Trading With Data, Not Your Emotions

Emotions have no place in successful futures trading. In volatile markets, decisions driven by fear, greed, or bias can lead to costly mistakes. The key to consistently making smart trades? Using data-driven analysis instead of gut feelings.

Edgeful LLC CEO Andre Arslanian discussed why this principle is at the heart of professional trading and how you can incorporate it for smarter trading. He provided a walkthrough of edgeful.com—a data analysis app for day traders—to demonstrate how it can help traders make trades with data and not their emotions.

The Power of Data in Trading

 

Andre, a seasoned trader with experience at top financial institutions like Goldman Sachs and Weiss Asset Management, discovered early on that trading without data was like navigating in the dark. When he transitioned to trading his own capital, he struggled with forming a daily bias—should he be bullish or bearish? Like many traders, he initially relied on intuition but quickly realized that success required a more structured approach.

One key insight he developed was the concept of gap fills—understanding how often price gaps between sessions close. While many traders assume gaps always fill, Andre put this idea to the test by analyzing historical market data. He compiled spreadsheets, tested different timeframes, and identified probabilities that allowed him to trade with confidence. Eventually, he built a platform designed to automate this type of analysis for traders, removing the guesswork from their strategies.

Key Trading Metrics to Track

 

1. Initial Balance Breakout

 

The high and low of the first hour of trading—the initial balance (IB)—often dictates market direction for the day. In a six-month analysis of the S&P 500 futures (ES), data revealed that 75% of the time, price only broke out in one direction—meaning traders could structure trades with a clear bias rather than expecting whipsaw movements in both directions.

2. Opening Candle Continuation

 

Another powerful statistic showed that if the first hour of trading closes green, the day’s closing price is also green 73% of the time in ES and 82% in NQ. Conversely, when the first hour closes red, the probability of a red close increases. This type of data helps traders confirm their bias early in the session.

3. Gap Fill Probability

 

Contrary to common belief, gaps don’t always fill immediately. Historical analysis showed that in some market conditions, gaps filled at a higher rate, while in others, they remained open for longer periods. By studying historical probabilities, traders can avoid blindly chasing gap fills and instead enter trades with statistical backing.

4. Volume and Range by Weekday

 

Not all trading days behave the same way. Mondays, for instance, often have lower volume and tighter ranges, making them prone to choppier price action. Meanwhile, midweek sessions tend to see higher volatility, offering better opportunities for momentum trades. Recognizing these patterns helps traders set realistic expectations for each trading session.

Adapting to Changing Market Conditions

 

One of the biggest takeaways from the event was the importance of adjusting to evolving market conditions. Data from Q1 2024 showed that 80% of opening range breakouts resulted in both sides being breached—meaning traders had to be cautious of false breakouts. However, by Q4 2024, that number dropped to 50%, signaling a shift toward trend continuation setups. Traders who relied on gut instincts would have struggled, while those who adapted to the changing data stayed ahead.

Trade Smarter, Not Harder

 

Successful traders don’t rely on intuition—they let the numbers guide them. Platforms like NinjaTrader provide advanced charting, backtesting, and real-time data analysis, enabling traders to base their decisions on hard facts instead of emotions.

By integrating data-driven insights into your trading, you can remove uncertainty, manage risk effectively, and increase your chances of success in the futures markets. Don’t trade on feelings—trade with data.

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