Alpha Report
S&P 500 | May 2026

Market SectorEquity IndexModelPythia-v0.5.0
Date FromMay 01 2026Date ToMay 31 2026
Published Jun 1, 2026

1. Executive Summary

ES Macro Review for May 2026

During May 2026, the S&P 500 delivered a robust +4.64% return as markets rallied on dovish signals from the Federal Reserve, which pivoted toward a more accommodative stance amid softening inflation data and growing concerns over geopolitical tensions in Eastern Europe that had initially weighed on risk sentiment early in the month. The strategy underperformed significantly with a -0.06% return, likely caught off-guard by the sharp risk-on rotation that accelerated into month-end as investors repositioned for potential rate cuts. Late-month momentum proved particularly challenging for the strategy as equity markets surged on renewed optimism around monetary easing, with the benchmark posting its strongest weekly performance in the final week of May.

Signal Performance Overview for May 2026

Trailing 12-month Sharpe 1.03 and return 4.41% (through the period in Table 3 below). During the report month, the S&P 500 benchmark rose approximately 4.64%; the strategy returned -0.06% over the same window. The current 12-month period shows marginally higher maximum drawdown at -4.22% compared to the previous period's -4.19%, while the win rate declined slightly from 53.92% to 53.19%. Overall risk characteristics remain remarkably consistent between periods, with both drawdown magnitude and hit rate showing minimal variation that suggests stable risk management performance.

Signal Coverage — S&P 500

Asset ClassTrading SymbolName
FuturesESE-mini S&P 500
FuturesMESMicro E-mini S&P 500
ETFSPYSPDR S&P 500 ETF

2. Trading Strategy

In order to produce the metrics below we use the signal in combination with the trading strategy below:

  • Leverage: No leverage is applied for this strategy and metrics
  • Positions:
    • Entry positions: Every 5 minutes (between 09:45 and 15:30 ET) we decide to take a long, short or no position using 1/70 of our starting portfolio for the day (there are 70 possible openings per day). Each long/short position is then split into 5 parts and executed on each minute for the next 5 minutes following the decision. There is no sizing adjustment.
    • Exit positions: We exit all positions at the end of the day. The exits are split over five minutes (15:55–16:00 ET).
  • Costs: 1 bp round-turn assumption. Extra exchange/clearing fees not included.
  • Contract series & roll: Front-month continuous. Switch at the open T–5 trading days before expiration; stop trading the expiring contract and start trading the next.

For detailed examples, flowcharts, and a full walkthrough of the trading strategy, see Benchmark Trading Strategy.

3. Model Training Data and Timeframe

CategoryValue
Model FamilyPythia
Versionv0.5.0
ExchangeCME Globex
DataLevel II Limit Order Book (10 levels)
Retrained Time Period21Q1 to 24Q4
Final Validation Period25Q1 to 26Q1

4. Performance Metrics

Table 1: Monthly Return and Win Rate Metrics (Last 12 Months)

MonthReturn (%)Win Rate (%)
2026 YTD-0.01251.384
2026 May0.15147.794
2026 Apr-1.00240.946
2026 Mar-0.37950.875
2026 Feb-0.54847.124
2026 Jan2.51370.539
2025 Dec0.58056.876
2025 Nov0.26147.283
2025 Oct0.70252.089
2025 Sep1.52762.937
2025 Aug1.29860.865
2025 Jul0.89455.729
2025 Jun-0.81944.653

Table 2: Year over Year Performance Comparison

MonthReturn (%)Win Rate (%)
May 20260.15147.794
May 20250.95656.172
May 20241.02254.480

Table 3: 12-Months Ending Performance

Metric12 months ending May 202612 months ending Apr 2026Change
Sharpe1.0261.387-0.361
Ann Return (%)4.4125.417-1.004
Win Rate (%)53.18853.917-0.730
Max DD (%)-4.223-4.185-0.037
Volatility3.9234.113-0.190
Calmar0.9541.363-0.409

Figure 1: Cumulative equity curve showing the trading strategy net long/short performance compared with the ES price (100 = May 01, 2026)

5. Next Steps

Download historical predictions for this month using the Client API and confirm performance in your own test harness.

6. Contact

Please reach out with any questions or comments at: info[at]quantumsignals.ai