Verifying with Python! Visualizing the Relationship Between Money Supply (M2) and Gold Prices

"When money is printed, gold prices rise."

This is a common saying in the investment world.

In fact, it is said that when monetary easing or quantitative easing is implemented, the value of fiat currency relatively decreases, making gold prices prone to rising.

So, is that really the case?

This time, using Python,

• US M2 (Money Supply)

• Gold Price

I combined these to,

"M2 ÷ Gold Price"

create and analyze a unique indicator.

Data Used

I used two types of data this time.

M2 (Money Supply)

M2 was obtained from FRED (Federal Reserve Economic Data).

In Python, you can easily obtain it just by using pandas_datareader.

#FREDからデータ取得
def fred(symbol,start,end):
    df = data.FredReader(symbol, start, end) 
    date = df.read().index
    value = df.read()[symbol]
    return date, value

m2_date, m2_price = fred("M2NS", start, end)

Since it is updated monthly, you can automatically retrieve the latest data.

Gold Price

On the other hand, I used a CSV file for the gold price.

gold_history = pd.read_csv('chart_20260625T044248.csv')

The dates in the CSV are

01/01/1915 #month/day/year

in a string format (object type), so

gold_history["Date"] = pd.to_datetime(
    gold_history["Date"],
    format="%m/%d/%Y"
)

I am converting it to datetime type as follows.

After that,

gold_history = gold_history.set_index("Date")
gold_value = gold_history['Value']

I made it possible to handle it as time-series data with.

Merging M2 and Gold Prices

Since both use the date and time as an index,

merge_df = pd.DataFrame()

merge_df["gold"] = gold_value
merge_df["m2"] = m2_price

merge_df.dropna(inplace=True)

you can easily merge them just by.

Pandas is very convenient because it automatically aligns data where the dates match.

Proprietary Indicator "M2 to Gold Ratio"

The indicator I created this time is as follows.

merge_df["ratio"] = (
    merge_df["m2"] /
    merge_df["gold"]
)

In other words,

an indicator obtained by dividing the amount of money in the market by the gold price

is what it becomes.

The larger this value is,

• the more undervalued gold is relative to the money supply

and the smaller it is,

• the more overvalued gold is relative to the money supply

can be considered.

Of course, this is an indicator I created for analysis and does not indicate the fair price of gold. However, it can be used as a yardstick for observing long-term trends.

Adding Moving Averages

To make it easier to grasp the trend,

• 50-month moving average

• 100-month moving average

• 200-month moving average

were also calculated.

merge_df["SMA50"] = (
    merge_df["ratio"]
    .rolling(50)
    .mean()
)

merge_df["SMA100"] = (
    merge_df["ratio"]
    .rolling(100)
    .mean()
)

merge_df["SMA200"] = (
    merge_df["ratio"]
    .rolling(200)
    .mean()
)

To observe long-term trends, overlaying moving average lines makes changes easier to understand.

Create graph

The green line represents the ratio of M2 to gold prices.

Furthermore,

• Blue: 50-month moving average

• Red: 100-month moving average

• Yellow: 200-month moving average

are overlaid.

By doing this, you can check short-term, medium-term, and long-term trends simultaneously.

What can be read from the graph

Looking at the graph, it is clear that the ratio of M2 to gold prices is not constant but follows a large cycle.

In the 1970s, the ratio dropped significantly due to the surge in gold prices.

The ratio increased in the early 2000s, but has since declined again following the financial crisis, quantitative easing, and the COVID-19 pandemic.

Also, the ratio is currently at a low level compared to the past, which indicates that gold prices remain relatively high in relation to the increase in money supply.

Of course, you cannot make investment decisions based solely on this ratio. However, it is interesting that by viewing gold prices from the perspective of money supply, you can grasp long-term changes that are difficult to notice from news alone.

The fun of analyzing with Python

In this analysis, by using Python, I was able to combine different data sources to create my own unique indicators.

Beyond just using existing technical indicators,

“What would happen if I combined this data with that data?”

The ability to analyze with that kind of mindset is the appeal of Python.

Since you can automate the entire flow of acquiring, processing, and visualizing data, once you have created the program, you can easily reproduce the same analysis with the latest data.

Summary

In this article, I used Python to calculate the ratio of money supply (M2) to gold prices and visualized the long-term trends.

Here are the three things I learned from this analysis:

• You can handle different data by combining FRED and CSV

• You can easily merge time-series data with Pandas

• By creating your own indicators, you can analyze the market from a new perspective

Python is a powerful tool that allows you to not only look at existing data but also realize your own unique analysis.