Ask traders what they want from a strategy and one answer keeps coming up: a higher win rate. More winning trades, better accuracy, fewer stop-losses. It sounds obvious. If a trader is right eight times out of ten, surely that should mean a lot of money.
Not necessarily.
A trader can be right 8 out of 10 times and still lose money. Another can be wrong on six of ten and still come out profitable. Win rate is only one part of the calculation; what also matters is how much you make when you are right and how much you lose when you are wrong.
The Formula That Decides Everything
Every trading system comes down to one equation:
Expectancy = (Win Rate x Average Win) – (Loss Rate x Average Loss)
If expectancy is positive, the system makes money over enough trades. If it is negative, a high win rate cannot save it.
Trader A wins 8 out of 10 trades. His winners are quick and small, averaging Rs 1,000 each. His losers are a different story. Some he cuts, but on others he holds on, hopes for a recovery, averages down, and exits deep in the red. Across his losing trades, the average loss works out to Rs 5,000.
Over a typical set of 10 trades: (8 x 1,000) minus (2 x 5,000) = Rs 8,000 minus Rs 10,000 = minus Rs 2,000.
An 80% win rate, and the account still bleeds every cycle. His win rate is real. So is his negative expectancy, and over enough trades, expectancy always wins the argument.
Trader B wins only 4 out of 10 trades. But he lets his winners run, so his average winning trade makes Rs 3,000, and he cuts every loser near a predefined stop, keeping his average loss around Rs 1,000.
Over a typical set of 10 trades: (4 x 3,000) minus (6 x 1,000) = Rs 12,000 minus Rs 6,000 = plus Rs 6,000.
Trader B is wrong more often than he is right, and he is the one making money.
Why Your Brain Fights This
If the maths is this simple, why do most traders chase accuracy? Because winning feels good. Taking a stop-loss is a hit on your ego. So traders judge themselves by how often they are right.
That leads to exactly the wrong behaviour. The trader books small profits quickly, not wanting them to disappear, and gives losing trades more time, because booking the loss would mean admitting the trade was wrong. The result can be a long list of small winners and a few losses large enough to wipe them out.
This is also why some option-selling strategies can look excellent for a long time. Selling far out-of-the-money options can produce many small winning trades and a strike rate that looks great. Then one large market move can take back months of those gains, sometimes more. The high accuracy was real. It never told the full story.
Swing trading often works differently. A trader may take several small stop-losses before catching a stock that moves 30%, 50% or more. The aim is not to be right on every trade. It is to keep the cost of being wrong small enough that the bigger winners pay for those losses. Small stop-losses are the premiums paid to stay in the game.
Why Large Losses Hurt More Than They Look
A 10% drawdown needs an 11.1% gain to recover.
A 25% drawdown needs roughly 33%.
A 50% drawdown needs a 100% gain just to get back to the start.
Losses and recoveries are not equal: as a drawdown gets deeper, the return required to recover rises faster than the drawdown itself. Controlling the average loss changes the maths of the whole system, because money that is not lost does not have to be earned back later.
The same thinking matters during losing streaks. A profitable system with a 40% win rate can still hit six, seven or eight losses in a row; that alone does not mean it has stopped working. Position sizing decides the damage: risk a large part of the account on every trade and a normal streak hurts badly; risk, say, 0.5% of capital per trade and the same streak is survivable.
Accuracy Is Easy to Sell
Seen this way, stop-losses look different. A losing trade is no longer automatically a bad trade: if the loss was planned, sized correctly and kept within the system’s rules, it is part of the cost of running the strategy. The same applies to winners. Being right is not enough if the trader books every winner for a tiny profit while letting the losers grow.
This idea sits at the centre of Smart Money Club’s framework. The Surat-based SEBI-registered Research Analyst firm (INH000028352), founded by CA Harshitha Iyer and Pranjal Rastogi, filters thousands of stocks to a small set of swing and positional setups, but gives risk-reward, position sizing and trade management the same weight as stock selection. Finding a good stock is one part of the job; knowing how much to risk, when to exit and how to handle a trade that goes wrong is the rest.
A high strike rate sounds attractive immediately. Expectancy is less exciting, but it forces the trader to judge the full set of trades rather than each green or red exit.
A useful exercise: take the last 20 trades and work out the win rate, the average profit on winners and the average loss on losers, then apply the expectancy formula. That calculation tells a trader more than knowing how many trades were right.
Smart Money Club is a SEBI-registered Research Analyst (Registration No. INH000028352). Investment in securities markets is subject to market risks. Read all related documents carefully before investing. Registration granted by SEBI does not guarantee performance or returns. This article is for educational purposes only.
