Visualizing the Shift of Wealth: System Yields vs. Hard Assets using Python
The definition of wealth in our society is shifting. As Robert Kiyosaki noted, AI and technological advancements aren't just replacing jobs; they are moving wealth. We are witnessing a transition from a linear, labor-intensive model to a non-linear, system-driven economy.
But how can we actually see this structural change?
To visualize this macro regime shift, I wrote a Python script that analyzes the relationship between the "yield of the system" (US 10-Year Treasury Yield, or TNX) and the value of "hard assets" (precious and industrial metals).
The Core Concept: Yield-to-Metal Ratio
Instead of just looking at nominal prices, we calculate a specific ratio:
System Yield (TNX) / Metal Price
This simple yet powerful formula visualizes the "purchasing power" or attractiveness of system-generated yields relative to physical assets.
• When the line goes up: The system (yields) is generating wealth faster than holding the physical asset. Capital is moving into the system.
• When the line goes down: Physical assets are outperforming the system's yield.
To complete the picture, I also included the Implied Fed Funds Rate (derived from the 30-day Fed Funds futures, ZQ=F) to represent the central bank's policy maneuvers.
The Python Code
Here is the script using yfinance to fetch the data and matplotlib to visualize it:
```python
import yfinance as yf
import matplotlib.pyplot as plt
import pandas as pd
dateStart = "2015-01-01"
dateEnd = "2026-06-08"
tickers = ["GC=F", "SI=F", "PL=F", "HG=F", "PA=F", "^TNX", "ZQ=F"]
# Fetch data from yfinance
df = yf.download(
tickers,
start=dateStart,
end=dateEnd,
auto_adjust=False,
progress=False
)
# Calculate monthly average
close_m = df["Close"].resample("ME").mean()
# Convert ZQ=F to Implied FF Rate (%)
implied_ff_rate = 100 - close_m["ZQ=F"]
# Create DataFrame with Ratios and Policy Rate
ratio = pd.DataFrame({
"TNX/Gold": close_m["^TNX"] * 100 / close_m["GC=F"],
"TNX/Platinum": close_m["^TNX"] * 100 / close_m["PL=F"],
"TNX/Palladium": close_m["^TNX"] * 100 / close_m["PA=F"],
"TNX/Silver": close_m["^TNX"] / close_m["SI=F"],
"TNX/Copper": close_m["^TNX"] / close_m["HG=F"],
"Implied FF Rate (100 - ZQ=F) / 5": implied_ff_rate / 5 # Scaled for visibility
}).dropna()
# Plotting the graph
fig, ax = plt.subplots(figsize=(12, 6))
ratio.plot(ax=ax, linewidth=2)
ax.set_ylabel("Ratio / Yield (%)")
ax.set_xlabel("Month")
ax.legend(loc='upper left')
plt.title("System Yields vs Hard Assets & Policy Rate")
plt.grid(True, alpha=0.3)
plt.show()
```
Analyzing the Results: The Gravity of "Dr. Copper"
Looking at the generated chart, a fascinating structural dynamic emerges. While the ratios for Gold (the ultimate safe haven) and Platinum show significant deviations during different market phases, one relationship stands out:
Interest rates (both TNX and the Policy Rate) orbit closely around Copper.
In financial markets, Copper is often referred to as "Dr. Copper" because its price acts as a reliable leading indicator of global economic health. Copper is the fundamental building block of our physical infrastructure—from power grids to data centers.
This chart visually proves a critical macroeconomic reality:
1. The Engine: When society accelerates the building of physical infrastructure, the demand for Copper surges.
2. The Brake and Accelerator: To manage this heating economy, the central bank adjusts the policy rate (Implied FF Rate), and the broader system yield (TNX) follows suit.
The system's yield doesn't exist in a vacuum; it is heavily tethered to the pace of physical infrastructure expansion represented by Copper. By simply adjusting our mathematical lens in Python, we can filter out the daily noise and observe the deeper mechanics of where wealth is truly moving.