumPy statistical functions, Python visual data analysis, NumPy array operations, Python financial charts, NumPy concatenate, NumPy append, NumPy where usage

Floating Graphs, Numpy Functions & Python Charting for Algorithmic Trading

Algorithmic trading is revolutionizing today’s financial markets. It helps traders to implement automated strategies with accuracy. But did you know that algo trading is incomplete without charts?

Using Floating Graphs, we can visualize market trends, which is essential for any trading strategy. Today we will learn how to create different types of charts with the help of Python and Numpy.

Creating Charts in Python using Numpy and Matplotlib

We use two major libraries for data visualization in Python:

  • numpy (for data generation and mathematical operations)
  • matplotlib.pyplot (for creating graphs and charts)

✅ X = Y: Straight Line Chart
First, we created 100 data points ranging from -10 to 10 using numpy.linspace(). Then we created the X=Y graph which shows a straight line.

✅ Y = X²: Parabola Graph
Next, we plotted the graph of Y = X**2, which gives us a parabola. This graph shows how the square of a variable makes the graph move upward.

✅ Y = sin(X): Sine Wave
We also plotted a sine wave using the numpy.sin(X) function. It is very useful in signal processing and time series data.

✅ Y = X*log(X): Logarithmic Graph
This graph shows how the graph of a logarithmic function gradually goes up as X increases. For this, X * np.log(X) was used.

✅ Chart of Sigmoid Function
Sigmoid function is an important non-linear function used in machine learning and trading signals. Its formula:

1 / (1 + np.exp(-X))

Its graph comes in the shape of “S”.

Essential Numpy Functions for Algo Trading

np.sort() – Sort Data
This function sorts the data in ascending order.

Use on 1D Array: Simple sorting

Use on 2D Array: Use axis=0 or axis=1 for Row-wise or Column-wise sorting.

np.sort(array_name, axis=1) # Row-wise
np.sort(array_name, axis=0) # Column-wise

np.append() – Append data to an array
This function is used to add new data to the end of any NumPy array.

  • 1D Array: Can add a single value
  • 2D Array: To add a new row or column, axis has to be set

np.append(array, new_values, axis=1) # Column-wise append

np.concatenate() – Concatenate two arrays
This function concatenates two arrays together.

Axis=0 for row-wise append

Axis=1 for column-wise append

Algorithmic Trading Essentials for Global Traders

If you want to learn quantitative trading in countries like Singapore or USA, then this video can be a great starting point for you. Using Python and open-source tools like Freqtrade, you can create automated trading strategies in cryptocurrency or the stock market.

Watch this Day 22 video tutorial

Day 22: Plotting Charts & Special NumPy Functions

1. What is the primary purpose of using candlestick charts in algorithmic trading?

2. Which matplotlib function is typically used to plot a simple line chart representing stock prices over time?

3. When plotting a moving average on a stock chart, which parameter is crucial for smoothing the data?

4. For a trader analyzing high-frequency trading data, which plot type provides the most information about price volatility within a single day?

5. In a subplot arrangement where multiple stock indices are compared, what feature should be synchronized across all subplots to ensure accurate analysis?

6. Which Python library would you use to interactively explore stock market data with zooming and panning features?

7. What type of visualization would best depict the correlation between different stocks’ returns?

8. How would you visually represent an algorithm’s buy and sell signals on a stock price chart?

9. Which chart type would be most useful for visualizing the distribution of returns from a trading algorithm?

10. What feature of a chart is essential when comparing the performance of an algo-trading strategy against its benchmark?

11. Which NumPy function can be used to calculate the weighted average of stock prices?

12. In NumPy, which function would you use to find the standard deviation for a dataset representing trading volumes?

13. What is the purpose of using np.log() in financial modeling?

14. Which NumPy function is best for generating random numbers that simulate stock price movements for a Monte Carlo simulation?

15. How would you use NumPy to calculate the exponential moving average (EMA) from a series of prices?

16. For a strategy that involves frequent recalculations of portfolio weights based on volatility, which NumPy function can quickly calculate inverse variances?

17. What advantage does np.matmul() offer when calculating returns from multiple securities over several time periods?

18. Which function can be utilized to ensure that all trading algorithm calculations are executed on NumPy arrays without loops for efficiency?

19. In the context of algo trading, why would you use np.clip() on an array of trading signals?

20. What is the primary use of np.diff() in analyzing stock price data?






 

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