# To fetch data for inflation rate in Indonesia and the world
import wbgapi as wb

# Importing pandas for data manipulation and making data structure (data frame)
import pandas as pd

# try:
#     inflation_data = wb.data.DataFrame('FP.CPI.TOTL.ZG', ['IDN', 'WLD'], time=range(1960, 2024))
#     inflation_data = inflation_data.reset_index()
#     inflation_data.to_csv('data/inflation_data.csv', index=False)
# except:
#     print("API call failed")

# Try fetch data with API
try:
    # Use API fetched data from 1960 to 2023
    inflation_data = wb.data.DataFrame('FP.CPI.TOTL.ZG', ['IDN', 'WLD'], time=range(1960, 2024))
    print("API call successfully")    
except:
    # Use locally stored data if API fails
    print("API call failed, attempting to use locally stored data")
    try:
        inflation_data = pd.read_csv("data/inflation_data.csv")
        print("Locally stored data loaded successfully")
    except FileNotFoundError:
        print("Locally stored data not found")

# inflation_data.info()
display(inflation_data)

API call successfully

# Process the column headers to remove the 'YR' prefix and convert them to integers
inflation_data.columns = inflation_data.columns.str.replace(r'^YR', '', regex=True)
inflation_data.columns.astype(int)
display(inflation_data)

# Clean the monthly to annual close price currency of investing.com data
def clean_investing_data(file_path):
    # Read the CSV file
    try:
        df = pd.read_csv(file_path)
    except FileNotFoundError:
        print(f"File from {file_path} not found.")
        return None

    # Remove unneccessary columns
    df.drop(["Open", "High", "Low", "Vol.", "Change %"], axis="columns", inplace=True)
    
    # Convert date string to datetime
    df['Date'] = pd.to_datetime(df['Date'])

    # Filter rows where the month is December to get the close price
    df = df[df['Date'].dt.month == 12]

    # Transform 'Date' to 'Year' column
    df = df.rename(columns={'Date': 'Year'})
    df['Year'] = df['Year'].dt.year
    
    # Convert string to float on 'Price' column
    if df['Price'].dtype == 'object':
        df['Price'] = df['Price'].str.replace(',', '', regex=False).astype(float)

    # Sort the time in ascending order
    df = df.iloc[::-1].reset_index(drop=True)
    return df

# Example usage for USD/IDR
clean_investing_data("data/USD_IDR Historical Data.csv").head(4)

# Fetch data for currency data from Yahoo Finance
import yfinance as yf

# Download historical exchange rate data for USD/IDR
test = yf.download('USDIDR=X', start=None, end='2023-12-31', interval='1mo')

# Reset index to get date column
test.reset_index(inplace=True)

# Display the raw data from API
display(test.head(4))

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# Function to return cleaned dataframe of the currency
def clean_yahoo_data(currency_code):
    # Concatenate the input to form the ticker
    ticker = f"{currency_code}IDR=X"
    
    # Get historical price from Yahoo Finance API
    try:
        data = yf.download(
            ticker, 
            start=None, 
            end='2023-12-31', 
            interval='1mo', 
            auto_adjust=True
        )

        # Handling empty data
        if data.empty:
            print(f"Data is empty for currency code: {currency_code}")
            return None
    except Exception as e:
        print(f"An error occurred on {currency_code}: {e}")
        return None
    
    # Reset index to make Date a column
    data.reset_index(inplace=True)

    # Ensure the date is in datetime format
    data['Date'] = pd.to_datetime(data['Date'])

    # Group by year and get the last row of each year (December)
    yearly_data = data.groupby(data['Date'].dt.year)['Close'].last().reset_index()

    # Look for anomalies
    yearly_data['% Change'] = yearly_data['Close'].pct_change()

    # Value for identifying anomalies 
    threshold = 0.9

    # Extract the rows with anomaly
    anomalies = yearly_data[abs(yearly_data['% Change']) > threshold]
    
    # If have anomalies
    if not anomalies.empty:
        print(f"\nAnomalies Detected in {currency_code}:\n\n{anomalies}")
        # Drop rows with anomalies
        yearly_data = yearly_data.drop(index=anomalies.index - 1)

    # Create a new DataFrame with just the columns we want
    yearly_data = yearly_data[['Date', 'Close']].copy()
    
    # Rename columns
    yearly_data.columns = ['Year', 'Price']
    

    return yearly_data.reset_index(drop=True)

# Example usage for EUR/IDR
test = clean_yahoo_data("EUR")
display(test.tail(8))

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Anomalies Detected in EUR:

Price   Date         Close    % Change
Ticker            EURIDR=X            
13      2016    144.508667   -0.990252
14      2017  16033.632812  109.952742

# Combine both dataframe
def combine_df(superset_df, subset_df):
    """
    superset_df is the df with full time period
    subset_df is the df with more accurate data but is a subset of superset_df
    """

    # Filter out rows in superset_df that overlap with subset_df
    unique_df = superset_df[~superset_df["Year"].isin(subset_df["Year"])]

    # Concatenate unique_df and subset_df
    combined_df = pd.concat([unique_df, subset_df], ignore_index=True)

    # Sort by year
    combined_df.sort_values(by="Year", inplace=True, ignore_index=True)
    
    return combined_df

# Example usage
investing_data = clean_investing_data("data/USD_IDR Historical Data.csv")
yahoo_data = clean_yahoo_data("USD")
display(combine_df(investing_data, yahoo_data).tail(10))

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# Investing.com cleaned data
investing_data = {
    "USD/IDR": clean_investing_data("data/USD_IDR Historical Data.csv"),
    "JPY/IDR": clean_investing_data("data/JPY_IDR Historical Data.csv"),
    "EUR/IDR": clean_investing_data("data/EUR_IDR Historical Data.csv"),
    "CAD/IDR": clean_investing_data("data/CAD_IDR Historical Data.csv"),
    "GBP/IDR": clean_investing_data("data/GBP_IDR Historical Data.csv")
}

# Yahoo Finance cleaned data
yahoo_data = {
    "USD/IDR": clean_yahoo_data("USD"),
    "JPY/IDR": clean_yahoo_data("JPY"),
    "EUR/IDR": clean_yahoo_data("EUR"),
    "CAD/IDR": clean_yahoo_data("CAD"),
    "GBP/IDR": clean_yahoo_data("GBP")
}

# Investing.com and Yahoo Finance data combined
combined_forex_data = {
    "USD/IDR": combine_df(investing_data["USD/IDR"], yahoo_data["USD/IDR"]),
    "JPY/IDR": combine_df(investing_data["JPY/IDR"], yahoo_data["JPY/IDR"]),
    "EUR/IDR": combine_df(investing_data["EUR/IDR"], yahoo_data["EUR/IDR"]),
    "CAD/IDR": combine_df(investing_data["CAD/IDR"], yahoo_data["CAD/IDR"]),
    "GBP/IDR": combine_df(investing_data["GBP/IDR"], yahoo_data["GBP/IDR"])
}

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Anomalies Detected in EUR:

Price   Date         Close    % Change
Ticker            EURIDR=X            
13      2016    144.508667   -0.990252
14      2017  16033.632812  109.952742

# Visualize Indonesia's Annual Inflation
import matplotlib.pyplot as plt

# Get the inflation data for Indonesia and the world
indonesia_inflation = inflation_data.iloc[0]
world_inflation = inflation_data.iloc[1]

# Visualize Indonesia's Annual Inflation from 1960-2023
plt.figure(figsize=(14, 6))

# Add world data
plt.plot(inflation_data.columns, indonesia_inflation, marker='o', linestyle='-', label='IDN')
plt.plot(inflation_data.columns, world_inflation, marker='o', linestyle='-', label='WLD')
plt.axvspan(inflation_data.columns[0], '1981', color='gray', alpha=0.2, label="Data not available for World Inflation")

# Add Indonesia inflation mean horizontal line
indonesia_inflation_mean = indonesia_inflation.mean()
print(f'Indonesia Mean Inflation: {round(indonesia_inflation_mean, 2)}%')
plt.axhline(y=indonesia_inflation_mean, color='red', linestyle='--', label='Indonesia Mean Inflation')

# Set y axis to log scale to see the data in more detail
plt.yscale('log')
plt.title("Indonesia's Inflation, Consumer Price (annual %)")
plt.xlabel('Year')
# Set x axis tick labels to show every 3 years
plt.xticks(ticks=inflation_data.columns[::3])
plt.ylabel('Inflation (%)')
plt.grid(True)
plt.tight_layout()
plt.legend()
plt.show()

Indonesia Mean Inflation: 41.99%

# Import BeautifulSoup for web scraping
from bs4 import BeautifulSoup

# Import requests for making HTTP requests
import requests

# tdqm for progress bar
from tqdm import tqdm

# Function to scrape the top result for events for a specific year in a specific region from Google search
def get_economic_events(year, region):
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
    }
    
    # Replace the google search query with year and region (e.g. Indonesia or world)
    url = f"https://www.google.com/search?q=what+happpened+in+{year}+economy+in+{region}"

    try:
        # Send the HTTP request
        response = requests.get(url, headers=headers)
        
        if response.status_code == 200:
            # Parse the HTML content
            html = BeautifulSoup(response.content, 'html.parser')

            # Find the first article block
            result = html.find('div', class_='N54PNb')

            # Extract title and description with handling for missing values
            # Extract the title
            if result and result.find("h3"):
                title = result.find("h3").text
            else:
                title = "Not available"

            # Extract the description
            if result and result.find("div", class_="VwiC3b"):
                description = result.find("div", class_="VwiC3b").text
            else:
                description = "Not available"

            # Store data in a dictionary
            search_results = {
                "title": title,
                "description": description
            }
            
            return search_results
        else:
            # Handle error status codes
            print(f"Failed to retrieve the page. Status code: {response.status_code}")
            return None
    except Exception as e:
        # Handle any exception
        print(f"An error occurred: {e}")
        return None

# List of years and regions to scrape
anomaly_years = ["1966", "1974", "1998", "2008", "2022"]
regions = ["Indonesia", "world"]

# Store results in a list of dictionaries
events_data = []

# Scrape data for all anomaly years
for year in tqdm(anomaly_years, desc="Scraping data", unit="year"):
    # Scrape data for each region on the same year
    for region in regions:
        try:
            event = get_economic_events(year, region)
            if event:
                # Add year and region to the dictionary and append to the list
                event['year'] = year
                event['region'] = region
                events_data.append(event)
        except Exception as e:
            print(f"Error retrieving data for {year} in {region}: {e}")

# Convert list of dictionaries into Pandas DataFrame
economic_event_articles = pd.DataFrame(events_data)

# Reorder columns for readability
column_order = ["year", "region", "title", "description"]
economic_event_articles = economic_event_articles.reindex(columns=column_order)

# Adjust pandas settings to display the column max width and avoid truncation
pd.set_option('display.max_colwidth', None)

# Display the DataFrame
display(economic_event_articles)

Scraping data: 100%|██████████| 5/5 [00:05<00:00,  1.07s/year]

# Trim inflation to 1999-2023
inflation_data_1999_2023 = inflation_data.loc[:, '1999':]

# x axis (Year) and y axis (Inflation %)
year_1999_2023 = inflation_data_1999_2023.columns
indonesia_inflation_1999_2023 = inflation_data_1999_2023.iloc[0]
world_inflation_1999_2023 = inflation_data_1999_2023.iloc[1]

# Calculate standard deviation and mean
std_dev = indonesia_inflation_1999_2023.std()
indonesia_mean_inflation = indonesia_inflation_1999_2023.mean()
world_mean_inflation = world_inflation_1999_2023.mean()

# Visualize Indonesia Annual Inflation from 1999-2023
plt.figure(figsize=(14, 6))
plt.plot(year_1999_2023, indonesia_inflation_1999_2023, marker='o', linestyle='-', label='Indonesia Inflation %')
plt.plot(year_1999_2023, world_inflation_1999_2023, marker='o', linestyle='-', label='World Inflation %')

# Plot indonesia standard deviations
plt.axhline(y=indonesia_mean_inflation - std_dev, color='magenta', linestyle='--', label="-1 Indonesia Standard Deviation")
plt.axhline(y=indonesia_mean_inflation + std_dev, color='purple', linestyle='--', label="+1 Indonesia Standard Deviation")
plt.axhline(y=indonesia_mean_inflation + (2 * std_dev), color='green', linestyle='--', label="+2 Indonesia Standard Deviation")
plt.axhline(y=indonesia_mean_inflation + (3 * std_dev), color='blue', linestyle='--', label="+3 Indonesia Standard Deviation")

# Mean inflation of Indonesia and World from 1999-2023
plt.axhline(y=indonesia_mean_inflation, color='red', linestyle='solid', label='Indonesia Mean Inflation')

plt.title("Indonesia Inflation, Consumer Price (annual %)")
plt.xlabel('Year')
plt.xticks(rotation=45)
plt.ylabel('Inflation (%)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# Print summary statistics
print(f"Indonesia Standard Deviation Inflation: {round(std_dev, 2)}")
print(f"Indonesia Mean Inflation: {round(indonesia_mean_inflation, 2)}%")
print(f"Indonesia Min Inflation: {round(indonesia_inflation_1999_2023.min(), 2)}%")
print(f"Indonesia Max Inflation: {round(indonesia_inflation_1999_2023.max(), 2)}%")
print(f"World Mean Inflation: {round(world_mean_inflation, 2)}%")
print(f"World Min Inflation: {round(world_inflation_1999_2023.min(), 2)}%")
print(f"World Max Inflation: {round(world_inflation_1999_2023.max(), 2)}%")

Indonesia Standard Deviation Inflation: 4.29
Indonesia Mean Inflation: 6.55%
Indonesia Min Inflation: 1.56%
Indonesia Max Inflation: 20.48%
World Mean Inflation: 3.62%
World Min Inflation: 1.44%
World Max Inflation: 8.95%

# Plot the new time scope (2000 - 2023)
# Trim inflation data to 2000-2023
inflation_data_2000_2023 = inflation_data.loc[:, '2000':]

# x axis (Year) and y axis (Inflation %)
year_2000_2023 = inflation_data_2000_2023.columns
indonesia_inflation_2000_2023 = inflation_data_2000_2023.iloc[0]
world_inflation_2000_2023 = inflation_data_2000_2023.iloc[1]

# Calculate standard deviation and mean
std_dev = inflation_data_2000_2023.std(axis=1).values[0]
indonesia_mean_inflation = indonesia_inflation_2000_2023.mean()
world_mean_inflation = world_inflation_2000_2023.mean()

# Visualize Indonesia's Annual Inflation from 2000-2023
plt.figure(figsize=(14, 6))
plt.plot(year_2000_2023, indonesia_inflation_2000_2023, marker='o', linestyle='-', label='Indonesia Inflation %')
plt.plot(year_2000_2023, world_inflation_2000_2023, marker='o', linestyle='-', label='World Inflation %')

# Mean inflation of Indonesia and World from 2000-2023
plt.axhline(y=indonesia_mean_inflation, color='red', linestyle='--', label='Indonesia Mean Inflation')
plt.axhline(y=world_mean_inflation, color='purple', linestyle='--', label='World Mean Inflation')

plt.title("Indonesia's Inflation, Consumer Price (annual %)")
plt.xlabel('Year')
plt.xticks(year_2000_2023)
plt.ylabel('Inflation (%)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

print(f"Indonesia Mean Inflation: {round(indonesia_mean_inflation, 2)}%")
print(f"World Mean Inflation: {round(world_mean_inflation, 2)}%")

Indonesia Mean Inflation: 5.97%
World Mean Inflation: 3.65%

# Function to clean, format, add yearly and cumulative percentage change to forex historical data
def process_forex_data(currency_pair):
    # Make a copy of the combined forex data (to avoid any future conflicts)
    df = combined_forex_data[currency_pair].copy()

    # Filter data to ensure that it is still within the time period of this project
    start_date = 1999
    end_date = 2023
    df = df[(df['Year'] >= start_date) & (df['Year'] <= end_date)]

    # Get the close price of 1999 as the initial value
    price_1999 = df['Price'].iloc[0]

    # Add a new column for Percentage Change since the initial value and store the value
    df['Cumulative Percentage Change'] = (df['Price'] / price_1999 - 1) * 100
    
    # Add a new column for Yearly Percentage Change and store the value
    df['Yearly Percentage Change'] = df['Price'].pct_change() * 100

    # Delete the first row, since it is 1999 which was only used for calculating yearly percentage change column.
    df = df.drop(index=df.index[:1])
    df.reset_index(drop=True, inplace=True)

    return df

# USD, EUR, GBP, JPY, and CAD 2000-2023
historical_data = {
    "USD/IDR": process_forex_data("USD/IDR"),
    "JPY/IDR": process_forex_data("JPY/IDR"),
    "EUR/IDR": process_forex_data("EUR/IDR"),
    "CAD/IDR": process_forex_data("CAD/IDR"),
    "GBP/IDR": process_forex_data("GBP/IDR")
}

# Example output for EURIDR
display(historical_data["EUR/IDR"].head(4))

# Cumulative Percentage Change from 2000-2023 in Pandas DataFrame
cumulative_changes_df = pd.DataFrame({
    "Year": historical_data["USD/IDR"]["Year"],
    "USD/IDR": historical_data["USD/IDR"]["Cumulative Percentage Change"],
    "JPY/IDR": historical_data["JPY/IDR"]["Cumulative Percentage Change"],
    "EUR/IDR": historical_data["EUR/IDR"]["Cumulative Percentage Change"],
    "CAD/IDR": historical_data["CAD/IDR"]["Cumulative Percentage Change"],
    "GBP/IDR": historical_data["GBP/IDR"]["Cumulative Percentage Change"]
})

display(cumulative_changes_df.head(4))

# Visualize Indonesia's Annual Inflation from 1960-2023
plt.figure(figsize=(14, 6))
plt.plot(cumulative_changes_df["Year"], cumulative_changes_df["USD/IDR"], marker='o', linestyle='-', label='USD/IDR')
plt.plot(cumulative_changes_df["Year"], cumulative_changes_df["JPY/IDR"], marker='o', linestyle='-', label='JPY/IDR')
plt.plot(cumulative_changes_df["Year"], cumulative_changes_df["EUR/IDR"], marker='o', linestyle='-', label='EUR/IDR')
plt.plot(cumulative_changes_df["Year"], cumulative_changes_df["CAD/IDR"], marker='o', linestyle='-', label='CAD/IDR')
plt.plot(cumulative_changes_df["Year"], cumulative_changes_df["GBP/IDR"], marker='o', linestyle='-', label='GBP/IDR')

plt.title("Key Global Currencies towards Indonesia's Rupiah (IDR) Cumulative Percentage change from 2000-2023")
plt.xlabel('Year')

plt.xticks(cumulative_changes_df["Year"])

plt.ylabel('Cumulative % Change')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

print("Cumulative Percentage Change from 2000-2023: ")
print(f"USD/IDR : {round(cumulative_changes_df['USD/IDR'].iloc[-1], 2)}%")
print(f"JPY/IDR : {round(cumulative_changes_df['JPY/IDR'].iloc[-1], 2)}%")
print(f"EUR/IDR : {round(cumulative_changes_df['EUR/IDR'].iloc[-1], 2)}%")
print(f"CAD/IDR : {round(cumulative_changes_df['CAD/IDR'].iloc[-1], 2)}%")
print(f"GBP/IDR : {round(cumulative_changes_df['GBP/IDR'].iloc[-1], 2)}%")

Cumulative Percentage Change from 2000-2023: 
USD/IDR : 120.15%
JPY/IDR : 59.62%
EUR/IDR : 139.96%
CAD/IDR : 141.26%
GBP/IDR : 73.71%

# Yearly Percentage Change from 2000-2023 in Pandas DataFrame
yearly_forex_data = pd.DataFrame({
    "Year": historical_data["USD/IDR"]["Year"],
    "USD/IDR": historical_data["USD/IDR"]["Yearly Percentage Change"],
    "JPY/IDR": historical_data["JPY/IDR"]["Yearly Percentage Change"],
    "EUR/IDR": historical_data["EUR/IDR"]["Yearly Percentage Change"],
    "CAD/IDR": historical_data["CAD/IDR"]["Yearly Percentage Change"],
    "GBP/IDR": historical_data["GBP/IDR"]["Yearly Percentage Change"]
}).set_index("Year")

display(yearly_forex_data.head(4))

# Plot the grouped bar chart
# Documentation reference: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html
yearly_forex_data.plot(kind='bar', width=0.8, figsize=(14, 6))

# Alternating background colors for clarity and readability
for i in range(len(yearly_forex_data)):
    if i % 2 == 0:
        plt.axvspan(i - 0.5, i + 0.5, color='lightgray', alpha=0.5, zorder=0)

# Title and labels
plt.title("Key Global Currencies towards Indonesia's Rupiah (IDR) Yearly Percentage Change from 2000-2023")
plt.xlabel('Year')
plt.ylabel('% Change')
plt.xticks(rotation=0)  

# Add legend and layout
plt.legend()
plt.tight_layout()
plt.show()

# Plot the heatmap
import seaborn as sns

# Plot the heatmap
plt.figure(figsize=(20, 6))
# Transpose and plot
sns.heatmap(yearly_forex_data.T, annot=True, fmt=".1f", cmap='coolwarm', linewidths=0.5)
plt.title('Yearly Percentage Change Heatmap (2000-2023)')
plt.xlabel('Year')
plt.ylabel('Currency Pair')
plt.show()

# Box plot for yearly percentage change
plt.figure(figsize=(10, 6))

# Using the dataframe to plot the box plot
sns.boxplot(data=yearly_forex_data, palette=['blue', 'green', 'red', 'purple', 'orange'])

plt.title("Yearly Percentage Change (2000-2023)", fontsize=14)
plt.xlabel("Currency Pair", fontsize=12)
plt.ylabel("Yearly Percentage Change (%)", fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

# Iterate over each column and calculate the median
print("Median yearly percentage change from 2000-2023:")
for column in yearly_forex_data.columns:
    median_value = yearly_forex_data[column].median()
    print(f"{column}: {median_value:.2f}%")

Median yearly percentage change from 2000-2023:
USD/IDR: 2.47%
JPY/IDR: 2.54%
EUR/IDR: 1.51%
CAD/IDR: 1.53%
GBP/IDR: 2.79%

# Violin plot for yearly percentage change
plt.figure(figsize=(10, 6))

# Using the dataframe to plot the violin plot
sns.violinplot(data=yearly_forex_data)
plt.title("Distribution of Yearly Percentage Changes")
plt.xlabel("Currency")
plt.ylabel("Depreciation Rate (%)")
plt.grid(True)
plt.show()

# Calculate the correlation matrix for the yearly forex data
correlation = yearly_forex_data.corr()

# Generate a heatmap of the correlation matrix
plt.figure(figsize=(8, 6))
sns.heatmap(correlation, annot=True, cmap='coolwarm')

plt.title('Correlation Matrix of Currency Depreciation')
plt.show()

print(f"Indonesia Average Yearly Inflation: {round(indonesia_mean_inflation, 2)}%")

Indonesia Average Yearly Inflation: 5.97%

print(f"Indonesia Yearly Inflation: \n{round(indonesia_inflation_1999_2023.head(6), 2)}%")

Indonesia Yearly Inflation: 
1999    20.48
2000     3.69
2001    11.50
2002    11.90
2003     6.76
2004     6.06
Name: IDN, dtype: float64%

# Preview yearly_forex_data dataframe
display(yearly_forex_data.head(4))

# Make a copy of the dataframe to prevent future conflicts with other analysis
yearly_forex_data_with_avg = yearly_forex_data.copy()

# Average value of the row (all currencies on the particular year)
yearly_forex_data_with_avg['Average'] = yearly_forex_data_with_avg.mean(axis=1)
display(yearly_forex_data_with_avg.head(4))

# Plot a scatter plot for top five currencies depreciation vs inflation on the corresponding year
plt.figure(figsize=(10, 6))
for currency_pair in historical_data.keys():
    plt.scatter(
        indonesia_inflation_1999_2023.iloc[1:], 
        yearly_forex_data[currency_pair], 
        label=f"{currency_pair} Depreciation vs Inflation"
    )

# Set up and show the plot
plt.xlabel('Indonesia Inflation Rate (%)')
plt.ylabel('IDR Depreciation rate (%)')
plt.title('Indonesia Annual Inflation vs Average IDR Depreciation 2000 - 2023')
plt.grid(True, linestyle='--')
plt.legend()
plt.show()

# Calculate each currency pair average and store it in the dictionary
yearly_mean = {}
for i in yearly_forex_data.columns:
    yearly_mean[i] = yearly_forex_data[i].mean()

# Calculate the overall mean
overall_yearly_mean = sum(yearly_mean.values()) / len(yearly_mean)

# Plot the horizontal bar chart
plt.figure(figsize=(10, 6))

# Create a horizontal bar chart for mean percentage change
mean_percentage_change_chart = plt.barh(
    yearly_forex_data.columns, 
    yearly_mean.values(), 
    color=['blue', 'green', 'red', 'purple', 'orange'], 
    zorder=2
)

# Display the labels of percentage values on each bar
for bar in mean_percentage_change_chart:
    plt.text(bar.get_width() - (bar.get_width() * 0.05), 
            bar.get_y() + bar.get_height()/2, 
            f'{bar.get_width():.2f}%', 
            va='center', 
            ha='right', 
            color='white', 
            fontweight='bold')

# Average value line
plt.axvline(
    overall_yearly_mean,
    color='black',
    linestyle='--',
    linewidth=1.5,
    label=f'Overall Average: {round(overall_yearly_mean, 2)}%'
)

# Set up and show the plot
plt.grid(axis='x')
plt.xlabel('% Change')
plt.ylabel('Currency Pair')
plt.title('Average Yearly Percentage Change (2020-2023)')
plt.legend()
plt.tight_layout()
plt.show()

print(f"IDR Average Depreciation Rate: {round(overall_yearly_mean, 2)}%")

IDR Average Depreciation Rate: 3.65%

# Create a new dataframe to calculate the CAGR for each currency
forex_cagr_data = pd.DataFrame({
    "Currency Pair": historical_data.keys(),
})

# Number of years
year_count = 2023 - 2000

# Get the initial values
initial_values = []
for key in historical_data.keys():
    price = historical_data[key]['Price'][0]
    percentage_change = historical_data[key]["Yearly Percentage Change"][0]
    initial_value = price / (1 + percentage_change / 100)
    initial_values.append(initial_value)

# Get the final values
final_values = []
for key in historical_data.keys():
    price_2023 = historical_data[key].loc[historical_data[key]['Year'] == 2023, 'Price'].values[0]
    final_values.append(price_2023)

# Store the values in the dataframe
forex_cagr_data['Initial Value'] = initial_values
forex_cagr_data['Final Value'] = final_values

# Calculate the CAGR for every currency
CAGR_values = (((forex_cagr_data['Final Value'] / forex_cagr_data['Initial Value']) ** (1 / year_count)) - 1) * 100

# Store the values in the dataframe
forex_cagr_data['CAGR'] = CAGR_values

# Preview dataframe
display(forex_cagr_data)

# Plot the horizontal bar chart
plt.figure(figsize=(10, 6))


# Create a horizontal bar chart for mean percentage change
forex_CAGR_chart = plt.barh(
    forex_cagr_data['Currency Pair'],
    forex_cagr_data['CAGR'],
    color=['blue', 'green', 'red', 'purple', 'orange'], 
    zorder=2
)

# Display the labels of percentage values on each bar
for bar in forex_CAGR_chart:
    plt.text(
        bar.get_width() - (bar.get_width() * 0.05),
        bar.get_y() + bar.get_height() / 2,
        f'{bar.get_width():.2f}%',
        va='center',
        ha='right',
        color='white',
        fontweight='bold'
    )

overall_forex_cagr_mean = forex_cagr_data['CAGR'].mean()

# Average value line
plt.axvline(
    overall_forex_cagr_mean,
    color='black',
    linestyle='--',
    linewidth=1.5,
    label=f'Overall Average: {round(overall_forex_cagr_mean, 2)}%'
)

# Set up and show the plot
plt.grid(axis='x')
plt.xlabel('Compounding Annual Growth Rate (CAGR)')
plt.ylabel('Currency Pair')
plt.title('Average IDR Depreciation from 2020-2023')
plt.legend()
plt.tight_layout()
plt.show()

print(f"IDR Average Depreciation Rate with CAGR method: {round(overall_forex_cagr_mean, 2)}%")

IDR Average Depreciation Rate with CAGR method: 3.15%

idr_depreciaton_rate = (overall_forex_cagr_mean + overall_yearly_mean)/2
print(f"IDR Depreciation Rate: {round(idr_depreciaton_rate, 2)}%")

IDR Depreciation Rate: 3.4%

indonesia_global_inflation_rate = indonesia_mean_inflation + idr_depreciaton_rate
print(f"Indonesia Inflation Rate Relative to the Global Market: {round(indonesia_global_inflation_rate, 2)}%")

Indonesia Inflation Rate Relative to the Global Market: 9.37%

	YR1960	YR1961	YR1962	YR1963	YR1964	YR1965	YR1966	YR1967	YR1968	YR1969	...	YR2014	YR2015	YR2016	YR2017	YR2018	YR2019	YR2020	YR2021	YR2022	YR2023
economy
IDN	39.590444	13.691932	131.397849	145.910781	108.994709	306.76311	1136.254112	106.0	128.843042	15.47987	...	6.394925	6.363121	3.525805	3.808798	3.198346	3.030587	1.920968	1.560130	4.209464	3.670131
WLD	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	2.354491	1.443857	1.605539	2.254277	2.438737	2.216776	1.905664	3.475403	7.930929	5.635669

Source	Strengths	Weaknesses
macrotrends.net	Provides data for multiple regions	Lacks data accuracy (only up to four decimal points); datasets only provide one region at a time, requiring combination of Indonesia and world datasets
tradingeconomics.com	Offers a variety of indicators such as CPI housing utilities, CPI transportation, food inflation, etc.	Requires a subscription to access datasets; project scope is limited to historical data of inflation and currency exchange rate
Bank Indonesia	Provides monthly inflation data	Data is only available from 2002-2023, which is not suitable for this project

Source	Strengths	Weaknesses
Yahoo Finance	Provides currency exchange data	The earliest data point is from 2001 onwards, while this project requires data from 2000 onwards
fxtop.com	The selected (USD/IDR) dataset has data from 1967 to 2023	The dataset is not the close price, which is needed for the analysis of this project

	1960	1961	1962	1963	1964	1965	1966	1967	1968	1969	...	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
economy
IDN	39.590444	13.691932	131.397849	145.910781	108.994709	306.76311	1136.254112	106.0	128.843042	15.47987	...	6.394925	6.363121	3.525805	3.808798	3.198346	3.030587	1.920968	1.560130	4.209464	3.670131
WLD	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	2.354491	1.443857	1.605539	2.254277	2.438737	2.216776	1.905664	3.475403	7.930929	5.635669

	Year	Price
0	1988	1725.0
1	1990	1901.0
2	1991	1992.0
3	1992	2067.5

Price	Date	Adj Close	Close	High	Low	Open	Volume
Ticker		USDIDR=X	USDIDR=X	USDIDR=X	USDIDR=X	USDIDR=X	USDIDR=X
0	2001-07-01	9500.0	9500.0	11490.0	9265.0	11315.0	0
1	2001-08-01	8863.0	8863.0	9840.0	8325.0	9563.0	0
2	2001-09-01	9708.0	9708.0	9820.0	8685.0	9055.0	0
3	2001-10-01	10475.0	10475.0	10655.0	9625.0	10035.0	0

	Year	Price
11	2014	15085.000000
12	2017	16033.632812
13	2018	16652.000000
14	2019	15595.000000
15	2020	17013.494141
16	2021	16133.000000
17	2022	16695.000000
18	2023	16893.000000

	Year	Price
26	2015	13845.000000
27	2016	13518.000000
28	2017	13477.000000
29	2018	14553.000000
30	2019	13919.000000
31	2020	13832.559570
32	2021	14285.200195
33	2022	15502.200195
34	2023	15383.099609
35	2024	16053.600000

	year	region	title	description
0	1966	Indonesia	Indonesia: Economic Stabilization, 1966-69 in	1 Des 1970 — THE INDONESIAN economy suffered from rapid inflation during the first half of the 1960's, culminating in an increase of over 1,500 per cent in ...
1	1966	world	The 1966 Financial Crisis \| Levy Economics Institute	As a result of Fed intervention, the economy continued to expand, new financial practices emerged and were validated, leverage ratios increased, memories of the ...
2	1974	Indonesia	INDONESIA 1974: A MOMENTOUS YEAR Gary Hansen	oleh G Hansen · 1975 · Dirujuk 25 kali — tion of economic developments in Indonesia. Although somewhat over- shadowed by the January demonstrations, the world-wide rise in oil prices.
3	1974	world	1973–1975 recession	The 1973–1975 recession or 1970s recession was a period of economic stagnation in much of the Western world during the 1970s
4	1998	Indonesia	Asian Financial Crisis - Cause & Effect	In January 1998 the rupiah lost half of its value within the time-span of five days only, causing Indonesians to hoard food. This second IMF agreement contained ...
5	1998	world	Developments in the World Economy in 1997/98	The sharp declines in private capital flows to emerg- ing market economies in Asia, although cushioned to some extent by an increase in official financing flows ...
6	2008	Indonesia	Not available	Not available
7	2008	world	2007–2008 financial crisis	The 2007–2008 financial crisis, or the global financial crisis (GFC), was the most severe worldwide economic crisis since the 1929 Wall Street crash that began ...
8	2022	Indonesia	Indonesia's Economy Sees Rebound in 2022 following ...	Indonesia is projected to experience strong growth of 5.2 percent in 2022 thanks to the post-COVID-19 reopening of the economy and commodity price rises.
9	2022	world	World Economic Outlook, October 2022	11 Okt 2022 — Global economic activity is experiencing a broad-based and sharper-than-expected slowdown, with inflation higher than seen in several decades.

	Year	Price	Cumulative Percentage Change	Yearly Percentage Change
0	2000	9117.700000	29.514624	29.514624
1	2001	9268.500000	31.656700	1.653926
2	2002	9395.300000	33.457862	1.368075
3	2003	10594.099609	50.486507	12.759567

	Year	USD/IDR	JPY/IDR	EUR/IDR	CAD/IDR	GBP/IDR
0	2000	38.461538	23.840187	29.514624	33.574902	28.038739
1	2001	49.624329	15.600761	31.656700	35.187592	33.926502
2	2002	28.085868	10.273672	33.457862	17.766902	27.562906
3	2003	20.276205	14.650964	50.486507	34.178340	33.126962

	Currency Pair	Initial Value	Final Value	CAGR
0	USD/IDR	6987.50	15383.099609	3.490613
1	JPY/IDR	68.33	109.068024	2.053952
2	EUR/IDR	7039.90	16893.000000	3.879019
3	CAD/IDR	4832.30	11658.270508	3.903356
4	GBP/IDR	11306.50	19640.048828	2.429891

Analysing Indonesia's Inflation in the Context of Global Markets and Establishing a Benchmark Return for Indonesian Investors: An Exploratory Data Analysis

1. Introduction to the research space¶

1.1 Aims and Objectives¶

1.2 Acquire a Dataset¶

1.3 Utilise the Dataset¶

1.4 Writing Style to Communicate Ideas and Concepts¶

1.5 Summary of the Area of Research¶

2. Project background¶

2.1 Why the field is of interest¶

2.2 Previously explored topics¶

2.3 Scope of Work¶

2.4 Steps in Analytical Data Processing Pipeline.¶

2.5 Evaluation of Aims and Objectives Based on Chosen Approach¶

3. Ethics of Use of Data¶

3.1 Where the Data Has Come From¶

3.2 Considerations About Usage/Reusage of Data¶

3.3 Considerations Around Implications of Utilising Data for Purpose¶

3.4 Considerations of the Data Processing Pipeline¶

3.5 Considerations of Potential Biases of the Dataset¶

4. Data Relavance and the Use of Data¶

4.1 Origin of Data¶

4.2 The Appropriateness of the Data Source¶

4.3 Identifiable Case for Working with This Data¶

4.4 Format of Data¶

4.5 Other Datasets Consideration¶

5. Exploration of Datasets¶

5.1 Removing Illegal Values¶

5.2 Data Format for Analysis¶

5.3 Depth of Exploration¶

5.4 Checks for Out of Bound Values¶

5.5 Data Appropriateness for Further Analysis¶

6. Clear Rhetoric for Modifications to Data¶

6.1 Transform Forex Data¶

7. Data analysis¶

7.1 Find Average Yearly Inflation Rate from 2000-2023¶

7.2 Find IDR Depreciation Rate from 2000-2023¶

7.2.1 Find Average Yearly IDR Depreciation Rate from 2000-2023¶

7.2.2 Find IDR Depreciation Rate from 2000-2023 with CAGR Method¶

7.2.3 Calculate the Final IDR Depreciation Rate¶

7.3 Find Indonesia Inflation Rate Relative to the Global Market¶

8. Summary¶

References:¶