The automated trading system is a system that performs trading automatically in the financial market. To develop such a system,
data processing and analysis is essential. Python provides a powerful library for data analysis,
pandas, which is very useful for handling dataframes.
1. What is pandas?
pandas is a widely used library in Python for data manipulation and analysis.
This library allows easy handling of data using a two-dimensional data structure called a dataframe.
A dataframe is similar to an Excel spreadsheet, consisting of rows and columns.
Various data analysis tasks can be performed using dataframes.
1.1 Installing pandas
To install the pandas library, use pip. Just enter the following command in the terminal:
pip install pandas1.2 Basic usage of pandas
To use pandas, first import the library. After importing, you can create a dataframe by generating sample data.
import pandas as pd
# Sample data generation
data = {
'Date': ['2023-01-01', '2023-01-02', '2023-01-03'],
'Price': [100, 102, 105],
'Volume': [200, 220, 210]
}
# Creating dataframe
df = pd.DataFrame(data)
print(df)2. Selecting DataFrame Columns
By selecting specific columns in the dataframe, you can easily manipulate the data for those columns.
Here’s how to select columns:
2.1 Selecting a Single Column
Example of selecting the Price column
price_col = df['Price']
print(price_col)2.2 Selecting Multiple Columns
Example of selecting multiple columns
selected_columns = df[['Price', 'Volume']]
print(selected_columns)2.3 Adding and Modifying Columns
You can also add new columns or modify the values of existing columns.
df['Volatility'] = df['Price'].pct_change() * 100 # Adding a volatility column
print(df)3. Selecting DataFrame Rows
By selecting rows, you can extract data for specific periods or data that meets specific conditions.
Here, we will look at various methods for selecting rows.
3.1 Selecting Rows Using Index
Example of selecting the first row
first_row = df.iloc[0] # Selects the row with index 0
print(first_row)3.2 Selecting Rows Based on a Condition
When selecting rows, you can set conditions to choose only the data that meets those conditions.
price_above_101 = df[df['Price'] > 101]
print(price_above_101)3.3 Selecting Rows Using Multiple Conditions
You can combine multiple conditions to perform complex filtering.
Example of selecting rows using various conditions
filtered_df = df[(df['Price'] > 100) & (df['Volume'] > 200)]
print(filtered_df)4. Using DataFrames: Generating Automated Trading Signals
Now, let’s generate simple automated trading signals using the data we have explored so far.
Here, we will show an example of signal generation using a moving average crossover strategy.
4.1 Preparing Data
import numpy as np
# Creating a dataframe for moving average calculation
df['Short_MA'] = df['Price'].rolling(window=2).mean()
df['Long_MA'] = df['Price'].rolling(window=3).mean()
print(df)4.2 Generating Trading Signals
Example of generating trading signals
df['Signal'] = np.where(df['Short_MA'] > df['Long_MA'], 1, 0) # 1: Buy signal, 0: Sell signal
print(df)5. Conclusion
In this tutorial, we learned how to select columns and rows in a dataframe using Python’s pandas library.
We also learned how to use this to generate simple automated trading signals.
Data processing capability is very important in developing an automated trading system, and pandas makes this task easier.
I encourage you to apply various data analysis techniques to create your own automated trading system!