What Is a Good Sharpe Ratio for a Trading Strategy
The standard answer is "above 1.0." The real answer depends on how many trades you ran, whether the data was optimized, and just how much you can trust that TradingView backtest.
What the Sharpe ratio actually measures
The Sharpe ratio tells you how much return you get for each unit of risk you take. Risk here is measured as standard deviation of returns, which means it penalizes both good volatility (big wins) and bad volatility (big losses) equally.
The formula is simple. Take your average return, subtract the risk-free rate (usually the 10-year Treasury or a fixed 2 to 5 percent), and divide by the standard deviation of those returns. What comes out is a single number. Below 0.5 means the strategy is barely compensating you for the risk. Above 1.0 starts to look interesting. Above 2.0 is exceptional.
But those thresholds come from academic finance, where they are calculated on monthly or daily returns of diversified portfolios over decades. A retail TradingView backtest on 200 trades over 3 years is a completely different animal. The rules are not the same, and pretending they are will cost you money.
When I first started testing strategies seriously, I had a mean reversion setup on EURUSD that showed a Sharpe of 2.3 over 4 years of backtest data. I was already mentally spending the profits. Three months of forward testing gave me a Sharpe of 0.4. The backtest was not wrong in the technical sense. It was wrong in the sense that it measured past data perfectly and future data not at all.
Sharpe ratio bands for retail backtests
Here is the scale I use for TradingView backtests. These are not academic thresholds. They come from running hundreds of strategies through validation and watching which ones survive live trading.
- Below 0.5 - The strategy is not compensating for risk. Skip it unless there is a very specific reason to keep testing.
- 0.5 to 1.0 - Average for a retail strategy that might hold up live. Worth investigating further but not worth betting on yet.
- 1.0 to 1.5 - Promising. Many strategies that survive forward testing fall in this range. The higher end needs more trades to be trustworthy.
- 1.5 to 2.0 - Strong. Start taking this strategy seriously, but run Monte Carlo and walk-forward before committing capital.
- 2.0 to 3.0 - Suspicious. Verify the trade count. Anything above 2.5 with fewer than 150 trades is almost certainly overfitted.
- Above 3.0 - Treat as an error unless you have over 500 trades and out-of-sample validation. I have never seen a retail strategy with a backtest Sharpe above 3.0 that held up in live trading.
The bands shift lower if you optimized parameters. Every optimization pass inflates the backtest Sharpe by roughly 0.3 to 0.5 depending on how many parameter combinations you tried.
I once tested a breakout system on NQ futures that showed a 2.7 Sharpe after optimization. I put it through our backtest report analyzer, and the Monte Carlo simulation revealed that 38 percent of the 1000 randomized runs produced negative returns. The optimization had found a specific sequence of winning trades that looked like a pattern but was just noise.
Why TradingView inflates your Sharpe ratio
TradingView calculates Sharpe ratio from bar-level data, not trade-level data. This matters because bars include all the tiny intraday fluctuations that make volatility look higher than it actually is in your trade sequence. Higher volatility in the denominator means the Sharpe ratio gets compressed.
But that is not the main inflation mechanism. The real issue is optimization. TradingView makes it trivially easy to run optimizations across dozens of parameters, and each run surfaces a combination that fits the historical data better than it should.
I see this pattern constantly. A trader posts a screenshot of a 2.8 Sharpe in a forum, asks if the strategy is ready for live funding, and has no idea that the metric is inflated by 40 to 60 percent relative to what a proper trade-level calculation would show. Running your CSV through a tool that recalculates from raw trade data gives you the real number.
Ready to check your actual Sharpe ratio?
Analyze Your Backtest NowKey caveats for backtest Sharpe ratios
Annualization matters. Most Sharpe calculations annualize by multiplying by the square root of the number of periods. If you trade daily, annualization multiplies by the square root of 252. If you trade monthly, it multiplies by the square root of 12. This assumes returns are independent and identically distributed, which they are not. Annualization systematically inflates Sharpe ratios for high-frequency strategies and deflates them for low-frequency ones.
Trade count is everything. With 20 trades, you can have a Sharpe ratio of any value purely by chance. With 200 trades, the estimate starts to converge. With 500 trades, you can be reasonably confident. Below 100 trades, do not trust the Sharpe ratio as a decision metric.
Standard deviation captures the wrong risk. A strategy with two 10 percent drawdowns and a strategy with one 30 percent drawdown can have the same Sharpe ratio. The difference in real trading is enormous. That is why I always check the SQN (System Quality Number)alongside Sharpe. SQN adjusts for the number of trades and gives a more honest picture of strategy quality.
What to do when the Sharpe ratio looks good
A high Sharpe ratio is not a green light. It is a reason to run more tests. Here is the checklist I use before I get excited about any backtest number.
First, check the trade count. If the backtest has fewer than 100 trades, the Sharpe ratio is not reliable regardless of the value. I have seen a 3.5 Sharpe from a 45-trade backtest that was pure luck in the trade sequence. The CVaR / Expected Shortfallmetric is more informative here because it focuses on the tail losses rather than the average volatility.
Second, run a Monte Carlo simulation. If the strategy goes negative in more than 20 percent of random trade reorderings, the Sharpe ratio was driven by trade sequence luck, not a real edge.
Third, check the ratio of Sharpe to Sortino. If they are close, the volatility is symmetric and the risk profile is honest. If Sharpe is significantly higher than Sortino, the strategy is getting carried by positive outliers and the drawdown risk is worse than the Sharpe suggests.
Other ratios to check alongside Sharpe
The Sharpe ratio is one piece of the puzzle. No serious trader makes a decision on Sharpe alone. Here are the metrics I cross reference.
- Calmar Ratio - Return divided by maximum drawdown. This tells you whether the strategy recovers from its worst loss. A high Sharpe with a low Calmar means smooth equity curve on paper but one bad hole that wipes out months of gains.
- Recovery Factor - Net profit divided by maximum drawdown. How fast does the strategy bounce back? This is the most intuitive risk-adjusted metric for retail traders.
- Ulcer Index - Measures the depth and duration of drawdowns, not just their peak. A strategy that sits in a drawdown for months scores worse on Ulcer Index than one that drops and recovers quickly, even if both have the same maximum drawdown.
- Value at Risk (VaR) - The worst expected loss at a given confidence level. VaR at 95 percent tells you the loss you should not exceed 95 times out of 100. It is a direct measure of the downside that Sharpe glosses over.
- MFE/MAE Analysis - Maximum Favorable and Adverse Excursion analysis shows how far trades go in your favor and against you. This helps set better stop-loss and take-profit levels.
A final thought on Sharpe and strategy development
The Sharpe ratio is a useful filter, not a final verdict. I use it to eliminate bad strategies quickly. A Sharpe below 0.5 and I move on without much thought. A Sharpe above 2.0 and I get suspicious and run every validation test I can find. The strategies I have actually traded with real money all fall in the 0.8 to 1.4 range on the trade-level Sharpe calculation. Not one of them broke 2.0 in the backtest.
If you are optimizing for Sharpe ratio in TradingView, consider taking the result with a 40 percent haircut. Subtract 0.3 to 0.5 for optimization bias, and check the trade count. If the number still looks good after those adjustments, you have something worth pursuing.
For a deeper analysis of your strategy quality, use the Strategy Optimizerto validate parameters on out-of-sample data before committing capital.
FAQ
Know Your Real Sharpe Ratio
Drop in your TradingView backtest CSV and get the real trade-level Sharpe calculation, plus Monte Carlo simulation and 15+ other metrics. All in your browser, no data leaves your device.
Analyze My Backtest Now