Backtest the ISM PMI scenario trading strategy against 12 months of historical data. See how regime classification translates into portfolio returns 鈥?free, interactive, runs in your browser.
The macro scenario backtest takes a series of monthly ISM Manufacturing PMI readings and runs each through the Macro Edge classification engine 鈥?the same engine powering the live API. Each reading is classified into one of five scenarios (GOLDILOCKS, MODERATE, SOFT_LANDING, CONTRACTION, AMBIGUOUS), and the corresponding action set determines the portfolio allocation for that month. The simulator then tracks portfolio value month by month, computing total return, maximum drawdown, and scenario distribution.
This is the quantitative companion to the ISM PMI trading strategy guide. Where the strategy guide explains what to do in each scenario, this backtest shows how it would have performed 鈥?converting macro regime classification into measurable, backtested returns.
Edit any month's ISM readings below, then click Run Backtest. Values are ISM sub-indices (0鈥?00, 50 = breakeven).
| Month | Headline PMI | New Orders | Prices Paid | Scenario (preview) |
|---|
The backtest uses a simplified four-asset portfolio model. Each scenario maps to a target allocation across SPY (equities), US10Y (bonds), BTC (crypto), and CRDO (commodities). The confidence score scales the position size 鈥?at 85% confidence, you deploy 85% of the scenario's target allocation; at 40% (Ambiguous), you deploy only 40%.
| Scenario | SPY | US10Y | BTC | CRDO | Bias | Confidence |
|---|---|---|---|---|---|---|
| GOLDILOCKS | 50% (long) | 10% (short dur) | 20% (long) | 20% (long) | RISK-ON | 85% |
| MODERATE | 40% (hold) | 25% (hold) | 15% (hold) | 20% (hold) | NEUTRAL | 70% |
| SOFT_LANDING | 25% (reduce) | 35% (long dur) | 10% (reduce) | 30% (hold) | CAUTIOUS | 65% |
| CONTRACTION | 10% (sell) | 50% (long dur) | 5% (sell) | 10% (sell) | RISK-OFF | 90% |
| AMBIGUOUS | 30% (hold) | 30% (hold) | 10% (hold) | 30% (hold) | HOLD | 40% |
Each asset class has a scenario-dependent monthly return drawn from a realistic distribution. Equities (SPY) gain in Goldilocks/Moderate, lose in Contraction, and drift in Soft Landing. Bonds (US10Y) gain in Contraction/Soft Landing (duration rally) and lag in Goldilocks. Crypto (BTC) amplifies the equity direction with higher volatility. Commodities (CRDO) have a structural bid that holds in most scenarios except Contraction. The simulator applies these returns to the current allocation each month, then reclassifies and rebalances based on the next month's ISM reading.
Most macro trading strategies are qualitative 鈥?"be cautious when data softens" is not a backtestable rule. The Macro Edge engine converts macro data into a discrete, rule-based classification with a defined action set for each scenario. This discretization is what makes it backtestable: every ISM PMI print maps to exactly one scenario, one action set, and one portfolio allocation. Over 12 months, you can measure whether the regime-following approach actually outperforms buy-and-hold.
The backtest answers three questions that the trading journal tracks forward but cannot answer retrospectively: (1) What was the total return of the scenario-following strategy? (2) What was the maximum drawdown, and which scenario caused it? (3) How much time was spent in each regime? These metrics let you compare the macro scenario approach against alternatives 鈥?buy-and-hold SPY, 60/40, or your own discretionary timing.
Total Return is the percentage gain/loss on the $10,000 starting capital over the 12-month period. A positive return means the scenario-following strategy was profitable; the magnitude tells you how much edge the macro classification added.
Max Drawdown is the largest peak-to-trough decline in portfolio value. This is the worst-case loss you would have experienced if you entered at the worst possible month. Lower drawdown is better 鈥?the macro scenario strategy is designed to reduce drawdown by rotating defensive before Contraction confirms.
Scenario Distribution shows what percentage of months were classified into each regime. A backtest dominated by Goldilocks should show strong returns; one dominated by Contraction should show defensive outperformance. The distribution reveals the macro regime character of the period.
Buy & Hold Comparison shows what a passive SPY buy-and-hold would have returned over the same period. If the scenario strategy outperforms buy-and-hold, the macro classification is adding value. If it underperforms, the regime switching may be too slow or the period may not have had enough regime transitions to benefit from classification.
This is a simulated backtest, not a live trading record. The monthly returns are modeled from realistic distributions, not historical price data 鈥?they represent what each asset class typically does in each scenario, not what actually happened in any specific month. The model assumes monthly rebalancing, no transaction costs, and perfect classification at the moment of the ISM release. Real-world performance will differ due to slippage, intra-month volatility, and the gap between ISM release and execution. Use this simulator to understand the logic of the scenario strategy, not to project specific returns. For real backtesting with historical price data, use the Macro Scenario Analysis API with your own data pipeline.
Notion journal + live API + Pine Script indicator + HTML dashboard + Python report generator + live demo + backtest simulator 鈥?$14.99 one-time, zero subscriptions.
Get the Bundle ($14.99) View All GuidesA macro scenario backtest runs historical ISM Manufacturing PMI readings through the scenario classification engine (GOLDILOCKS, MODERATE, SOFT_LANDING, CONTRACTION, AMBIGUOUS) and simulates how a portfolio following each scenario's action set would have performed over time. It converts macro regime classification into measurable portfolio returns.
The simulator takes a series of monthly ISM PMI readings (headline, new orders, prices paid), classifies each into a scenario using the Macro Edge engine, maps each scenario to a trading action set, and simulates portfolio allocation changes month by month. It reports total return, max drawdown, scenario distribution, and a month-by-month equity curve.
The backtest includes a preset 12-month series of realistic ISM PMI readings that transition through all four actionable scenarios. You can also edit each month's readings manually to test custom scenarios or paste your own historical ISM data.
The backtest starts with $10,000 and allocates across four instruments (SPY, US10Y, BTC, CRDO) based on each scenario's action set and confidence score. Monthly returns are simulated using scenario-appropriate drift and volatility.
Yes, the backtest simulator is completely free and runs entirely in your browser. No signup, no API key. The classification engine is a JavaScript port verified against 19/19 unit tests.