Your Python file runs. Now we can give Python a few specialized tools for market analysis.
These tools are called packages. Before installing anything, it is worth understanding what a package is and why we need it.
What you will finish: you will understand the role of three important packages, install them, and confirm that Python can use them.
1. What Is a Python Package?
Python comes with many useful features, but it does not include every tool for every possible job.
A Python package is a collection of extra code that adds new abilities to Python. Think of Python as a basic workshop and packages as specialized toolboxes that you add when you need them.
Python
├── basic language
├── variables
├── loops
├── functions
└── + packages for specialized workFor market research, we often need tools to download prices, organize tables, calculate new values, and draw charts. Packages let us use tools that other developers have already built instead of creating everything from zero.
2. What Is pip?
If a package is a toolbox, pip is the tool that installs that toolbox into Python.
python -m pip install ...The word install means: “download this package and make it available to this Python environment.”
We use python -m pip instead of only pip because it asks the current Python interpreter to run pip. This helps reduce a common beginner problem: installing a package into one Python while VS Code is using another.
3. The Three Packages We Need First
Our first Alphesta workflow uses three packages. Each one has a different job.
FinanceDataReader — Get Market Data
FinanceDataReader helps Python retrieve financial market data.
Later, we will use it to request daily Open, High, Low, Close, and Volume values instead of copying rows manually from a website.
Market
↓
FinanceDataReader
↓
Price data enters PythonWe usually import it like this:
import FinanceDataReader as fdras fdr gives the package a shorter nickname.
pandas — Organize and Calculate Data
After market data enters Python, we need a convenient way to work with it. That is where pandas comes in.
pandas is especially useful for data arranged in rows and columns. If you have used Excel, this idea will feel familiar.
Date Open High Low Close Volume
2026-07-01 ... ... ... ... ...
2026-07-02 ... ... ... ... ...In pandas, this kind of table is commonly stored in an object called a DataFrame.
Later, we will use pandas to select columns, calculate moving averages, compare values, create indicator columns, and inspect missing data.
import pandas as pdpd is a widely used short name for pandas.
matplotlib — Turn Numbers into Charts
matplotlib is a plotting package. We will use it to turn numbers into charts.
Later, it will help us draw price lines, moving averages, indicator curves, and entry or exit markers.
In this article, we only check that matplotlib is installed. We will draw real charts later.
4. How the Three Packages Work Together
Market
↓
FinanceDataReader
↓
pandas DataFrame
↓
Calculations and indicators
↓
matplotlib
↓
ChartFinanceDataReader brings the data in. pandas organizes and calculates it. matplotlib helps you see the result.
5. Open the VS Code Terminal
Open your alphesta-lab folder in Visual Studio Code.
Select Terminal → New Terminal.
Check Python:
python --versionYou should see:
Python 3.x.x6. Install the Three Packages
python -m pip install finance-datareader pandas matplotlibPress Enter. Python may also install smaller supporting packages called dependencies.
You may see:
Successfully installed ...or:
Requirement already satisfied: ...7. What Does import Mean?
Installing a package and using a package are two different steps.
Installation puts the package into your Python environment. Import makes it available inside the Python file you are currently running.
Install once
→ package exists in the environment
Import in a file
→ that file can use the package8. Create check_packages.py
import FinanceDataReader as fdr
import pandas as pd
import matplotlib
print("FinanceDataReader:", fdr.__version__)
print("pandas:", pd.__version__)
print("matplotlib:", matplotlib.__version__)
print("Market analysis tools are ready.")The first three lines load the packages. The next three print their installed versions. The last line is our success message.
9. Run the Check
FinanceDataReader: 0.9.x
pandas: x.x.x
matplotlib: x.x.x
Market analysis tools are ready.Your version numbers may differ. The important point is that all three imports work and the final line appears.
Check Your Result
- You can explain what a Python package is.
- You know that
pipinstalls packages. - You know what FinanceDataReader, pandas, and matplotlib each do.
check_packages.pyruns without an import error.Market analysis tools are ready.appears at the end.
One Common Problem: ModuleNotFoundError
ModuleNotFoundError: No module named 'pandas'This means the Python interpreter running your file cannot find pandas.
Check the interpreter selected in VS Code, then run:
python -m pip install finance-datareader pandas matplotlibIf python itself is not recognized on Windows, try:
py -m pip install finance-datareader pandas matplotlibWhat You Just Added
alphesta-lab/
├── hello_alphesta.py
└── check_packages.pyMore importantly, you now understand the first Alphesta tool chain:
Get data
→ Organize data
→ Calculate
→ VisualizeIn the next step, you will add Codex to Visual Studio Code. We will introduce it the same way: what it is, why we need it, what it can do, and what it should not do for you yet.