In Trading, Positive Expectancy Matters More Than Accuracy

One of the most common mistakes in trading is judging a trader, Research Analyst or trading system solely by its accuracy or success ratio. People often ask: "How many trades were correct?" "What is the accuracy?" But real trading mathematics is much more than that. The profitability of a trading system does not depend only on how many trades are right. The more important question is: How much was made on the winning trades, and how much was lost on the losing trades? That is where Positive Expectancy becomes important. What Matters More Than Accuracy? A profitable trading system depends on several factors, including: * Risk-Reward Ratio * Trade Structure * Position Sizing * Average Profit * Average Loss * Drawdown * Loss Management * Profit Management * Consistency * Net Returns Suppose a trading system has only 36% accuracy. That means only 36 out of 100 trades were profitable. At first glance, that number may not look impressive. However, if the average profit generated by those winning trades is sufficiently larger than the average loss on the losing trades, the overall system can still produce a positive net result. This is why judging a trading system only by accuracy can be misleading. An Example from My Verified Performance According to my reported verified performance under the SEBI PaRRVA framework: NIFTY Futures — approximately 89% Success Ratio Whereas: Index Options — approximately 36% Success Ratio Despite the lower success ratio in Index Options, the overall net result has remained positive. This is a simple demonstration that: Accuracy and profitability are two different concepts. Understanding the Net Result A simplified mathematical formula is: Net Result = Total Profit from Winning Trades − Total Loss from Losing Trades For example, suppose there are 10 trades: * 4 winning trades generate ₹10,000 each * 6 losing trades lose ₹4,000 each Then: Total Profit = ₹40,000 Total Loss = ₹24,000 Net Result = ₹16,000 Profit The accuracy is only 40%, yet the net result is positive. Therefore, the statement: "Higher accuracy automatically means higher profitability" is not mathematically correct. Even High Accuracy Is Not Enough Now consider a system with 80% accuracy. Out of 100 trades: * 80 trades are profitable * 20 trades are losing trades That sounds excellent. But if the average profit on the 80 winning trades is very small while the average loss on the 20 losing trades is very large, the overall result can still be negative. Therefore, when analysing trading performance, we should not look only at the Win Rate. We should also examine: What is the Average Win? What is the Average Loss? What is the Risk-Reward Ratio? What is the Maximum Drawdown? What are the Net Returns? What is the Position Sizing? And most importantly: How is the overall trade structure designed? Don't Compare Performance on Accuracy Alone If you are comparing Research Analysts, traders or trading systems, accuracy alone is an incomplete metric. Where verified data is available, you should consider: 📌 Net P&L / Returns 📌 Average Profit 📌 Average Loss 📌 Risk-Reward 📌 Drawdown 📌 Number of Trades 📌 Success Ratio 📌 Consistency 📌 Relevant Time Period 📌 Segment-wise Performance This provides a much more complete picture of performance. That is why I have always maintained: Accuracy is one metric, but Net Result is a more meaningful outcome. Review the PaRRVA Verified Performance Yourself For investors and clients, one of the most important aspects of performance reporting is transparency and verified data. My performance is reported under the PaRRVA framework, and the relevant verified reports can be reviewed through the Reports section of CarePaRRVA. Therefore, rather than forming an opinion based only on social media claims or marketing statements, investors can review the available verified performance data themselves. You can examine different participants and analyse: Net Results + Returns + Risk + Drawdown + Trade Sample Size rather than looking at accuracy alone. Trading Is Not an Accuracy Competition Treating trading like a school examination is a fundamental mistake. If a student gets 90% of the answers correct, higher marks generally follow. But in trading, 90% winning trades do not automatically mean that the system will generate the highest returns. One large loss can wipe out many small profits. Similarly, a few large profitable trades can compensate for multiple small losses. That is why trading mathematics works differently. Probability + Risk Management + Risk-Reward + Trade Structure + Position Sizing + Execution = Trading Outcome This is why my focus is not simply on accuracy. My focus is on: Positive Expectancy. One More Important Point For some segments, complete historical trade data could not be captured in PaRRVA due to technical issues. Therefore, the currently available data for those segments should not be treated as a complete representation of the entire historical performance, and definitive conclusions should not be drawn from incomplete data. I would personally prefer to wait for a sufficiently large sample size. In particular: 🔹 At least 500 trades in Equity Options and Equity Futures And for other segments: 🔹 Around 1,000 trades or another sufficiently large sample size Once a larger number of verified trades becomes available, performance can be assessed more meaningfully. As the verified dataset grows, we can evaluate: * Accuracy * Average Profit * Average Loss * Risk-Reward * Drawdown * Net Returns * Consistency * Segment-wise Performance A trading system should not be conclusively judged on the basis of a small number of trades or a short observation period. The larger and more consistent the verified dataset, the more robust the performance assessment can become. Therefore, keep reviewing the PaRRVA Reports as additional verified trades are added. More verified data → Better assessment. My final point is simple: Do not look only at how many trades were correct. Look at how much the winning trades made, how much the losing trades lost, and what the final Net Result was after considering both. And if the mathematics above seems difficult, just ask. There is nothing wrong with being weak in mathematics. The real problem is making trading decisions based on the wrong metric. 🙂 For educational/informational purposes only. Past performance does not guarantee future results. Please conduct your own independent due diligence and risk assessment before making any investment or trading decision. — Varun Bhargav SEBI Registered Research Analyst