When looking at RA Algos on AlgoTest, one of the most visible metrics you’ll come across is the win rate. It’s easy to assume that a higher win rate means a better algo, but that’s not always the full picture. An algo with a 50% win rate can be highly profitable — and sometimes even more efficient than one with 70%+.
In this guide, we’ll break down what win rate means, how it's calculated, and why it's only part of the story. We’ll also show you how to analyze and filter RA Algos based on win rate using AlgoTest’s built-in tools.
What is the Win Rate in Trading?
Win rate is the percentage of trades that end in profit compared to the total number of trades taken by the algo.
Formula:
Win Rate (%) = (Number of Winning Trades / Total Trades) × 100
Example:
If an algo takes 100 trades and 55 of them are profitable, its win rate is:
(55 / 100) × 100 = 55%
This metric helps users understand how frequently an algo "wins" in the market.
Is a Higher Win Rate Always Better?
Not necessarily.
A higher win rate doesn’t always mean higher profits. What really matters is the risk-reward ratio—how much you gain when you're right versus how much you lose when you're wrong.
Let’s compare two algos:
Algo A
- Win Rate: 70%
- Average Profit per Win: ₹50
- Average Loss per Loss: ₹100
- Out of 100 trades:
• 70 wins → ₹50 × 70 = ₹3,500
• 30 losses → ₹100 × 30 = ₹3,000 - Net Profit = ₹500
Algo B
- Win Rate: 50%
- Average Profit per Win: ₹200
- Average Loss per Loss: ₹100
- Out of 100 trades:
• 50 wins → ₹200 × 50 = ₹10,000
• 50 losses → ₹100 × 50 = ₹5,000 - Net Profit = ₹5,000
Summary:
Algo B, despite having a lower win rate, ends up being more profitable because it makes more on its winning trades than it loses on losing ones. This is known as having a positive expectancy.
Many professional algos have win rates between 40% and 60%, but compensate with strong reward-to-risk setups.
Role of Risk-Reward Ratio
Risk-Reward Ratio (RRR) measures how much an algo aims to make on a winning trade versus how much it risks losing.
Formula:
RRR = Average Profit per Win / Average Loss per Loss
Example:
If an algo risks ₹500 to make ₹1,000, its RRR is 2:1.
Even if such an algo wins only 40% of the time, it can still be profitable:
- Win: 4 out of 10 trades ✓ ₹1,000 = ₹4,000
- Loss: 6 out of 10 trades ✗ ₹500 = ₹3,000
- Net = ₹1,000 profit over 10 trades
This is why context matters more than just the win rate.
How AlgoTest Displays Win Rate & Other Metrics
When you browse RA Algos on AlgoTest, each algo card includes key performance indicators:
- Win Rate (%): Total winning trades divided by total trades
- Max Drawdown: The largest capital dip from peak to trough
- Margin Required: Clearly mentioned upfront
- MTM, Expectancy Ratio, and Number of Trades: Gives a fuller performance view
These are based on backtested data with customizable options to add charges, slippages and brokerages.
You can click on any algo to view detailed charts and trade-wise performance, including how the win rate evolved over time.
Should I Avoid Low Win Rate Algos?
Not at all. Some of the most robust and sustainable algos operate with 45–50% win rates but have:
- High reward-to-risk ratios
- Fewer trades but higher average profit per trade
- Better control of downside (lower drawdown)
Focus on overall consistency, not just win accuracy.
Key Questions to Ask:
- What’s the average gain vs. average loss?
- How often does the algo trade?
- Is the drawdown within my comfort zone?
- Does the strategy fit my capital and lifestyle?
Final Thoughts
Win rate is a helpful number, but it’s just one part of a larger picture. Don’t let a 50% win rate scare you off, if the risk-reward ratio is solid, that could be the foundation of a highly profitable system.
AlgoTest makes this evaluation easier by:
- Displaying win rate transparently
- Providing full backtest context including slippage and costs
Take your time to explore different ranges, forward test when in doubt, and choose algos that align with your personal trading goals.
When in doubt, dig deeper than the headline percentage.
