What makes a trading strategy good?
I analyzed over 100,000 different backtests.
No, that is not an exaggeration.
Over 5 years ago, I started building what would later become NexusTrade, a no-code algorithmic trading platform. Since my official launch on January 31st 2023, NexusTrade users have launched exactly 114,549 backtests.
Press enter or click to view image in full size A screenshot from MongoDB Compass that shows the number of backtests in the database and the date of the first backtestUsing natural language processing and data science techniques I analyzed the 100K+ backtests for patterns. The question I asked myself was simple.
What makes a trading strategy good? What makes a trading strategy bad?
After sifting through the noise, a clear pattern emerged. The difference between success and failure isn’t a secret indicator; it’s a fundamental approach to design. So, what’s the answer in a single sentence?
A good trading strategy executes a simple, logical, and complete plan tailored to a specific asset’s behavior, whereas a bad strategy is an over-complex, illogical, or incomplete set of rules that mistakes historical noise for a true market edge.
Let’s break that down. The data tells a compelling story, best understood by looking at what doesn’t work first.
Full Disclosure: I’m the founder of NexusTrade, the platform that generated this data. This analysis uses real user strategies from our production database. All code and methodology are open-source.
The Hallmarks of a Bad Strategy
The worst-performing strategies weren’t unlucky; they were fundamentally flawed. They consistently shared these traits:
- Illogical or Impossible Rules: Many failing strategies contained conditions that could never be met, like Buy “when 0 < 0”, or conditions that were always true, like “Sell always”. These are not strategies; they are programming errors that guarantee failure through either inaction or excessive trading costs.
- The “Bag Holder” Problem: A huge number of bad strategies had rules to buy but no clear rules to sell. A “buy the dip” strategy without a profit-taking or stop-loss rule is just a plan to accumulate a depreciating asset. A strategy must be a complete plan for a trade’s entire lifecycle.
- Over-Complexity and Contradiction: The most convoluted strategies were often the worst. One strategy had 13 different rules mixing dollar-cost averaging, percentage-based buys, and conflicting moving averages. This “kitchen sink” approach is a classic sign of overfitting to historical noise and is almost guaranteed to fail in live markets.
The Anatomy of a Good Strategy
In stark contrast, the elite and high sharpe strategies were built on clear, logical, and often simple principles that were context-aware.
- Context is King: Momentum vs. Mean Reversion: The data showed a powerful pattern: momentum strategies work best on individual growth stocks, while mean-reversion strategies work best on broad market indexes.
- For example, a top condition was GOOG Price > 20 Day GOOG Bollinger Band and 15 Day GOOG RSI > 70 — a classic momentum breakout signal on a specific stock. Conversely, another top condition was 30 Day SPY Rate of Change ≤ -5 — a classic “buy the dip” signal on the entire market. Good strategies don’t apply one logic to everything; they match the logic to the asset’s behavior.
- Simplicity and a Clear Thesis: The best conditions were easy to understand. They represented a clear market thesis, like “buy when the market is fearful” or “join a strong trend.” This simplicity makes them more robust and less likely to be overfitted.
- Intelligent Risk Management: Some of the smartest conditions weren’t just about buying dips; they were about buying dips in an established uptrend. An elite-performing condition was SPY Price > 200 Day SPY SMA and 30 Day SPY Rate of Change ≤ -5. This logic is brilliant in its simplicity: it waits for the market (SPY) to pull back significantly (≤ -5%), but will only consider this a buying opportunity if the overall market is still in a long-term uptrend (above its 200-day moving average). This rule systematically avoids catching a falling knife at the start of a new bear market, dramatically improving risk-adjusted returns by filtering out the most dangerous drawdowns.
My Methodology: From 100K Backtests to Actionable Insights
Data Pipeline:
- Population: 114,549 backtests run between Jan 31st, 2023 — Nov 3rd, 2025
- Tested Against: ~20 years of historical market data (primarily 2004–2025). Includes: 2008 Financial Crisis, 2010s Bull Market, 2020 COVID Crash, 2021–2022 Bear Market, 2023–2024 Recovery
- Normalization: Intelligent number rounding to group similar strategies (e.g., “RSI > 69.8” and “RSI > 70.1” → “RSI > 70”)
- Metrics: Annualized returns, Sharpe ratio, Calmar ratio (risk-adjusted)
- Diversity Selection: TF-IDF + cosine similarity to prevent showing 100 variations of the same RSI strategy
- Corpuses: Top 1% (elite), Top 20% (high performers), Bottom 10% (terrible)
Statistical Rigor:
- Used median (not mean) to avoid outlier bias.
- Filtered infinite/invalid values (e.g., Calmar > 1000).
- Minimum 5 backtest runs per component for statistical validity
View full analysis code →
How Confident Am I?
The patterns are statistically robust:
- Elite strategies (top 1%): Median Sharpe 3.2 vs. 0.8 overall (4x better)
- Terrible strategies (bottom 10%): Median Sharpe -0.4 (consistently lose money)
- Sample size: Elite conclusions based on 1,145+ unique strategy components
- Consistency: 73% of “elite” components appear in multiple high-Sharpe backtests
Translation: These aren’t flukes. The patterns are real.
What This Analysis Doesn’t Tell You
Be skeptical. This analysis has limitations:
- Survivorship Bias: Only analyzed completed backtests (users may delete failures)
- Data Quality: Pre-2004 market data is limited/lower quality in our database
- Recency Bias: Most users test recent 5–10 year periods, not the full 20 years available
- Market Evolution: Strategies that worked 2004–2008 may not work in modern algorithmic markets
- Execution Gap: Backtest results ≠ live trading (slippage, fees, psychological factors)
Bottom line: These patterns are strong directional indicators, not guarantees.
Conclusion: Your Three Takeaways
After analyzing over 100,000 backtests, the conclusion is clear: the secret to a good trading strategy isn’t a magic indicator, but a disciplined approach.
- Simplicity Over Complexity: A clear, simple plan based on a logical market thesis will almost always outperform a complex strategy that tries to account for every variable.
- Context is Non-Negotiable: The best strategies are tailored to the specific behavior of the asset being traded. Don’t apply a stock strategy to a market index and expect it to work.
- A Strategy Must Be a Complete Plan: Every entry rule must be paired with a well-defined exit plan for both taking profits and cutting losses.
Take Action
Want to test these principles? Three options:
- DIY: Use the analysis code (Python/Polars) with your own backtest data
- Study Further: Research academic papers on momentum vs mean-reversion strategies
- Quick Start: Try the no-code platform that generated this dataset (NexusTrade)
All analysis code is open-source and available on GitHub.
