Performance
Next Wave Daily
Next Wave Daily identifies stocks showing unusual strength and delivers predefined trade plans with entries, stops, and trailing exits. The hypothetical results below summarize thousands of historical signals across multiple market environments using the same core rules-based framework first developed by Andrew Falde in 2018.
Average Winner
+10.4%
Winning trades only
Average Loser
-4.9%
Losing trades only
Average Trade
+1.89%
Across all modeled trades
Maximum Drawdown
-8.8%
S&P 500: -50.8%
Sharpe Ratio
1.39
S&P 500: 0.72
Sortino Ratio
3.83
S&P 500: 1.06
Best Year
+82.1% (2020)
S&P 500 best: +32.3% (2013)
Worst Year
-6.9% (2022)
S&P 500 worst: -36.8% (2008)
See how the signals are structured in real time.
Next Wave Daily combines predefined entries, trailing-stop management, and historical trade behavior into a daily execution framework.
Historical Growth Comparison
Log scale view. Both series start at $1. Strategy uses equal-size distribution to signals with up to 100 concurrent positions, net of modeled $0.02 per-share average round-trip slippage.
Designed to participate when strength is present — and stay defensive when it is not.
The goal is not constant market exposure. During weaker environments, the model often reduced exposure significantly while waiting for stronger conditions to emerge.
Drawdown Comparison
Drawdown is measured from each series’ prior peak.
Trade Behavior
Bars held are measured in trading days. These statistics are based on the full trade log.
Average Trade Duration
10.1 days
Winners: 14.9 · Losers: 6.2
Quick Losses
57.8%
Losing trades closed within 5 trading days
Extended Winners
22.9%
Winning trades held at least 20 trading days
Trade behavior: The model is designed to cut weaker trades quickly while giving stronger trades more room to develop. Across the historical test, losing trades averaged 6.2 trading days, while winning trades averaged 14.9 trading days. More than half of losing trades closed within five trading days, while nearly one quarter of winners remained open for at least 20 trading days. Individual trades were profitable 44.9% of the time, while 60.2% of calendar months still finished positive overall because winning trades tended to last longer and outpace the size of losing trades.
This is an important part of the experience. The model does not require every signal to work immediately. Instead, it relies on defined entries, trailing-stop exits, and consistent application across many signals over time.
This is an important part of the experience. The model does not require every signal to work immediately. Instead, it relies on defined entries, trailing-stop exits, and consistent application across many signals over time.
Exit management: All exits in this model are based on trailing-stop logic. Positions remain open until the trailing stop is triggered.
In practice, this can reduce the number of ongoing decisions required after entry. A trader can make the initial decision to enter, then use a broker-supported trailing stop so the trade can largely manage itself. The stop adjusts as the trade moves, helping reduce exposure when price weakens while allowing stronger trades more time to continue.
Many traders also choose to close positions one trading day before earnings to reduce event-related volatility. That is a practical risk-management overlay, not a separate forecast about whether the earnings move will be favorable or unfavorable.
In practice, this can reduce the number of ongoing decisions required after entry. A trader can make the initial decision to enter, then use a broker-supported trailing stop so the trade can largely manage itself. The stop adjusts as the trade moves, helping reduce exposure when price weakens while allowing stronger trades more time to continue.
Many traders also choose to close positions one trading day before earnings to reduce event-related volatility. That is a practical risk-management overlay, not a separate forecast about whether the earnings move will be favorable or unfavorable.
Ready to see the actual signals, entries, and trailing-stop management in real time?
Move from historical behavior to daily trade plans with predefined entries, stops, and management rules.
Calendar-Year Performance
Annual performance comparison using year-end monthly equity values.
Monthly Performance
Each cell shows the modeled monthly performance for Next Wave Daily, net of slippage.
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Year |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1997 | +0.0% | +0.4% | +0.9% | -0.0% | +1.2% | +0.1% | +4.2% | +1.6% | +0.8% | +3.6% | -1.2% | -0.0% | — |
| 1998 | -0.8% | -0.7% | +0.4% | +0.8% | -0.4% | -0.0% | +0.7% | +0.2% | +0.4% | +0.1% | +1.0% | +1.7% | +3.4% |
| 1999 | +2.6% | -1.0% | -0.6% | +0.6% | +0.4% | +1.4% | +4.0% | -0.4% | +1.5% | +1.8% | +9.8% | +7.8% | +30.8% |
| 2000 | +14.6% | +12.0% | +6.9% | -3.1% | -3.7% | +1.8% | +2.8% | +0.2% | +2.9% | -1.1% | -1.1% | -0.4% | +34.5% |
| 2001 | +0.5% | -0.4% | +0.1% | +0.0% | +0.6% | -0.2% | -0.6% | -0.3% | -0.3% | -0.1% | +0.3% | -0.9% | -1.1% |
| 2002 | -0.9% | -0.1% | -0.3% | -1.1% | +0.3% | -0.0% | -0.5% | +0.1% | +0.0% | -0.0% | -0.1% | -0.2% | -2.9% |
| 2003 | -0.4% | -0.3% | -0.8% | +0.2% | +1.7% | +6.8% | +0.3% | +0.7% | +1.3% | -0.1% | +2.1% | -0.4% | +11.4% |
| 2004 | +4.6% | -0.1% | -0.3% | +0.4% | -0.4% | +0.2% | +1.5% | +0.2% | +1.3% | +0.2% | +1.4% | +1.5% | +10.9% |
| 2005 | +0.2% | +0.3% | +0.0% | -0.4% | +0.2% | +1.2% | +0.4% | -0.2% | +0.8% | +1.2% | +0.8% | +1.3% | +5.9% |
| 2006 | +3.2% | +0.9% | +1.1% | +0.8% | -0.9% | -0.7% | -0.9% | -0.1% | +0.6% | +0.9% | +1.8% | -0.9% | +5.8% |
| 2007 | +0.8% | +1.3% | +0.5% | +2.0% | -0.2% | +0.4% | +2.4% | -0.6% | -0.1% | +4.0% | +0.4% | -0.5% | +10.9% |
| 2008 | -0.6% | -0.1% | -0.8% | +0.2% | +0.1% | +0.6% | -0.6% | -0.4% | -0.5% | — | -0.4% | -0.3% | -2.8% |
| 2009 | -0.1% | -0.6% | -0.2% | -0.2% | +1.2% | -0.6% | +2.0% | +2.3% | +1.2% | -0.4% | -0.9% | -0.1% | +3.7% |
| 2010 | +2.1% | +0.0% | +1.5% | +1.2% | +0.2% | -1.3% | -0.5% | -0.7% | -0.1% | +1.9% | +0.2% | +0.5% | +4.9% |
| 2011 | +1.0% | +1.2% | -0.1% | +0.2% | -1.3% | -0.2% | +0.4% | -0.5% | -0.2% | -0.6% | -0.3% | -0.2% | -0.6% |
| 2012 | +0.6% | +2.4% | +0.3% | -0.0% | -0.4% | -1.2% | +0.3% | +0.0% | +0.6% | +1.6% | +0.1% | +0.8% | +5.3% |
| 2013 | +0.6% | +0.2% | -0.4% | +0.9% | +2.3% | +0.5% | +2.5% | +1.0% | +0.6% | +1.1% | -0.9% | +0.8% | +9.7% |
| 2014 | +2.8% | +0.8% | +0.4% | -0.1% | -0.5% | +0.9% | +1.3% | +0.0% | +0.4% | -0.6% | +1.3% | +0.7% | +7.6% |
| 2015 | -0.4% | +0.6% | +0.8% | +0.8% | -0.1% | +1.0% | -0.1% | -0.4% | -0.3% | -0.1% | +0.8% | -0.2% | +2.2% |
| 2016 | -0.6% | -0.4% | -0.6% | +0.7% | -0.0% | +0.6% | +0.5% | +2.5% | +1.0% | +1.1% | -0.4% | +0.5% | +5.0% |
| 2017 | +1.1% | +2.1% | +1.9% | +0.8% | +2.3% | +2.4% | +0.6% | +0.2% | +1.6% | +3.2% | +4.2% | +0.6% | +23.0% |
| 2018 | +3.9% | +0.2% | +2.4% | -0.7% | +1.1% | +5.3% | -0.0% | +1.4% | +4.2% | -1.0% | -1.7% | -2.7% | +12.7% |
| 2019 | -0.9% | +3.3% | +5.2% | -0.1% | +2.1% | +0.5% | +1.6% | +0.3% | +0.1% | -0.3% | +1.7% | +6.1% | +21.2% |
| 2020 | +5.8% | +4.4% | -1.5% | +2.1% | +9.5% | +5.3% | +8.6% | +4.0% | +4.5% | +5.5% | +1.7% | +12.3% | +82.1% |
| 2021 | +13.2% | +8.4% | -6.1% | -0.9% | -0.8% | +3.9% | +1.5% | -0.1% | +2.4% | +3.5% | +4.0% | -1.6% | +29.5% |
| 2022 | -1.8% | -0.7% | -1.1% | +0.0% | -0.1% | +0.0% | +0.3% | +0.9% | -1.5% | -0.6% | -1.2% | -1.4% | -6.9% |
| 2023 | +1.8% | +0.7% | -2.8% | -0.0% | +2.4% | +2.2% | +6.7% | -1.1% | -1.3% | -2.7% | +1.2% | +7.0% | +14.5% |
| 2024 | +7.0% | +4.3% | +5.6% | -2.5% | +3.3% | +1.4% | +1.3% | -1.2% | +1.0% | +6.6% | +11.0% | +1.1% | +45.6% |
| 2025 | -2.7% | +3.0% | -5.6% | -2.6% | +4.1% | +4.1% | +8.8% | +3.1% | +10.9% | +10.9% | -2.2% | +2.5% | +38.0% |
| 2026 | +7.7% | -2.2% | -2.8% | +1.8% | +4.6% | — | — | — | — | — | — | — | +9.2% |
Risk Multiple Profile
R multiples measure results relative to the initial risk defined by the trade setup.
What is R? Many traders measure performance using R multiples instead of dollars or percentages. One R represents the amount initially at risk on a trade based on the stop distance.
For example, risking 5% to potentially make 10% equals +2R. Risking 5% and losing 5% equals -1R.
This framework helps standardize trade evaluation across stocks with different prices and volatility levels. It also allows traders to scale position sizes more consistently while comparing opportunities on a common basis.
Because the model uses trailing-stop exits rather than fixed profit targets, winning trades can expand well beyond the original risk amount. In rare cases, overnight gaps can also produce losses larger than the intended stop level.
Across thousands of historical trades, the model generated positive expectancy because larger winning trades outweighed the frequency and size of losing trades.
For example, risking 5% to potentially make 10% equals +2R. Risking 5% and losing 5% equals -1R.
This framework helps standardize trade evaluation across stocks with different prices and volatility levels. It also allows traders to scale position sizes more consistently while comparing opportunities on a common basis.
Because the model uses trailing-stop exits rather than fixed profit targets, winning trades can expand well beyond the original risk amount. In rare cases, overnight gaps can also produce losses larger than the intended stop level.
Across thousands of historical trades, the model generated positive expectancy because larger winning trades outweighed the frequency and size of losing trades.
Average Winner
+2.58R
Average winning trade
Average Loser
-1.19R
Average losing trade
Expected Value
+0.48R
Per trade
Annual Performance Table
| Year | Next Wave Daily | S&P 500 ($SPY) | Difference |
|---|---|---|---|
| 2026 | +9.2% | -4.6% | +13.8% |
| 2025 | +38.0% | +17.7% | +20.3% |
| 2024 | +45.6% | +24.9% | +20.7% |
| 2023 | +14.5% | +26.2% | -11.7% |
| 2022 | -6.9% | -18.2% | +11.2% |
| 2021 | +29.5% | +28.7% | +0.7% |
| 2020 | +82.1% | +18.3% | +63.7% |
| 2019 | +21.2% | +31.2% | -10.0% |
| 2018 | +12.7% | -4.6% | +17.3% |
| 2017 | +23.0% | +21.7% | +1.3% |
| 2016 | +5.0% | +12.0% | -7.0% |
| 2015 | +2.2% | +1.2% | +1.0% |
| 2014 | +7.6% | +13.5% | -5.9% |
| 2013 | +9.7% | +32.3% | -22.6% |
| 2012 | +5.3% | +16.0% | -10.7% |
| 2011 | -0.6% | +1.9% | -2.5% |
| 2010 | +4.9% | +15.1% | -10.2% |
| 2009 | +3.7% | +26.4% | -22.7% |
| 2008 | -2.8% | -36.8% | +34.0% |
| 2007 | +10.9% | +5.1% | +5.7% |
| 2006 | +5.8% | +15.8% | -10.0% |
| 2005 | +5.9% | +4.8% | +1.1% |
| 2004 | +10.9% | +10.7% | +0.2% |
| 2003 | +11.4% | +28.2% | -16.8% |
| 2002 | -2.9% | -21.6% | +18.7% |
| 2001 | -1.1% | -11.8% | +10.6% |
| 2000 | +34.5% | -9.7% | +44.3% |
| 1999 | +30.8% | +20.4% | +10.4% |
| 1998 | +3.4% | +28.7% | -25.2% |
Methodology
Summary: Trade-level backtest → filtered to max 100 concurrent trades → sized by available symbol universe → adjusted for $0.02/share round-trip slippage → aggregated into monthly returns → compounded monthly → compared to S&P 500 ($SPY).
- Allocation: max(1 / available symbols, 1% minimum per trade).
- Capital constraint: maximum 100 concurrent open positions. Trades beyond capacity were skipped. (This rarely happens and reduced trade count by only 2.5%).
- Slippage: Estimated based on $0.01/share entry + $0.01/share exit on median entry price of $44.15 (~0.045%).
- Compounding: for purposes of comparing to benchmark; monthly, based on trades closing during each calendar month.
- Idle cash: Because the strategy often has undeployed capital, idle cash is modeled using a conservative risk-free return assumption.
- Benchmark: SPY adjusted close, monthly, rebased to the same starting value.
- Assumptions: no leverage; no commissions included beyond modeled slippage; results are modeled and not guarantees.
Structured execution. Historical transparency. Real-time signals.
Next Wave Daily delivers predefined entries, trailing exits, and historical trade behavior across repeatable market structures.
Historical Signal Log Download
Review thousands of historical signals including entries, exits, holding periods, returns, R-multiples, and trailing-stop behavior.
These hypothetical trades are provided for transparency and educational review and have important limitations. The raw signal log does not fully account for market factors including liquidity constraints, commissions, taxes, market impact, or execution differences between traders and brokers. Modeled slippage assumptions are applied separately within the performance statistics shown on this page. Past hypothetical performance is not a guarantee of future results.
Download Historical Signal Log (CSV)