RealTest strategies / Turtle Max Growth Strategy

Turtle Max Growth Strategy

A systematic trend-following breakout strategy adapted for US equities, inspired by the classic Turtle trading rules. It enters expanding momentum, sizes positions based on volatility, and rides multi-month runners while aggressively cutting losers. Tested since 2000 on survivorship-bias-free Norgate data with real commissions and slippage modeled.

Live traded since Mar 2026Trading costs included Survivorship-bias-free data
$100k$300k$1.0M$3.0M'02'04'06'08'10'12'14'16'18'20'22'24'26 Live $6.6M $589k
Strategy SPY buy & hold
Sharpe ratio?
1.06
+121%vs SPY 0.48
Best year?
+49.1%
1.8×SPY best: 27.0% in 2003
Max drawdown?
-20.9%
58% smallervs SPY -49.9%

Validated four ways

See how each test is run →

Live market performance
Performs in real market conditions since 2026-03-17
Verified
Statistical validation
Validated across in-sample & out-of-sample data
Passed
Walk forward analysis
Tests robustness across unseen data
Passed
Monte Carlo stress test
Resilient against 1,000+ sequence risk simulations
Passed

What happened in every crash

CrisisDatesStrategySPYSame $100k in
Dotcom crash Mar 2000 – Oct 2002 -14.3% -41.8%
2008 financial crisis Oct 2007 – Mar 2009 -11.4% -49.9%
COVID-19 crash Feb 2020 – Mar 2020 -18.8% -28.7%
2022 bear market Jan 2022 – Oct 2022 -13.4% -21.3%

Strategy monthly returns

JanFebMarAprMayJunJulAugSepOctNovDecYear
2000-2.09.2-2.0-4.7-3.11.30.17.6-0.2-4.2-0.10.01.0%
20010.00.00.00.00.00.00.00.00.00.00.00.00.0%
2002-1.00.0-4.00.00.00.00.00.00.00.00.00.0-4.9%
20030.00.00.01.34.62.95.97.3-0.712.3-0.6-3.932.0%
20040.30.3-1.61.210.69.2-8.9-1.18.86.28.5-2.233.4%
20050.21.9-4.1-2.96.7-5.83.0-0.16.2-0.26.22.012.9%
20061.1-6.2-1.61.7-6.2-0.9-1.32.36.75.13.7-4.3-0.7%
2007-1.8-2.51.1-0.510.15.55.50.77.23.5-6.9-1.920.3%
2008-4.90.00.00.00.00.00.00.00.00.00.00.0-4.9%
20090.00.00.00.00.0-3.44.6-2.85.74.54.5-0.213.1%
2010-4.12.411.37.1-4.2-5.21.1-3.010.65.80.51.924.9%
20114.11.82.34.6-1.4-0.11.4-6.70.00.00.0-0.45.1%
20124.28.05.8-2.9-1.4-0.4-3.13.40.2-5.31.11.711.1%
20134.2-0.11.41.42.4-3.32.13.79.4-3.43.02.925.6%
20140.77.0-4.5-4.93.25.70.17.5-1.6-1.86.1-5.311.6%
2015-2.30.9-6.13.22.22.78.4-8.6-0.12.70.3-1.90.4%
2016-5.60.02.3-1.61.6-3.37.83.56.3-1.76.78.525.9%
20176.20.56.20.511.4-1.51.8-0.62.46.90.6-4.433.0%
201816.80.83.3-3.16.51.2-0.99.10.5-4.50.0-2.029.4%
20190.0-2.22.83.3-6.33.90.0-3.5-4.17.74.911.417.9%
20203.3-5.9-3.8-0.00.112.212.120.63.6-2.60.83.149.1%
2021-1.20.5-0.62.61.17.78.85.7-2.98.49.8-6.037.6%
2022-5.8-3.74.2-5.6-0.10.00.00.00.00.00.0-3.7-14.2%
20232.81.38.2-1.612.53.42.7-3.1-5.3-4.36.48.234.0%
20247.510.52.20.23.33.4-6.0-0.90.5-2.17.29.239.6%
20252.7-3.2-5.02.71.77.55.5-3.28.513.5-1.2-3.327.2%
20264.7-3.9-4.615.716.33.5-4.8-2.38.4-9.022.6%

Strategy overview

Why the trend-following edge works

Markets spend most of their time in choppy consolidation, interrupted by explosive directional trends. The Turtle model does not try to predict tops or bottoms. Instead, it buys when price breaks to new highs and proves buyers are in control. Positions are sized based on market volatility, and winning trades are given room to compound for months. The mathematics of trend following rely on an asymmetric payoff where large outlier winners pay for all the small, disciplined cuts along the way.

How it was tested

The backtest runs from 2000 to the present across US equities, including all delisted tickers so survivorship bias cannot distort the results. Interactive Brokers commissions and execution frictions are deducted on every trade. The strategy uses pure price action and classic volatility channels with minimal moving parts. This keeps the logic robust and avoids the curve-fitting that breaks indicator-heavy systems when traded live.

How it fits your portfolio

This system is the natural counterpart to mean-reversion. Mean-reversion systems thrive during range-bound conditions but struggle during sustained directional trends. Turtle does the opposite, capturing the large macro and stock-level runners that drive substantial portfolio growth. Pairing a breakout strategy with stock-level mean-reversion smooths your equity curve and cushions drawdowns across full market cycles.

Specification

StyleTrend followingUniverseNasdaq 100 current & past stocksTimeframeDailySideLongAverage hold27 trading daysTrade frequency2.2 per monthEntry executionBuy stop orderExit executionMarket on open OR Exit stop orderDataNorgate Data (US Stocks Platinum) SoftwareRealTest or PythonCommission $0.005 / shareSlippage 10 bps per sideLimit price buffer 0.05% past limitSPY benchmarkNo fees applied (favors SPY)

What you get

RealTest by MHP Trading logo
RealTest strategy code (.rts)
Complete RealTest (.rts) strategy code. Import, backtest, and modify.
Trading strategy rules plain text document
Plain text rules
Trade it on any platform without reading the code.
Python programming logo for quantitative trading backtest
Python backtest code (.py)
Full Python script. Implements the exact same strategy rules for independent backtesting.

Quick start & live trading

RealTest trading software logo
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
Watch setup tutorial →
OrderClerk automated trading execution interface
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'
Get OrderClerk automation course →

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
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
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
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
Systematic Traders
Algo trader & Substack writer (+6.4K subscribers)

FAQ

What is Turtle Max Growth Strategy?
A systematic trend-following breakout strategy adapted for US equities, inspired by the classic Turtle trading rules. It enters expanding momentum, sizes positions based on volatility, and rides multi-month runners while aggressively cutting losers. Tested since 2000 on survivorship-bias-free Norgate data with real commissions and slippage modeled.
Why should this edge keep working?
Markets spend most of their time oscillating in ranges, but when genuine breakouts occur, trends tend to persist far longer than most participants anticipate. A stock breaking through multi-week channel highs shows that aggressive institutional demand is absorbing available supply. By taking breakout signals, scaling exposure to volatility, and giving winners room to run while cutting failed trades quickly, the math relies on an asymmetric return distribution. A small number of large multi-month winners pays for all the small, disciplined scratches along the way. Trend-following momentum is one of the most documented and durable structural edges in market history.
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, slippage of 10 bps per side, and a limit-order buffer of 0.05% past limit, so the equity curve is after costs.
When did live trading start?
Live trading started in March 2026. 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

2026-03-17Rules finalised. Live tracking starts.

No rule changes since.

Portfolio growth since 2000
6,507.6%
SPY same period: 489.0%
$990one-time
Instant download
MetricStrategySPY
Sharpe1.060.48
ROR16.95%–
Sortino0.960.45
MAR0.81–
Net profit$6.5M$489k
Expectancy4.11%–
Max exposure100.71%100%
Worst year-14.2%-33.2%

Download the complete strategy now

Active Script - C:\REALTEST\Strategies\turtle_trading.rts
Turtle Max Growth Strategy full RealTest script
Python programming logo for quantitative trading backtest
Python backtest code (.py)
Full Python script. Implements the exact same strategy rules for independent backtesting.
Turtle Max Growth Strategy
$990one-time
Instant download