Market Reaction

By shrijeetverma13 · April 18, 2026

This dataset tracks the real-world impact of US tariff policies and the ongoing trade war - from Section 232 Steel tariffs (2018) to the 2025 Liberation…

The steel-to-aluminum futures price ratio has evolved significantly over time, and the chart shows both the ratio trend with moving averages and the underlying price movements of each metal. Currently, the ratio sits at 0.2908 — notably below the historical average of 0.3960 — and is in a falling short-term trend (30-day MA below 90-day MA). This signals that aluminum prices are relatively stronger compared to steel right now, pointing toward demand strength in aluminum-intensive sectors like automotive, aerospace, and lightweight manufacturing, rather than heavy construction or infrastructure.

The analysis shows that the S&P 500 slightly leads the Shanghai Composite by approximately 1 day in trend reversals, not the other way around. The peak cross-correlation of 0.318 occurs at a lag of -1 day, meaning movements in the S&P 500 tend to appear in the Shanghai Composite the following day. Two data tables were generated summarizing the cross-correlation results across lags from -30 to +30 days.

The analysis examined how the US Dollar Index (DXY) correlates with four key commodity prices. Two data tables were generated showing the results. Crude Oil (WTI) has the strongest positive correlation with the dollar (r=0.45), meaning when the dollar strengthens, oil prices tend to rise modestly. Steel Futures show the strongest negative correlation (r=-0.43), meaning a stronger dollar tends to push steel prices lower. Aluminum and Soybeans show very weak correlations (r=0.05 and r=0.07 respectively), suggesting the dollar has little direct influence on their prices in this dataset.

The data does not support a strong risk-off dynamic between USD/CNY and S&P 500 selloffs. The overall correlation between their daily percentage changes is nearly zero (-0.028), indicating no consistent relationship. On S&P 500 selloff days (215 total), USD/CNY weakened only about half the time (50.7%) — essentially a coin flip. Two visualizations were generated: a scatter plot showing the daily % change relationship colored by market regime, and a 60-day rolling correlation chart tracking how this relationship evolves over time.

The S&P 500 showed strong overall growth from 2020 to 2026, climbing from approximately 3,258 to 7,126 — a total return of roughly 119%. The chart displays the full price history with major peaks (red triangles) and troughs (green triangles) marked along the way, highlighting the key turning points in the market cycle. Despite significant volatility during this period, the long-term trend was clearly upward.

The analysis successfully identified extreme crude oil WTI price moves, including the historic negative price event, and charted how markets reacted. Two visualizations were generated: the first shows the full WTI price history with extreme days highlighted in red, plus a bar chart of daily percentage changes. The second chart compares oil, S&P 500, and DXY percentage changes side-by-side on the most extreme oil days, giving a clear picture of cross-market reactions.

The bar chart shows the average daily S&P 500 return for each calendar month, color-coded from red (negative) to green (positive). November is historically the best month with an average daily return of +0.2424%, while September is the worst, averaging -0.1410% per day.

The analysis successfully calculated and visualized the maximum drawdown for each of the 8 assets in the dataset. A bar chart has been generated showing the peak-to-trough percentage decline for each asset, color-coded by severity so you can quickly spot which assets experienced the steepest losses from their highs.

The analysis produced two interactive charts and supporting data tables showing monthly volatility patterns for the S&P 500 and four commodities (Crude Oil, Steel, Aluminum, and Soybeans). The first chart displays S&P 500 monthly volatility over time alongside commodity volatility trends. The second chart zooms into the top 10 most volatile months for the S&P 500 and compares them side-by-side with commodity volatility during those same periods, making it easy to spot whether commodities spiked in tandem with equities.

The chart shows how the 60-day rolling correlation between the S&P 500 and crude oil (WTI) has evolved over time. The correlation fluctuates significantly, swinging between positive and negative values, indicating a dynamic and regime-dependent relationship between these two assets rather than a stable, consistent one.

The year-to-date 2026 performance analysis covers all major assets from January 1 through April 17, 2026. Two interactive charts were generated: a line chart showing the cumulative % change over time for all assets, and a bar chart highlighting total YTD returns with green bars for gains and red bars for losses. These visuals make it easy to spot which assets are trending up or down and which is showing the strongest momentum heading into mid-2026.

The analysis examined how equities, oil, metals, and agriculture recovered from the March 2020 COVID crash. Three data tables were generated comparing the recovery performance of key assets including the S&P 500, Shanghai Composite, Crude Oil WTI, Steel Futures, Aluminum Futures, and Soybeans — tracking each asset's drawdown depth and the number of days needed to return to pre-crash peak levels.

Soybeans do NOT closely track DXY movements — the overall correlation is just 0.07, which is essentially negligible. However, soybeans do show a strong relationship with crude oil (0.69), making them a partial inflation proxy rather than a pure dollar-hedge. Two charts were generated: a normalized price comparison with a 60-day rolling correlation panel, and a scatter plot showing the soybean-DXY relationship with a trend line.

The relationship between Shanghai Composite rallies and metal futures is PARTIAL — aluminum futures clearly confirm China demand signals, while steel shows only a weak connection. Two scatter plots and conditional return bar charts were generated to visualize these dynamics.