Neocolonialism: The power of a floating exchange rate market

economy
How dominant countries use the free-floating currency markets as strategic tool to exert economic control and perpetuate social inequalities worldwide.
Published
Aug 19, 2026
Keywords
neocolonialism
Summary

The chart shows the evolution of currencies in each country relative to the USD from the year 2001 to 2025.
Code
# Libraries
# ============================================
from sqlalchemy import create_engine, text
import pandas as pd
import plotly.express as px
import requests
import os

# Data Extraction
# ============================================
server = "DESKTOP-FUOV4IE\\MSSQLSERVER_TAB2"
database = "master"

connection_string = (
    f"mssql+pyodbc://@{server}/{database}"
    "?driver=ODBC+Driver+17+for+SQL+Server"
    "&trusted_connection=yes"
)

engine = create_engine(connection_string)

with engine.connect() as conn:
    # Query h_currencies
    result = conn.execute(text("SELECT right(symbol, 3) as symbol, date, rate FROM h_currencies_exh WHERE date > '2001-01-01'"))
    df = pd.DataFrame(result.fetchall(), columns=result.keys())

    # Query d_countries
    url = "https://raw.githubusercontent.com/guillemmaya92/Analytics/refs/heads/master/Data/DIM_Country.json"
    data = pd.read_json(url)
    df_countries = pd.DataFrame(data).T.reset_index().rename(columns={"index": "cod_iso3", "ISO2": "cod_iso2", "Cod_Currency": "cod_currency", "Country_Abr": "country"})
    df_countries['currency'] = df_countries['Currency'] + " (" + df_countries['Symbol'] + ")"
    df_countries = df_countries[['cod_currency', 'currency', 'cod_iso3', 'country']]
    df_countries = df_countries[df_countries['cod_iso3'] != 'ATA']

    # Adjust SQS (Somaliland)
    sos_lines = [
        {"cod_currency": "SQS", "currency": "Somaliland Shilling (S)", "cod_iso3": 'SOL', "country": "Somaliland"},
    ]
    df_countries = pd.concat([df_countries, pd.DataFrame(sos_lines)], ignore_index=True)

# Data Manipulation
# ============================================
# Format date
df['date'] = pd.to_datetime(df['date'])

# Adjust VES (Venezuela)
df['rate'] = df.apply(lambda row: row['rate'] / 100000 if row['symbol'] == 'VEF' else row['rate'], axis=1)
df['symbol'] = df['symbol'].apply(lambda x: 'VES' if x == 'VEF' else x)

# Adjust ZMW (Zambia)
df['rate'] = df.apply(lambda row: row['rate'] / 1000 if row['symbol'] == 'ZMK' else row['rate'], axis=1)
df['symbol'] = df['symbol'].apply(lambda x: 'ZMW' if x == 'ZMK' else x)

# Adjust USD (United States and others)
usa_lines = [
    {"symbol": "USD", "date": "2001-01-01", "rate": 1},
    {"symbol": "USD", "date": "2001-05-28", "rate": 1},
]
df = pd.concat([df, pd.DataFrame(usa_lines)], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])

# Adjust SSP (South Sudan)
ssp_lines = [
    {"symbol": "SSP", "date": "2008-01-01", "rate": 3.21},
    {"symbol": "SSP", "date": "2025-05-28", "rate": 130},
]
df = pd.concat([df, pd.DataFrame(ssp_lines)], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])

# Adjust SQS (Somaliland)
ssp_lines = [
    {"symbol": "SQS", "date": "2001-01-01", "rate": 2967},
    {"symbol": "SQS", "date": "2024-01-01", "rate": 9703},
]
df = pd.concat([df, pd.DataFrame(ssp_lines)], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])

# Adjust ZWL (Zimbawe)
zwl_lines = [
    {"symbol": "ZWL", "date": "2001-01-01", "rate": 1 / (10 ** 25)}
]
df = pd.concat([df, pd.DataFrame(zwl_lines)], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])

# Adjust CUP (Cuba)
zwl_lines = [
    {"symbol": "CUP", "date": "2001-01-01", "rate": 1}
]
df = pd.concat([df, pd.DataFrame(zwl_lines)], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])

# Adjust MRU (Mauritania)
mru_lines = [
    {"symbol": "MRU", "date": "2001-01-01", "rate": 23.8}
]
df = pd.concat([df, pd.DataFrame(mru_lines)], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])

# Adjust AOA (Angola)
mru_lines = [
    {"symbol": "AOA", "date": "2001-01-01", "rate": 18.7}
]
df = pd.concat([df, pd.DataFrame(mru_lines)], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])

# Adjust MGA (Madagascar)
mru_lines = [
    {"symbol": "MGA", "date": "2001-01-01", "rate": 1.27}
]
df = pd.concat([df, pd.DataFrame(mru_lines)], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])

# Get index of min and max date for each symbol
min_idx = df.groupby('symbol')['date'].idxmin()
max_idx = df.groupby('symbol')['date'].idxmax()

# Select rows and rename columns
df_min = df.loc[min_idx].rename(columns={'date': 'min_date', 'rate': 'min_rate'})
df_max = df.loc[max_idx].rename(columns={'date': 'max_date', 'rate': 'max_rate'})

# Merge dataframes minmax
df = pd.merge(df_min[['symbol', 'min_date', 'min_rate']],
                  df_max[['symbol', 'max_date', 'max_rate']],
                  on='symbol')

# Merge dataframes currencies
df = pd.merge(
    df,
    df_countries,
    how='left',
    left_on='symbol',
    right_on='cod_currency'
)

# Filter null currencies
df = df[df['cod_iso3'].notna()]

# Calculate variation
df['variation'] =  ( ( df['max_rate'] / df['min_rate'] ) -1 )

# Clasification groups
df['class'] = pd.cut(df['variation'], 
                     bins=[-float('inf'), -0.5, -0.3, -0.01, 0.01, 0.3, 0.5, 5, 12, 150, float('inf')],
                     labels=['G1', 'G2', 'G3', 'G4', 'G5', 'G6', 'G7', 'G8', 'G9', 'G10'])

# Classification groups names
class_name_map = {
    'G1': 'Appreciation > 50%',
    'G2': 'Appreciation [30-50%]',
    'G3': 'Appreciation [1-30%]',
    'G4': 'Equal',
    'G5': 'Depreciation [1-30%]',
    'G6': 'Depreciation [30-50%]',
    'G7': 'Depreciation [50-500%]',
    'G8': 'Depreciation [500-1000%]',
    'G9': 'Depreciation [1000-15000%]',
    'G10': 'Depreciation [>15000%]'
}
df['class_name'] = df['class'].map(class_name_map)

# Sort by class
df = df.sort_values(by='class')

# Rate and date column
df['before'] = df['min_rate'].map('{:.4f}'.format) + " (" + df['min_date'].dt.strftime('%Y-%m-%d') + ")"
df['after'] = df['max_rate'].map('{:.4f}'.format) + " (" + df['max_date'].dt.strftime('%Y-%m-%d') + ")"

# Download to Excel
download_folder = os.path.join(os.path.expanduser("~"), "Downloads")
filename = os.path.join(download_folder, "FIG_EXAPI_Currency_Map.xlsx")
df.to_excel(filename, index=False)

print(df)

# Data Manipulation
# ============================================
# Map
url = 'https://raw.githubusercontent.com/guillemmaya92/Analytics/refs/heads/master/Maps/world-countries-plotly.geojson'
geojson = requests.get(url).json()

# Custom Map Color
color_map_name = {
    'Appreciation > 50%': '#9CF6D2',
    'Appreciation [30-50%]': '#CDFFE8',
    'Appreciation [1-30%]': '#EFFFF8',
    'Equal': '#FFFFFF',
    'Depreciation [1-30%]': '#F2DCDB',
    'Depreciation [30-50%]': '#E6B8B7',
    'Depreciation [50-500%]': '#DA9694',
    'Depreciation [500-1000%]': '#963634',
    'Depreciation [1000-15000%]': '#632523',
    'Depreciation [>15000%]': "#291312"
}

# Figure map choropleth
fig = px.choropleth(
    df,
    geojson=geojson,
    locations='cod_iso3',
    featureidkey="properties.iso_a3",
    color='class_name',
    hover_name='country',
    hover_data={
        'cod_currency': True,
        'currency': True,
        'before': True,
        'after': True,
        'variation': ':,.2%',
        'cod_iso3': False,
        'class_name': False
    },
    color_discrete_map=color_map_name,
    category_orders={'class': ['G1', 'G2', 'G3', 'G4', 'G5', 'G6', 'G7', 'G8', 'G9', 'G10']},
    labels={'class_name': 'Classification'}
)

# Adjust plot
fig.update_geos(fitbounds="locations", visible=False)
fig.update_traces(marker_line_width=0.5, marker_line_color="#808080")
fig.update_layout(
    annotations=[
        dict(
            text="<b>The power of a floating exchange rate market</b>",
            xref="paper",
            yref="paper",
            x=0,
            y=1.05,
            showarrow=False,
            font=dict(size=20)
        ),
        dict(
            text="While USD is getting stronger, the world are feeling pain",
            xref="paper",
            yref="paper",
            x=0,
            y=1.015,
            showarrow=False,
            font=dict(size=16)
        ),
        dict(
            text="(Variation in exchange rates during the period 2001–2025)",
            xref="paper",
            yref="paper",
            x=0,
            y=0.985,
            showarrow=False,
            font=dict(size=13)
        ),
        dict(
            text="<b>Data Source:</b> <a href='https://exchangeratesapi.io' target='_blank' style='color:#3c586b;'>exchangerateapi.io</a>",
            xref="paper",
            yref="paper",
            x=0,
            y=0,
            showarrow=False,
            font=dict(size=12),
            align="left"
        )
    ],
    legend=dict(
        title=dict(text='<b>Categories</b>'),
        font=dict(size=11),
        x=1.02,
        y=0.5,
        yanchor='middle',
        xanchor='left',
        bgcolor='rgba(255,255,255,0.8)',
        bordercolor="#808080",
        borderwidth=1
    )
)

# Save it...
download_folder = os.path.join(os.path.expanduser("~"), "Downloads")
filename = os.path.join(download_folder, f"FIG_EXAPI_Currency_Map")
fig.write_html(filename + ".html")

# Show the plot!
fig.show()