RealTest strategies / RealTest All-Time-High Mean-Reversion Strategy

RealTest All-Time-High Mean-Reversion Strategy

Name: RealTest All-Time-High Mean-Reversion Strategy
Brand: SetupAlpha
SKU: all-time-high-mean-reversion-realtest-strategy
Price: 899.00 USD
Availability: InStock
Rating: 5.0 (4 reviews)

A RealTest mean-reversion strategy that buys Russell 3000 stocks on a pullback from a new all-time high. Tested on survivorship-bias-free Norgate data, with Interactive Brokers commissions and limit fills modeled, and trading live since May 2024.

Live traded since May 2024Trading costs included Survivorship-bias-free data

Strategy SPY buy & hold

Sharpe ratio?

1.08

+125%vs SPY 0.48

Best year?

+50.2%

1.9×SPY best: 27.0% in 2003

Max drawdown?

-24.0%

52% smallervs SPY -49.9%

Validated four ways

See how each test is run →

Live market performance

Performs in real market conditions since 2024-05-07

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

Crisis	Dates	Strategy	SPY
Dotcom crash	Mar 2000 – Oct 2002	25.8%	-41.8%
2008 financial crisis	Oct 2007 – Mar 2009	2.1%	-49.9%
COVID-19 crash	Feb 2020 – Mar 2020	-12.5%	-28.7%
2022 bear market	Jan 2022 – Oct 2022	-5.7%	-21.3%

Strategy monthly returns

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Year
2000	4.4	12.6	-15.3	12.7	10.4	0.9	5.2	9.8	3.9	0.2	0.0	0.0	50.2%
2001	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0%
2002	0.0	0.0	0.2	0.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.6%
2003	0.0	0.0	0.0	0.2	2.6	1.3	-0.9	0.8	0.8	8.5	5.3	2.3	22.5%
2004	0.4	2.9	2.3	-1.7	3.9	-0.8	-0.7	-0.4	1.1	2.0	1.8	2.0	13.3%
2005	-5.2	3.6	6.6	-2.9	-0.8	3.1	0.1	2.9	2.1	-5.3	1.5	1.2	6.4%
2006	9.9	-4.1	5.2	1.0	-1.0	-2.4	0.3	-0.1	-0.8	2.2	2.2	1.0	13.5%
2007	1.4	-2.7	-2.1	1.2	3.3	2.4	-0.6	2.7	1.0	3.7	-1.4	0.1	9.0%
2008	0.0	0.0	0.0	0.0	0.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.4%
2009	0.0	0.0	0.0	0.0	0.0	2.8	1.3	2.7	-0.3	-0.8	2.8	0.2	9.0%
2010	-1.7	1.8	0.6	0.6	9.5	-2.1	0.6	0.2	0.0	1.1	1.0	1.3	13.5%
2011	2.5	2.7	1.5	1.9	3.0	-0.3	-3.1	-2.2	0.0	0.1	0.1	0.0	6.0%
2012	1.6	0.8	2.6	1.1	-0.5	2.1	-1.9	0.6	0.2	0.3	0.6	0.1	7.7%
2013	1.0	0.9	0.5	0.2	0.6	1.8	1.7	-1.3	2.7	4.2	3.3	1.6	18.6%
2014	-1.8	2.5	-0.7	9.8	0.6	1.4	-1.9	3.7	3.1	-12.0	1.0	2.4	7.0%
2015	2.9	2.2	5.6	0.4	2.0	1.1	1.6	-1.8	0.1	0.0	1.2	-0.6	15.5%
2016	0.0	0.0	0.2	-0.8	1.2	3.3	0.2	0.0	4.1	-0.1	2.3	2.5	13.6%
2017	0.7	0.9	0.6	0.5	0.1	2.2	-0.0	3.8	-2.2	6.2	1.4	0.1	15.0%
2018	1.3	1.8	0.6	2.9	2.7	-1.0	0.9	6.6	0.2	-4.4	0.8	0.0	12.7%
2019	0.0	0.2	2.7	3.3	-1.7	1.2	5.1	1.2	-1.4	4.5	1.3	2.4	20.2%
2020	-0.1	-2.0	-5.6	0.0	0.6	2.2	5.8	-5.2	2.6	-0.9	11.9	1.2	9.7%
2021	5.2	-3.9	1.3	1.5	4.4	-0.0	1.8	-0.4	1.0	3.2	-0.7	0.3	14.2%
2022	-4.9	0.9	0.5	-0.3	-1.6	0.0	0.0	0.0	0.0	0.0	0.0	0.1	-5.3%
2023	0.1	2.3	-0.7	1.3	-3.1	4.0	3.5	1.0	-1.1	-1.5	2.8	-0.4	8.3%
2024	8.0	8.4	5.2	0.4	1.9	2.1	0.8	0.4	1.0	3.5	9.8	-0.1	49.4%
2025	19.7	-7.0	-2.8	-1.2	0.5	0.9	1.8	2.5	5.7	3.5	-4.5	2.5	21.2%
2026	6.1	0.6	4.2	2.3	7.7	10.9	-4.4	2.4	2.2	-2.7			32.5%

Strategy overview

Why the pullback happens

A stock at a new all-time high has no overhead supply. Everyone holding it is in profit, so there is no trapped seller waiting to exit at breakeven. The new high triggers profit-taking from short-term holders and shakes out late buyers, and that selling pushes the stock below where its trend would place it. The strategy waits for that pullback rather than buying the breakout. The stock is strong and briefly cheaper, which is a different situation from a stock that is simply falling.

How it was tested

The backtest runs on the Russell 3000 with delisted companies included, so survivorship bias does not inflate the results. Every fill is modeled with a limit-order buffer and Interactive Brokers commissions, and a liquidity filter keeps entries in stocks you can trade at size, so the equity curve is after real costs. The rules use few parameters, which leaves fewer ways to fit the strategy to past data, and it was checked with walk-forward analysis and Monte Carlo simulation. Both are shown on this page.

How it fits your trading

The strategy takes many small trades and holds them for a short time, so capital keeps turning over rather than sitting in one position. It is long-only and built on liquid US equities, which makes it straightforward to run alongside a trend or breakout system. Because the rules are simple and the logic is clear, it is a strategy you can follow through a full market cycle without second-guessing each signal.

Specification

StyleMean reversionUniverseRussell 3000 current & past stocksTimeframeDailySideLongAverage hold9 trading daysTrade frequency7.6 per monthEntry executionLimit orderExit executionMarket on openDataNorgate 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.

Trading strategy rules plain text document

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

You download the .rts file

Click download

You open it in RealTest

File → Open → Select file

You import symbols

Click import

You run the backtest

Click test

Watch setup tutorial →

OrderClerk automated trading execution interface

Daily live trading

For RealTest automated execution

Open OrderClerk

Click 'Connect to IB'

Import signals

Click 'Import' in RealTest

Generate orders

Click 'Orders' in RealTest

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

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 All-Time-High Mean-Reversion Strategy?

Why should this edge keep working?

A new all-time high means no seller is trapped above the current price, which is a structurally strong position. The pullback that follows is usually profit-taking and late-buyer exits rather than a change in trend. Entering into that selling means buying a stock that is still in an uptrend at a short-term low. The behavior behind it, short-term traders taking profits into strength, is a standard feature of how stocks trade, not a one-time pattern.

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 May 2024. 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.