What makes a trading strategy good?

Part 1: The Answer

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.

Using 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.