Our Market Risk On/Off System trades between the S&P 500 (SPY) during risk-on periods and the AGF U.S. Market Neutral Anti-Beta (BTAL) during risk-off periods. Historical testing assumes buying and selling on next day open with 1.25% slippage.
Recently held (past year): SPY
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| Sharpe Ratio | |
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12 closed calls: 8 up, 4 down or flat. Losses are included. Result % is the change in system equity over the hold, not a per-share return.
| Symbol | Entry | Exit | Result |
|---|---|---|---|
| BTAL | 2025-04-21 | 2025-05-02 | -5.81% |
| BTAL | 2025-03-04 | 2025-04-18 | 4.01% |
| SPY | 2024-08-13 | 2025-03-04 | 6.67% |
| BTAL | 2024-08-06 | 2024-08-13 | -0.65% |
| SPY | 2023-04-28 | 2024-08-06 | 27.79% |
| BTAL | 2023-04-26 | 2023-04-28 | 0.50% |
| SPY | 2022-07-18 | 2023-04-26 | 6.14% |
| BTAL | 2022-04-18 | 2022-07-18 | 8.68% |
| BTAL | 2022-04-06 | 2022-04-15 | 1.74% |
| SPY | 2022-03-24 | 2022-04-06 | -1.08% |
See every closed call for this system · How our machine-learning models time trades
Past performance is not indicative of future results.
If you want to participate in market rallies but attempt to side-step the inevitable setbacks, the Market On/Off system might be right for you. Rather than simply moving to cash, this model moves to Anti-Beta during market sell-offs. Historically, Anti-Beta has moved in the opposite direction of the S&P 500 - that means this system can profit during market declines without having to go short.
Just watch the daily PortfolioAI email for updates to the model and when indicated, take the trade.
PortfolioAI is the result of almost 15 years of research. We started with simple momentum models and technical indicators and have slowly grown our capability to include over 40 different parameters for each security. Early into our research with Machine Learning we developed a simple but effective strategy for labelling our data-set. That insight allowed us to train our MLs to predict whether the next period was likely to be 'Optimal' or 'Non-Optimal' for owning the security. We use an ensemble of ML combinations for each security for additional robustness. Our MLs are retrained frequently and we continue to search for new data sets to add to our work.
Risk-on and risk-off describe investor behavior in response to market sentiment. Risk-on signifies higher risk tolerance and investment in riskier assets. Risk-off reflects risk aversion and a shift towards safer options. These dynamics impact various asset classes based on investor sentiment and economic conditions. During periods of low perceived risk, the risk-on risk-off theory suggests that investors gravitate towards higher-risk investments.