Section 1.1: Prerequisites for Building the System

To follow this tutorial, you should have:

• Python 3.x installed on your device
• A fundamental understanding of Python programming
• Familiarity with object-oriented programming principles
• Basic knowledge of financial markets and investment strategies

Let’s kick things off by preparing our development environment and installing the necessary libraries.

Section 1.2: Setting Up Your Development Environment

Before diving into coding, we need to establish our development environment. We will create a new Python virtual environment and install the required libraries.

Begin by opening your terminal or command prompt and navigating to your project directory. Create a new virtual environment using the following command:

python -m venv recommendation-system

Activate the virtual environment with the relevant command for your operating system:

• For Windows:

recommendation-systemScriptsactivate

• For macOS/Linux:

source recommendation-system/bin/activate

Next, install the necessary libraries. Use the following command to install the yfinance library:

pip install yfinance

We’ll also need the numpy library for data manipulation and analysis. Install it with:

pip install numpy

Now that our environment is ready, we can start building our personalized investment recommendation system.

Section 1.3: Accessing Financial Data

To offer personalized investment advice, our recommendation system requires access to historical financial data. We will utilize the yfinance library to obtain data for real assets, such as stocks, bonds, or cryptocurrencies.

Let’s import the necessary libraries and define a function to download financial data for a specific asset.

import yfinance as yf

import numpy as np

def download_data(ticker, start_date, end_date):

asset = yf.Ticker(ticker)

data = asset.history(start=start_date, end=end_date)

return data

In this code, we import the yfinance library and numpy for data manipulation. The download_data function accepts three parameters: ticker, start_date, and end_date. It employs the yf.Ticker class to fetch historical data for the specified asset within the given date range.

Let’s test our function by downloading data for a specific stock:

data = download_data("AAPL", "2010-01-01", "2023-10-31")

print(data.head())

In the code above, we call download_data with the ticker symbol "AAPL" (Apple Inc.) and the date range from January 1, 2010, to October 31, 2023. We then print the first few rows of the downloaded data to ensure it functions correctly.

Run the script, and you should see the historical stock data for Apple Inc. displayed in the console.

Section 1.4: Exploratory Data Analysis

Having retrieved the financial data, it’s time to conduct exploratory data analysis to gain insights into the dataset. We’ll visualize the data using various plots to identify trends and patterns.

Line Plot Visualization

Line plots are a straightforward and effective way to visualize financial data. They illustrate changes in a variable over time, making them perfect for visualizing stock prices.

Let’s create a line plot to visualize the closing prices of the downloaded stock data:

import matplotlib.pyplot as plt

def plot_line(data, title, ylabel):

plt.figure(figsize=(12, 6))

plt.plot(data.index, data["Close"])

plt.title(title)

plt.xlabel("Date")

plt.ylabel(ylabel)

plt.grid(True)

plt.show()

plot_line(data, "Apple Inc. Stock Prices", "Closing Price")

In this code, we import the matplotlib.pyplot library and define a plot_line function. This function takes financial data, a title, and a y-axis label. It generates a line plot using plt.plot and customizes its appearance.

Run the script, and you should see a line plot displaying the closing prices of Apple Inc. stock.

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Candlestick Chart Visualization

Candlestick charts offer a detailed perspective on stock prices by displaying the opening, closing, high, and low prices for each time frame. To create a candlestick chart, we’ll use the mplfinance library. Install it with:

pip install mplfinance

Once installed, add the following code to your script:

import mplfinance as mpf

def plot_candlestick(data, title):

mpf.plot(data, type="candle", title=title, ylabel="Price")

plot_candlestick(data, "Apple Inc. Candlestick Chart")

This code defines a plot_candlestick function that creates a candlestick chart using the mpf.plot function.

Run the script, and you should see a candlestick chart of Apple Inc.'s stock prices.

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Section 2.1: Creating the User Class

To represent users in our recommendation system, we will develop a User class that stores user preferences, such as risk tolerance and investment horizon.

Add the following code to your script:

class User:

def __init__(self, name, risk_tolerance, investment_horizon):

self.name = name

self.risk_tolerance = risk_tolerance

self.investment_horizon = investment_horizon

This code defines a User class with an __init__ method that initializes the user's name, risk tolerance, and investment horizon.

Section 2.2: Implementing the Recommendation System Class

Next, we’ll create a RecommendationSystem class that will manage the recommendation logic. This class will take the user’s preferences and financial data as input and provide tailored investment advice.

Add the following code to your script:

class RecommendationSystem:

def __init__(self, user, data):

self.user = user

self.data = data

def get_recommendation(self):

# Recommendation logic goes here

pass

In this code, we define a RecommendationSystem class with an __init__ method and a get_recommendation method. The __init__ method takes a user object and financial data as input, initializing the relevant attributes.

Section 2.3: Implementing the Recommendation Logic

To provide tailored investment advice, our recommendation system must analyze financial data while considering user preferences. We’ll implement the recommendation logic in the get_recommendation method.

Add the following code to your script:

class RecommendationSystem:

# ...

def get_recommendation(self):

filtered_assets = self.data[self.data["Close"].notna()]

returns = filtered_assets["Close"].pct_change()

average_returns = returns.mean()

filtered_assets = pd.DataFrame(average_returns[average_returns <= self.user.risk_tolerance])

sorted_assets = filtered_assets.sort_values(by=0, ascending=False)

top_assets = sorted_assets.head(self.user.investment_horizon)

return top_assets.index.tolist()

In this code, we compute daily returns for each asset using the pct_change method, calculate the average return, and filter based on the user’s risk tolerance. We then sort the assets by average return in descending order and select the top assets based on the user’s investment horizon.

Section 2.4: Testing the Recommendation System

With the recommendation logic in place, let’s test our recommendation system. Add the following code to your script:

user = User("John Doe", 0.02, 5)

recommendation_system = RecommendationSystem(user, data)

recommendation = recommendation_system.get_recommendation()

print(f"Recommended assets for {user.name}: {recommendation}")

In this code, we create a User object named "John Doe" with a risk tolerance of 0.02 and an investment horizon of 5. We then create a RecommendationSystem object, call the get_recommendation method, and print the personalized investment recommendation.

Run the script, and you should see the recommended assets for "John Doe" displayed in the console.