Data up to..
Wednesday, April 22, 2026
Timezone
UTC
This interactive line chart compares monthly inflation to the gold, silver, and copper metal commodity futures returns. It displays how the metal markets behave during periods of rising or falling inflation. The Month to Month Inflation is displayed in its percentage form. ## Row {height=“45%”}
This interactive chart compare movements in the Federal Funds Rate to returns the in metal commodity futures, displaying each commodity futures relationship with the federal funds rate as an economic indicator.
This interactive chart compares changes in unemployment to returns in the metal commodity futures. They illustrate how these commodity markets behave during shifts in labor‑market conditions. Note that the change in the unemployment rate is displayed in decimal form rather than percentage form. ## Row {height=“45%”}
This summary table compares gold, silver, and copper by their average prices, associated economic indicators, and return volatility. It provides a quick snapshot of how each metal future behaves and its potential link to broader economic conditions. The output is descriptive, helping with comparison, but does not prove or depict any actual relationships. ## Row {height=“25%”}
| Commodity | Futures_Average | Economic_Indicator_Average | Standard_Deviation |
|---|---|---|---|
| Gold | 1294.587163 | 0.0021241 | 1.116704 |
| Silver | 18.725052 | 1.9280135 | 2.072606 |
| Copper | 2.879105 | 5.6712544 | 1.710917 |
This heatmap displays the correlation between key economic indicators and commodity futures. It shows how the each of the metal future prices relate to inflation, interest rates, and unemployment. Highlighting how the metals markets co‑move with major macroeconomic variables. ## Row {height=“50%”}
Data up to..
Wednesday, April 22, 2026
Timezone
UTC
This interactive line chart compares monthly inflation to the energy commodity futures returns natural gas, crude oil, and gasoline. It displays how the energy markets behave during periods of rising or falling inflation. The Month to Month Inflation is displayed in its percentage form. ## Row {height=“45%”}
This interactive chart compares movements in the Federal Funds Rate to returns in the energy commodity futures, displaying each commodity futures relationship with the federal funds rate as an economic indicator. ## Row {height=“45%”}
This interactive chart compares changes in unemployment to returns in the energy commodity futures. It illustrates how these commodity markets behave during shifts in labor‑market conditions. Note that the change in the unemployment rate is displayed in decimal form rather than percentage form. ## Row {height=“45%”}
This summary table compares natural gas, crude oil, and gasoline futures by their average prices, associated economic indicators, and return volatility. It provides a quick snapshot of how each energy future behaves and its potential link to broader economic conditions. The output is descriptive, helping with comparison, but does not prove or depict any actual relationships. ## Row {height=“25%”}## Row {height=“25%”}
| Commodity | Futures_Average | Economic_Indicator_Average | Standard_Deviation |
|---|---|---|---|
| Crude Oil | 64.026108 | 0.0021234 | 25.4277258 |
| Natural Gas | 4.329285 | 1.9294190 | 2.2357646 |
| Gasoline | 1.891891 | 5.6707697 | 0.7551368 |
This heatmap displays the correlation between key economic indicators and the chosen energy commodity futures. The first chart shows how the chosen energy future prices relate to inflation, interest rates, and unemployment. It depicts the energy markets movement correlation with major macroeconomic variables. ## Row {height=“50%”}
These two graphs quantify the monthly average logarithmic standard deviation of our futures stocks, mapping them from 2001-2025. This highlights the variability in return prices relative to similar futures.
These two graphs display the average biweekly return compared to each year’s average return price or our futures to identify seasonal variability. Holiday markers are included to highlight periods where cultural or seasonal events may drive demand shifts.
This page examines how conflict conditions escalate by comparing shock versus non-shock periods and the relationship between severity and impact.
This chart compares conflict intensity during shock and non‑shock periods. Shock periods represent unusually high‑intensity events that exceed the established threshold, while non‑shock periods reflect baseline conditions.
This chart examines the relationship between impact and severity across conflict events. Severity reflects the intensity level of each event, while impact captures the broader effect or scale of the conflict.
Focus
News Tone vs Futures Market Impact
Data Sources
Kaggle Analyst Ratings | Yahoo Finance via tidyquant
Time Period
2008 – 2020
Financial news sentiment was scored daily using the Bing lexicon applied to headline text. Days where positive words outweighed negative words by more than 10% were labeled Positive, the reverse Negative, and all others Neutral.
Note: Sentiment data covers 2008–2020. Futures returns outside this window are excluded from sentiment comparisons. The sentiment score is a general financial news signal and is not commodity-specific.
This dashboard was created using Quarto in RStudio, and the R Language and Environment.
The dataset used to create this dashboard was downloaded from Yahoo Finance, Kaggle, and Federal Reserve Bank of St. Louis
The Holidays dataset used to create this dashboard was downloaded from Microsoft Azure Databases
Allaire J, Dervieux C (2024). quarto: R Interface to ‘Quarto’ Markdown Publishing System. R package version 1.4.4, https://CRAN.R-project.org/package=quarto.
Arnold J (2024). ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’. R package version 5.1.0, https://github.com/jrnold/ggthemes, https://jrnold.github.io/ggthemes/.
Bache S, Wickham H (2025). magrittr: A Forward-Pipe Operator for R. doi:10.32614/CRAN.package.magrittr https://doi.org/10.32614/CRAN.package.magrittr, R package version 2.0.4, https://CRAN.R-project.org/package=magrittr.
Dancho M, Vaughan D (2025). tidyquant: Tidy Quantitative Financial Analysis. doi:10.32614/CRAN.package.tidyquant https://doi.org/10.32614/CRAN.package.tidyquant, R package version 1.0.11, https://CRAN.R-project.org/package=tidyquant.
Kunst J (2022). highcharter: A Wrapper for the ‘Highcharts’ Library. R package version 0.9.4, https://CRAN.R-project.org/package=highcharter.
Neuwirth E (2022). RColorBrewer: ColorBrewer Palettes. R package version 1.1-3, https://CRAN.R-project.org/package=RColorBrewer.
Posit team (2025). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.
R Core Team (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.https://www.R-project.org/.
Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.
Xie Y (2025). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.50, https://yihui.org/knitr/.
Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963
Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible Research in R. In Victoria Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595
Zhu H (2024). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.4.0, https://github.com/haozhu233/kableExtra, http://haozhu233.github.io/kableExtra/.