TLDR

• Even coin-flip strategies can uncover strong market bias.

• The S&P 500 drifts higher, and random long-only systems make money.

• Natural Gas drifts lower, random short-only systems make money.

• The Australian Dollar is neutral, showing little drift either way.

• The lesson: align strategy direction with a market’s natural tendency.

 Introduction: Can Randomness Beat the Market?

Traders spend years searching for the perfect indicator. I did too.
I kept trying to force strategies to work both long and short, convinced that the holy grail was balance. But years later, one experiment with random strategies made me stop in my tracks: sometimes, the market itself gives you the edge.

Most traders obsess over indicators, but the real edge may already be coded into the market itself, its natural bias.

You don’t need to be born with natural intuition to detect market bias, as I explain in Natural Talent vs. Skill in Trading, trading is more about learned competence than innate gift.

What if I told you that simply flipping a coin could make you money, but only if you flip in the right direction for the right market?

This post walks you through the results of running 300,000 random trading strategies on three futures markets: the S&P 500 (ES), Natural Gas (NG), and the Australian Dollar (AD).

This random backtest analysis enables traders to identify market drift and structural bias across futures and forex markets.

🎥 Watch the Full Experiment in Action:

• Can a Coin Flip Beat the Market? – Step-by-step setup and randomization logic.

• What Random Strategies Reveal About Market Bias – Visual breakdown of drift across ES, NG, and AD.

• 
  

Methodology: The Coin Flip Test

To isolate market tendencies, I removed every form of optimization and let randomness do the work. Here’s how I set it up:

• Markets: ES, NG, AD, continuous back-adjusted futures contracts.

• Timeframe: 2006–2025 daily bars (RTH for ES, 24h for NG, full 24h for AD).

• Simulation size: 100,000 random strategies per market, 50K long and 50K short.

• Position sizing: Always 1 contract, no compounding.

• Entries: Flip a biased coin, heads = take trade, tails = skip. Weighting applied (e.g., 20% coin weight = ~80/20 trade/no trade).

• Exits: Fixed exit bars (time-stop hold length).

• Wait bars: Minimum cooldown before next flip.

• Rules: One position at a time, no same-bar re-entry.

• Costs: No commissions or slippage, drift only.

This isn’t about curve-fitting. It’s about stripping trading down to randomness to see if the market itself reveals a bias.

Results: S&P 500’s Long Bias

The S&P 500 is famous for its upward drift, and the random test confirmed it.

Seeing almost every random long strategy end up profitable was the moment I realized bias isn’t theory, it’s baked into the data

Long vs Short Performance

Bar chart showing average net profit for 100K ES strategies. Longs average strong positive profits, shorts show losses.

• Long random strategies: out of 50,000 strategies, %97.2 are profitable, with an average Net Profit of +$122,000

• Short random strategies: out of 50,000 strategies, only %3.4 are profitable, with an average Net Profit of +$18,000

  
  

“The trick isn’t finding magic indicators, it’s aligning your strategy with the market’s bias.” - Ali Casey

Impact of Coin Weight

Bar chart of ES long random strategies showing net profit increasing with higher coin weights (more frequent trades).

The more frequently the coin allowed trades, the higher the net profit. The drift is so strong that more exposure equals more profit.

Coin weight of 20, means 80% chance of flipping heads, (trade ON).

Exit and Wait Bars

Bar chart of ES long random strategies showing consistent profits across exit/wait bar settings.

Different holding lengths and cooldowns didn’t erase the bias. Almost any configuration on the long side showed profits.

Distribution of Profit Factor

Histogram showing most ES random strategies cluster around profit factor 1.1–1.3.

Most strategies cluster around PF 1.1–1.3. Even randomness has an edge, if you’re long.

Average net profit of 50,000 Long strategies is +$120,000, while the average net profit of 50,000 Short strategies is -$117,000

Results: Natural Gas’s Short Bias

Natural Gas is volatile, but randomness showed its true character: short-only drift.

Long vs Short Performance

Bar chart showing NG short strategies with strong average profits, while long strategies lose money.

• Long random strategies: out of 50,000 strategies, only %3.9 are profitable, with an average Net Profit of +$17,000

• Short random strategies: out of 50,000 strategies, %96.8 are profitable, with an average Net Profit of +$110,000

“Some markets are insanely profitable on one side only.” - Ali Casey

Coin Weight, Exit, and Wait Bars

The more short trades you took, the more money you made. Exit and wait bars didn’t change the outcome.

Distribution of Profit Factor

Histogram showing NG random short strategies cluster around PF 1.2–1.4.

Just like ES on the long side, NG shows consistent drift, only on the Short side.

Results: Australian Dollar’s Neutral Drift

To contrast, I ran the same test on the Australian Dollar.

Long vs Short Performance

Bar chart showing AD random long and short strategies close to break-even with no clear bias.

Here’s the surprise: no clear edge either way. Long and short strategies average net profit hovered around flat results, even though win rate is totally different.

Distribution of Profit Factor

Histogram showing AD random Long & Short strategies cluster around PF 0.9-1.1.

The AD acts as a neutral benchmark, proving that the biases seen in ES and NG aren’t universal.

Comparative Distribution: Bias in Context

To visualize:

Side-by-side comparison table showing ES long bias, NG short bias, and AD neutral distribution with average profit metrics.

• ES: Skewed to profitable longs.

• NG: Skewed to profitable shorts.

• AD: Balanced between long and short

• Also notice how NG makes more from the average daily move than the ES, and 9 times more than AD

Lessons Learned

1. Markets have natural tendencies. The ES wants to go up. NG wants to go down.

2. Random strategies reveal drift. If random systems make money, the edge is in the market itself.

3. Don’t force symmetry. I wasted years trying to build equal long and short strategies. The truth? Some markets only reward one side.

4. Test for drift. Before building a strategy, run a simple randomization test.

“Markets have a natural drift. Randomness reveals it.” - Ali Casey

 I break down this process step-by-step inside the Algo Trading Masterclass. You’ll learn how to test markets for drift, measure robustness, and align every strategy with its natural bias.

Conclusion

If random coin flips can make money in the right direction, imagine what happens when you add real structure.

Don’t waste years forcing markets into symmetry. Instead, lean into their bias. Market bias isn’t luck, it’s structure. Once you find it, you can design strategies that move with the market, not against it.

If today you take only one lesson home: markets bias behavior matters, and you shouldn’t bet everything on a single system. For a deeper dive into why having multiple systems matters, see The #1 Lesson Traders Learn Too Late: No Single System Will Save You on our blog.

Trade ES with long strategies. Trade NG with short strategies. Use neutral markets like AD to understand when no directional edge exists.

I take this even further in the Algo Trading Masterclass, where I show that every strategy style (mean reversion vs breakout) show their heads when tested. For example, ES works very well with mean reversion and not with breakout even when using its high edge in long only strategies.

FAQ

Below are common questions traders ask about market bias and randomness testing.

🧠 1. What is market bias in trading?

Market bias refers to the natural directional tendency of a market, for example, the S&P 500’s long-term upward drift or Natural Gas’s tendency to trend lower. Recognizing this helps traders align their strategies with the underlying flow instead of fighting it.

🎲 2. How can random strategies reveal market bias?

By removing all indicators and using random trade entries, you can observe whether a market tends to rise or fall over time. If random long trades consistently profit, the market has an upward bias; if short trades do, it has a downward bias.

📈 3. What markets show the strongest directional drift?

In long-term backtests, equity indexes like the S&P 500 typically show strong bullish drift, while commodities such as Natural Gas often show bearish drift. Currencies like the Australian Dollar are usually more neutral.

🧪 4. How do you test for market bias using random strategies?

Run thousands of random strategies on historical data, half long and half short, with fixed exits and no compounding. Compare the average net profit of both sides. The side that consistently wins reveals the market’s bias.

💡 5. Why is it important to align strategy direction with market bias?

Trading against bias increases drawdowns and reduces the probability of success. Aligning with bias (e.g., using long-only systems on upward-drifting markets) allows even simple or noisy strategies to perform better.

⚙️ 6. How many random strategies are enough to detect bias?

Testing at least 10,000–100,000 random strategies per market produces statistically reliable results. Larger sample sizes ensure that the outcomes reflect true market drift, not randomness.

⏱️ 7. Does market bias change over time?

Yes. Bias can shift during macroeconomic cycles, volatility regimes, or structural changes (such as energy supply shocks). Regularly re-testing for drift keeps your strategy direction aligned with the current market phase.

🧭 8. What’s the difference between market bias and strategy edge?

Bias comes from the market’s structure, it exists even without an indicator. Edge comes from your strategy design, rules, filters, and timing. The best systems combine both: exploiting bias with a structured edge.

🪙 9. Can a coin flip really beat the market?

A pure coin flip can’t produce alpha, but when flipped in the direction of a market’s natural bias, it can outperform random expectations. The test isn’t about luck, it’s about exposing underlying drift.

🎓 10. How can traders use market bias in portfolio design?

You can build a diversified portfolio by grouping markets based on bias: long-biased equities, short-biased commodities, and neutral currencies. Combining uncorrelated biases improves overall return-to-drawdown ratios.