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RealTest Short Term Mean Reversion Strategy
$790.00
30-Day Refund
Free Ongoing Support
Full RealTest code
A complete RealTest strategy built exclusively on the highest-probability quantified rules from the widely respected book Short-Term Trading Strategies That Work by Larry Connors and Cesar Alvarez.
Included files
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RealTest file (.rts)
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Full code in .TXT file with RealTest code for the AmiBroker or Python users (Python users mostly convert this code to python with chatgpt, need help send email)
Requirements
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RealTest
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Norgate Data – US Stocks (Platinum / Diamond package)
Strategy Equity Curve
All results shown include trading costs in the calculations.
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Combined Monthly Percent Gains
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Survivourship Bias Free
This strategy uses Norgate’s Premium data, ensuring every backtest includes both delisted (past) and current stocks.
Backtested with Real Cost
- This strategy incorporates brokers (IBKR) commission.
- Slippage applied to market orders.
- Added limit extra buffers reflecting real-world trading fills.
Robust Logic, No Curve-Fitting
Built on fundamental market drivers not overfitted indicator formulas. Our strategies prioritize robustness and simplicity over complexity for its own sake.
30-Day Refund
If the strategy logic or performance metrics differ from what is shown on our website, or if there are coding errors or issues, we will offer a full refund. Read more.
Free Ongoing Support
We’re here to help with everything from setup to step-by-step strategy customization.
No Coding Needed
How To Use
Download strategy, open RealTest, click "import" and "test".
Quick & Easy Download
Frequently Asked Questions
What do I get when I buy a strategy?
Each strategy includes the RealTest (.rts) file and a fully written-out text file detailing the strategy rules. This makes it easy to adapt the logic for Python, Amibroker, TradeStation, or other platforms.
The product description clearly outlines the included files, so you know exactly what you're getting.
Can I trust the backtests?
Yes, and here’s why:
- We use robust testing techniques (out-of-sample, Monte Carlo, walk-forward)
- We include trading costs, avoid survivorship bias, and don’t over-optimize
If a strategy doesn’t pass our own robustness checks, it’s not sold.
How do I combine multiple strategies?
If you're building a portfolio:
- We help you combine uncorrelated strategies in RealTest (trend, breakout, mean reversion, short)
- You can build “all-weather” setups and even apply dynamic weighting (based on VIX, trend filters, etc.)
- Ask for help, we’ll guide you
We recommend starting with four systems - Portfolio Builder.
What happens after I buy?
Here’s the usual process:
- Download files
- Open RealTest
- Open downloade file in RealTest
- Click test
We're available throughout this process if you need help.
Can I run this strategy in RealTest without coding?
Yes. All strategies are ready to load and run. No coding required. Just open RealTest, select the file, and hit “Run.”
What data do I need?
Most strategies are designed for Norgate Premium Data (U.S. stocks) to avoid survivorship bias and ensure proper backtests. Norgate Silver data works fine if you want only live trade. Some simpler strategies can also use Yahoo Finance. Check each product page for specifics.
Can I use this in Python, AmiBroker, or other platforms?
Yes. Each strategy includes a full text description of the logic, so you can rebuild it in other platforms.
We’ve had users convert to Python, AmiBroker, TradeStation, and more.
How is the forecasted performance calculated?
Below the monthly performance chart, you can see the average gain/loss for each month. If the next month is August, the forecast uses August’s historical average performance, then converts that percentage into a dollar value. All forecasts are statistical estimates, not guarantees.
What if I don’t understand something?
You can message us. Every purchase includes free support.
Whether it’s a setup issue, RealTest usage, or logic clarification, we’ll walk you through it. You’re not left alone.
Do you offer a refund?
Yes.We provide a 30-day money-back guarantee for your peace of mind. If the strategy logic or performance metrics differ from what is shown on our website, or if there are coding errors or issues, we will offer a full refund.
How often do you update performance results?
It can vary, usually not longer than two months.
Can I get help customizing or combining strategies?
Yes. We offer services to custom code, fix, or combine strategies into one RealTest portfolio or even prep them for live trading. Check our Service pages.
About Mean Reversion Trading
What is a mean reversion strategy in trading?
A mean reversion strategy is a quantitative approach based on the idea that asset prices tend to move back toward their long-term average after extreme highs or lows. Traders use statistical tools like moving averages, Bollinger Bands, and RSI to identify when a market is overbought or oversold, then position for a reversion to the mean.
How does mean reversion trading work?
- Define the mean – often a moving average.
- Detect deviations – look for price moves far above (overbought) or below (oversold) the mean.
- Confirm signals – use indicators like RSI or Bollinger Bands.
- Trade – buy when price is well below the mean, sell when it’s above.
- Risk management – apply stop-losses to avoid being trapped in strong trends
When does mean reversion work best?
Mean reversion performs best in range-bound markets where prices oscillate within a stable band. It is less effective during strong trending markets where prices may stay above or below the mean for extended periods.
Is mean reversion profitable?
Yes is highly profitable, but profitability depends on robust testing and risk controls. Poorly designed mean reversion systems can suffer large losses in trending markets. Successful strategies rely on backtesting, walk-forward testing, and risk management to ensure robustness. Discover our robust startegies - https://setupalpha.com/collections/realtest-strategies-and-backtests
How do professional traders use mean reversion?
Professional quants often:
- Combine mean reversion with trend-following to diversify risk.
- Use pairs trading where two correlated assets revert to their historical spread.
- Run multi-strategy portfolios to smooth out drawdowns.
What asset classes are suitable for mean reversion strategies?
Mean reversion strategies are versatile and can be applied to various asset classes that exhibit range-bound behavior. Assets well-suited for these strategies include:
- Stocks
- Exchange-Traded Funds (ETFs)
- Forex (currency pairs)
- Commodities
- Fixed income instruments
- Crypto
Why is mean reversion important in trading?
Mean reversion is a significant concept in trading because it provides a structured framework for identifying and acting on market opportunities. Its importance can be summarized by the following points:
- Exploiting Market Inefficiencies: It allows traders to capitalize on temporary mispricings that occur when markets overreact to news or events.
- Risk Management: By identifying overbought and oversold conditions, it helps traders avoid entering positions at unsustainable levels and set more effective stop-loss orders.
- Versatility Across Assets: The strategy is applicable to a wide range of asset classes, including stocks, commodities, currencies, and bonds.
- Foundation for Quantitative Strategies: It is the underlying principle for many quantitative strategies, such as statistical arbitrage and pairs trading.
- Diversification Benefits: It can complement trend-following strategies, as it tends to perform well in range-bound markets where trends are absent.
How does mean reversion work in RealTest (MHPTrading software)?
RealTest, developed by MHPTrading (Marsten Parker), allows traders to:
- Backtest mean reversion strategies across thousands of stocks.
- Use portfolio-level simulations with realistic trading costs.
- Avoid survivorship bias and lookahead errors. (By using Norgate Data)
- Combine mean reversion with other models like trend-following or rotation strategies.
Why do quants use RealTest for mean reversion?
- Ease of scripting – strategies can be written in rule-based logic without heavy coding.
- Speed – RealTest handles multi-strategy backtests much faster than Python/Pandas.
- Transparency – detailed trade lists, order generation, and slippage modeling.
- Integration – works with Norgate Data for high-quality historical datasets