RealTest Low Drawdown Nasdaq Mean Reversion Strategy
A RealTest mean-reversion strategy for Nasdaq 100 stocks that sizes each position by current market volatility. It buys oversold pullbacks in the Connors and Alvarez style, then automatically holds less when markets turn turbulent, which is what keeps its drawdowns shallow. Tested on survivorship-free Norgate data with commissions modeled, and trading live since January 2025.
Live traded since Jan 2025Trading costs included
Survivorship-bias-free data
Strategy
SPY buy & hold
Sharpe ratio?
1.36
+183%vs SPY 0.48
Best year?
+46.2%
1.7×SPY best: 27.0% in 2003
Max drawdown?
-9.4%
81% smallervs SPY -49.9%
Validated four ways
Live market performance
Performs in real market conditions since 2025-01-02
VerifiedStatistical validation
Validated across in-sample & out-of-sample data
Walk forward analysis
Tests robustness across unseen data
Monte Carlo stress test
Resilient against 1,000+ sequence risk simulations
What happened in every crash
| Crisis | Dates | Strategy | SPY | Same $100k in |
|---|---|---|---|---|
| Dotcom crash | Mar 2000 – Oct 2002 | 48.7% | -41.8% | |
| 2008 financial crisis | Oct 2007 – Mar 2009 | 0.6% | -49.9% | |
| COVID-19 crash | Feb 2020 – Mar 2020 | 0.0% | -28.7% | |
| 2022 bear market | Jan 2022 – Oct 2022 | 2.5% | -21.3% |
Strategy monthly returns
| Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2000 | 1.6 | 7.0 | 8.3 | 8.5 | 6.7 | 0.2 | 2.5 | 1.6 | 1.9 | -1.1 | 0.7 | 1.2 | 46.2% |
| 2001 | 1.3 | 0.1 | -0.0 | 0.0 | 1.7 | 3.1 | 2.4 | 0.7 | 0.0 | 0.0 | 0.1 | 3.3 | 13.4% |
| 2002 | 0.3 | 1.1 | 0.7 | 0.0 | 1.6 | -1.0 | -0.4 | 0.0 | 0.6 | 0.0 | 0.0 | 0.9 | 3.8% |
| 2003 | 2.0 | 0.2 | 0.5 | 3.9 | 2.5 | 2.8 | 2.5 | 2.7 | -2.1 | 7.7 | 4.2 | 0.8 | 31.1% |
| 2004 | 0.4 | 3.3 | -0.4 | 1.0 | 1.3 | 1.1 | 1.2 | 0.3 | 1.1 | 1.7 | 0.4 | 1.6 | 13.7% |
| 2005 | 2.2 | 2.5 | 2.2 | 0.5 | 0.0 | 1.8 | 1.4 | -3.3 | 0.6 | 1.0 | -0.1 | 4.2 | 13.7% |
| 2006 | 3.6 | 1.5 | 1.0 | 1.4 | 0.8 | 4.4 | 0.8 | 0.1 | 0.1 | 1.1 | 1.0 | 3.2 | 20.7% |
| 2007 | 1.4 | 1.0 | 0.4 | 0.3 | 1.1 | 0.9 | -0.6 | 1.6 | 2.3 | 0.5 | 3.6 | 0.5 | 13.6% |
| 2008 | -5.1 | 1.7 | 1.2 | 0.1 | 2.6 | 0.0 | 1.2 | 0.8 | -3.3 | -0.0 | 0.0 | 0.0 | -1.2% |
| 2009 | 0.1 | -1.0 | -0.9 | 0.0 | 0.9 | -1.0 | 1.3 | 2.4 | 3.6 | -1.2 | 3.3 | 2.0 | 9.8% |
| 2010 | -2.4 | 2.7 | 1.4 | 0.3 | -0.0 | -1.8 | 2.7 | 1.4 | 0.4 | 0.4 | 1.0 | 0.7 | 6.7% |
| 2011 | 0.6 | 1.0 | -0.6 | 0.4 | 2.3 | -2.4 | -0.2 | -3.6 | -0.0 | 1.2 | 3.2 | 1.5 | 3.2% |
| 2012 | 0.0 | 0.8 | 1.0 | 0.3 | 0.4 | 0.6 | 1.0 | -0.4 | -0.3 | 0.8 | 0.1 | 0.6 | 4.9% |
| 2013 | 1.1 | 2.7 | -0.6 | 0.6 | 0.2 | 2.1 | 0.4 | 2.5 | 1.4 | 1.0 | 2.8 | 0.9 | 16.3% |
| 2014 | -2.9 | 1.0 | 0.3 | 0.9 | 0.4 | 0.6 | -0.4 | 0.0 | -0.2 | 0.3 | 0.4 | 2.2 | 2.4% |
| 2015 | 2.6 | -0.2 | 1.1 | 2.1 | 1.5 | -0.5 | 1.2 | 0.9 | 0.1 | 0.0 | 0.8 | 1.5 | 11.6% |
| 2016 | -2.0 | 0.0 | 0.8 | 0.7 | 0.6 | 0.4 | -0.2 | 0.4 | -0.4 | 1.0 | 0.8 | 0.5 | 2.6% |
| 2017 | 2.2 | -0.1 | -0.5 | -0.0 | 0.9 | 0.3 | 1.1 | 0.6 | 0.2 | 1.3 | 0.4 | 0.8 | 7.5% |
| 2018 | 1.2 | -2.3 | 0.7 | 2.0 | 1.1 | -0.6 | -0.6 | 0.2 | 1.2 | -2.9 | 0.6 | -1.9 | -1.5% |
| 2019 | -0.0 | 0.7 | 3.1 | 0.5 | -2.7 | 2.3 | 1.7 | 0.3 | 1.3 | 0.7 | 1.5 | 0.8 | 10.5% |
| 2020 | -0.5 | -0.1 | 2.0 | 0.0 | 1.4 | 0.4 | 0.6 | 2.7 | 3.8 | -1.1 | 6.2 | 3.9 | 20.7% |
| 2021 | 1.0 | 1.8 | 2.9 | 2.0 | 0.4 | 1.4 | 1.9 | 1.4 | -2.0 | 0.5 | -1.5 | 2.0 | 12.3% |
| 2022 | 0.3 | 2.4 | -0.1 | 1.2 | 0.1 | -0.3 | 0.2 | -0.1 | -1.8 | 1.1 | 0.6 | 2.5 | 6.2% |
| 2023 | 0.2 | -0.4 | 2.5 | -1.2 | 1.6 | 3.8 | 0.9 | -0.1 | -1.6 | -0.1 | 0.1 | 1.6 | 7.3% |
| 2024 | 0.6 | 1.3 | 0.9 | 0.3 | -0.6 | 1.2 | 1.2 | 0.1 | 3.0 | 1.0 | 0.8 | -4.0 | 6.0% |
| 2025 | 6.6 | 0.1 | -0.4 | 0.2 | 0.2 | 0.3 | 0.6 | -0.1 | 0.6 | 1.8 | -3.6 | 0.4 | 6.7% |
| 2026 | 2.3 | 0.8 | -1.2 | 0.2 | -0.8 | 1.1 | 12.9 | 2.5 | 2.1 | -8.7 | 10.4% |
Strategy overview
Specification
StyleMean reversionUniverseNasdaq 100 current & past stocksTimeframeDailySideLongAverage hold4 trading daysTrade frequency9.1 per monthEntry executionLimit orderExit executionLimit & marketDataNorgate Data (US Stocks Platinum)
SoftwareRealTest or PythonCommission$0.005 / shareLimit price buffer0.1% past limitSPY benchmarkNo fees applied (favors SPY)
What you get
RealTest strategy code (.rts)
Complete RealTest (.rts) strategy code. Import, backtest, and modify.
Plain text rules
Trade it on any platform without reading the code.
Python backtest code (.py)
Full Python script. Implements the exact same strategy rules for independent backtesting.
Quick start & live trading
Quick start guide
From download to first backtest in 1 minute
1
You download the .rts file
Click download
2
You open it in RealTest
File → Open → Select file
3
You import symbols
Click import
4
You run the backtest
Click test
Daily live trading
For RealTest automated execution
1
Open OrderClerk
Click 'Connect to IB'
2
Import signals
Click 'Import' in RealTest
3
Generate orders
Click 'Orders' in RealTest
4
Execute trades
Click 'Place Orders'
What traders say
❝
Buying a SetupAlpha strategy didn't save me money, it saved me research time. I estimate it replaced 70–90 hours of development, debugging, and validation. Even if I never trade the strategy exactly as delivered, the research process was worth the investment.
Roman Blackwood
Founder of AI in Trading (+13K subscribers)
❝
I've been using a strategy from SetupAlpha and I'm really impressed with its elegance and stability. The unique approach gives me a fresh perspective and ideas I can apply to other strategies I'm developing. I'm currently working on integrating it into my suite of trading strategies.
TradeQuantiX
Multi-country systematic trader (+4.7K subscribers)
❝
I traded discretionary for over a decade and kept putting off going systematic because I did not know where to start. Having a finished, tested system to take apart was what finally got me moving.
Daniel
Crypto trader
❝
I have run it live since 2024 and it is still in my portfolio. It also changed how I test the systems I build myself.
Systematic Traders
Algo trader & Substack writer (+6.4K subscribers)
FAQ
What is RealTest Low Drawdown Nasdaq Mean Reversion Strategy?
A RealTest mean-reversion strategy for Nasdaq 100 stocks that sizes each position by current market volatility. It buys oversold pullbacks in the Connors and Alvarez style, then automatically holds less when markets turn turbulent, which is what keeps its drawdowns shallow. Tested on survivorship-free Norgate data with commissions modeled, and trading live since January 2025.
Why should this edge keep working?
Mean reversion buys stocks that have sold off too hard and waits for the recovery. The usual problem is that the same volatility that creates those setups also drives the deep drawdowns, because a plain strategy is fully exposed when the market is at its wildest. This strategy measures volatility as it changes and scales its exposure to match, holding smaller in turbulent conditions and larger in calm ones, so the drawdowns stay shallow while the mean-reversion edge stays intact. Volatility-scaled position sizing is a standard, well-tested risk technique.
Do I need RealTest to run this strategy, or can I use Python?
No, you do not need RealTest if you use Python. This strategy includes a complete, standalone Python implementation (.py) alongside the RealTest (.rts) script and plain-text rules. You can run, backtest, and modify the strategy directly in Python (using libraries like pandas, NumPy, vectorbt, etc.) with your own data source. If you prefer a turnkey trading platform, RealTest is fully supported for one-click backtests and automated live execution via OrderClerk and Interactive Brokers.
How was this backtested?
Survivorship-free Norgate data from January 2000 to the present, with out-of-sample walk-forward analysis and over 1000 Monte Carlo simulations to test stability.
Are transaction costs included?
Yes. The results include Interactive Brokers commissions, and a limit-order buffer of 0.1% past limit, so the equity curve is after costs.
When did live trading start?
Live trading started in January 2025. The equity curve shows the backtest from 2000 and real performance from that point on.
What's included in the download?
Complete RealTest .rts source code. Full Python implementation (.py script for independent backtesting). Plain-text trading rules document. Full documentation with parameter explanations.
How does it compare to SPY?
The chart at the top plots this strategy against SPY, the S&P 500 ETF, from 2000 with both starting at $100,000. A metrics table lists the exact numbers side by side: Sharpe, Sortino, net profit and worst year, for the strategy and for SPY over the same period.
Can I modify the code?
Yes. You get the complete source code (both RealTest and Python) with a perpetual single-user license. You can freely adjust parameters, rewrite position sizing, combine rules with other strategies, or embed the logic into your own custom quant pipeline.
What market data do I need?
For RealTest, Norgate Data (US Stocks Platinum) is recommended for survivorship-free testing. For Python, you can use your own historical data (CSV/Parquet), Norgate, or free sources like Yahoo Finance for exploratory testing. The benchmark backtest metrics shown here use survivorship-free Norgate Platinum data.
Can I automate execution?
Yes. For RealTest users: daily signals are generated in RealTest and automated via OrderClerk directly to Interactive Brokers (automation course available). For Python users: you can generate signals directly in your Python workflow and route orders to Interactive Brokers (via the IBKR API / ib_insync) or any broker that supports an API.
Can I ask questions about the code or the strategy?
Yes. Email info@setupalpha.com and you will get an answer, before or after you buy.
Changelog
2025-01-02Rules finalised. Live tracking starts.
No rule changes since.
Portfolio growth since 2000
1,444.3%
SPY same period: 489.0%
$999one-time
Instant download
| Metric | Strategy | SPY |
| Sharpe | 1.36 | 0.48 |
| ROR | 10.78% | – |
| Sortino | 1.23 | 0.45 |
| MAR | 1.15 | – |
| Net profit | $1.4M | $489k |
| Expectancy | 0.97% | – |
| Max exposure | 99.73% | 100% |
| Worst year | -1.5% | -33.2% |
Download the complete strategy now
Active Script - C:\REALTEST\Strategies\low_drawdown_nasdaq_mean_reversion.rts
Python backtest code (.py)
Full Python script. Implements the exact same strategy rules for independent backtesting.
RealTest Low Drawdown Nasdaq Mean Reversion Strategy
$999one-time
Instant download