Alpha Report
Natural Gas
Composite Signal
September 2026

Market SectorEnergyModelPythia-v0.12.0SignalComposite
Date FromSep 01 2026Date ToSep 30 2026
Published Oct 15, 2026

1. Executive Summary

NG Macro Review for September 2026

Natural gas started September firmly: late-summer heat, tighter storage balances and LNG feedgas back above 19 Bcf/d gave prompt-month futures a fourth straight weekly gain by 4 September. The tone softened as forecasts cooled later in the month and speculators added to their net short positions, while storage stayed above its five-year average heading into the heating season.

Signal Performance Overview for September 2026

Trailing 12-month Sharpe 1.064 and return 21.693% (Table 3), down from Sharpe 1.408 and return 29.236% for the 12 months ending August.

The strategy was roughly flat in September at −0.11% (Sharpe −0.08) with a win rate of 39.05%. Longs gained 0.92 points and shorts lost 1.03 points; the rebased natural gas series in Figure 1 rose about 4.5% but in volatile swings, and the maximum intra-month drawdown reached −4.99%.

Risk Posture: The trailing 12-month maximum drawdown deepened to −10.65% from −8.65%, and the win rate fell about 2.0 percentage points to 53.44%; the trailing return also fell as a very strong September a year earlier (+7.4%) rolled out of the window.

Signal Coverage: Natural Gas

Asset ClassTrading SymbolName
FuturesNGHenry Hub Natural Gas
FuturesQGE-mini Natural Gas
ETFUNGUnited States Natural Gas Fund

2. Trading Strategy

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

  • Entry positions: every 5 minutes between 09:30 and 09:45 ET (4 possible openings per day) we take a long, short or no position. Each position is split into 5 parts, executed on each minute of the 5 minutes after the decision. No sizing adjustment.
  • Exit positions: each decision's position is closed between 14:25 and 14:30 ET, its five parts over the five minutes.
  • Costs: 1.25 bp per side, 2.5 bp round trip (NG). Extra exchange/clearing fees not included.
  • Contract series & roll: Front month monthly contract. Switch at the open 7 trading days before the last business day of the month preceding delivery, ahead of the contract's last trading day, and start trading the next month.

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-v0.12.0: committee of models voting per decision
VersionNATGAS_DTC v7
ExchangeCME Globex
DataLevel II Limit Order Book (10 levels)
Trained Time PeriodQ1 2020 to Q4 2025
Final Validation PeriodJan 2026 to Aug 2026

4. Performance Metrics

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

MonthReturn (%)Win Rate (%)
2026 YTD9.97249.686
2026 Sep-0.10739.048
2026 Aug-0.64638.182
2026 Jul1.16357.778
2026 Jun-5.15227.179
2026 May-0.32565.833
2026 Apr0.46154.545
2026 Mar4.18856.279
2026 Feb14.77884.000
2026 Jan-4.38834.583
2025 Dec9.86663.500
2025 Nov0.48166.667
2025 Oct1.37463.333

Table 2: Year over Year Performance Comparison

MonthReturn (%)Win Rate (%)
Sep 2026-0.10739.048
Sep 20257.43664.490
Sep 202410.42970.435

Table 3: 12-Months Ending Performance

Metric12 months ending Sep 202612 months ending Aug 2026Change
Sharpe1.0641.408-0.344
Return (%)21.69329.236-7.544
Win Rate (%)53.43755.470-2.033
Max DD (%)-10.648-8.654-1.995
Volatility21.65622.267-0.612
Calmar1.9773.275-1.299

Table 4: Quarterly Performance

QuarterSharpeReturn (%)Win Rate (%)Max DD (%)VolatilityCalmar
Q3 20260.1580.40946.000-6.81910.2340.231
Q2 2026-1.365-5.01646.250-10.89211.715-1.684
Q1 20261.90914.57954.545-10.23152.0346.929

Figure 1: Cumulative equity curve showing the trading strategy net long/short performance compared with the NG price (100 = September 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

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