Understanding Backtest Results – Volensy – Smarter Tools. Sharper Trades.

Note: The Volensy Backtest Suite is coming soon. This article describes the planned performance reports and metrics. Specific values, layouts, and visualization details may change before the final release. This preview is provided so you can understand the key concepts and be ready to interpret your results effectively when the suite launches.

After running a backtest, the Backtest Suite generates a comprehensive performance report. This report contains the quantitative evidence you need to evaluate whether a strategy is worth pursuing. This guide explains every major metric in the report, what it means, why it matters, and how to use these metrics together to form a complete picture of strategy quality.

The Results Dashboard

When a backtest completes, the suite displays a results dashboard organized into several sections:

Summary Metrics — Key performance numbers displayed as cards or tiles at the top.
Equity Curve — A line chart showing the growth (or decline) of the simulated account over time.
Trade List — A detailed table of every simulated trade, similar to the Signals Panel format.
Distribution Charts — Optional visual breakdowns of wins vs. losses, PnL distribution, and other patterns.

Key Performance Metrics

Win Rate

What it is: The percentage of closed trades that were profitable.

Formula: (Number of Winning Trades / Total Closed Trades) x 100

Why it matters: Win rate is the most intuitive measure of strategy accuracy. A 60% win rate means the strategy produces a profit on 6 out of every 10 trades. However, win rate alone is not sufficient to determine profitability. A strategy with a 40% win rate can still be highly profitable if its average winning trade is much larger than its average losing trade.

What to look for: A win rate above 50% is generally encouraging, but always evaluate it alongside the win/loss size ratio. A very high win rate (above 80%) paired with very small wins and occasional large losses may be a warning sign, not a strength.

Total Trades

What it is: The total number of simulated trades the strategy executed during the backtest period.

Why it matters: Total trades tells you about the strategy’s activity level and the statistical reliability of the results. A backtest that produced 200 trades gives you much more confidence in the metrics than one that produced 15 trades. Small sample sizes make all other metrics unreliable.

What to look for: Aim for at least 30-50 trades as a minimum for the results to have basic statistical meaning. Ideally, 100+ trades give you a solid dataset. If your backtest produced fewer than 20 trades, consider extending the date range or switching to a shorter timeframe to generate more data points.

Maximum Drawdown

What it is: The largest peak-to-trough decline in the simulated account balance during the backtest. Expressed as a percentage, it measures the worst losing streak the strategy experienced.

Formula: ((Peak Balance – Trough Balance) / Peak Balance) x 100

Why it matters: Maximum drawdown is one of the most important risk metrics. It tells you the worst-case scenario you would have experienced if you had followed the strategy during its hardest period. A strategy with a 25% max drawdown means your account would have dropped by a quarter from its high point at some stage during the test.

What to look for: Lower drawdown is better. A max drawdown under 20% is generally considered manageable for most traders. Above 30-40%, the strategy carries significant risk and requires strong psychological resilience to follow through losing periods. Always consider whether you could personally tolerate the drawdown level shown.

Warning: Maximum drawdown is a historical measure. Future drawdowns could be larger. Use the backtest drawdown as a guide, not a guarantee.

Profit Factor

What it is: The ratio of total gross profits to total gross losses across all trades.

Formula: Sum of All Winning Trade Profits / Sum of All Losing Trade Losses (absolute value)

Why it matters: Profit factor provides a single number that captures the relationship between money made and money lost. A profit factor of 1.0 means the strategy broke even. Above 1.0 is profitable. Below 1.0 is unprofitable.

What to look for:

Below 1.0: The strategy loses money overall. Not viable.
1.0 to 1.5: Marginally profitable. May not survive real-world conditions (slippage, fees).
1.5 to 2.0: Solid profitability. A good target range.
Above 2.0: Strong profitability. Verify this is not the result of overfitting or a very small sample size.
Above 3.0: Exceptional, but scrutinize carefully. Very high profit factors in backtests often do not survive in live trading.

Sharpe Ratio

What it is: A risk-adjusted return metric that measures how much excess return the strategy generates per unit of risk (volatility).

Why it matters: Two strategies might have the same total return, but the one with a higher Sharpe ratio achieved that return with less volatility — meaning a smoother, more consistent equity curve. Sharpe ratio helps you compare strategies on a risk-adjusted basis rather than purely on returns.

What to look for:

Below 0.5: Poor risk-adjusted returns.
0.5 to 1.0: Acceptable.
1.0 to 2.0: Good. The strategy is generating returns efficiently relative to its risk.
Above 2.0: Excellent risk-adjusted performance.

Note: Sharpe ratio is sensitive to the time period and frequency of returns used in the calculation. The Backtest Suite will calculate it based on the trade-level returns over the backtest period.

Net Profit

What it is: The total simulated profit (or loss) at the end of the backtest, expressed as a percentage or absolute value relative to the starting balance.

Why it matters: Net profit is the bottom-line answer to “Did the strategy make money?” It is the simplest metric but should always be interpreted alongside drawdown, trade count, and profit factor.

What to look for: A positive net profit is necessary but not sufficient. A strategy that made 50% over a year but had a 40% max drawdown along the way may not be worth the stress. Always weigh net profit against the risk required to achieve it.

The Equity Curve

The equity curve is a line chart that plots the simulated account balance over time, from the first trade to the last. It is one of the most informative visual tools in the report.

Close-up of an equity curve chart showing a line that generally trends upward with some dips and drawdowns, the x-axis showing dates across the backtest period, the y-axis showing account balance, key drawdown periods highlighted with shaded areas, the line colored in green (#5DEA66) for upward segments and red for drawdown segments

Reading the Equity Curve

Upward slope indicates profitable periods where the strategy is generating gains.
Flat periods indicate times when the strategy is either not trading or breaking even.
Downward dips indicate drawdown periods where the strategy is experiencing losses.
The overall shape tells you about consistency. A smooth, steadily rising curve is ideal. A jagged, volatile curve with deep dips suggests inconsistent performance.

What to Watch For

Steady climb with small dips: Strong indicator of a robust strategy. Drawdowns are manageable and the strategy recovers quickly.
Long flat periods: The strategy may not be active enough or may struggle in certain market conditions. Not necessarily bad, but worth noting.
Sharp spike followed by a crash: Could indicate the strategy got lucky during a trending period and then gave back gains when conditions changed. Be cautious about strategies that show this pattern.
Continuous decline: The strategy loses money consistently. Not viable.

The Trade List

Below the summary metrics and equity curve, the results include a full trade list — a table of every simulated trade from the backtest. Each row includes:

Entry date — When the simulated trade opened.
Exit date — When it closed.
Direction — Long or Short.
Market — The asset pair.
Entry price — Price at position open.
Exit price — Price at position close.
PnL — Profit or loss as a percentage.
Result — Win, Loss, or Break-even.
Exit reason — What triggered the close (take profit, stop loss, strategy exit).

The trade list is your raw data. It lets you examine individual trades, look for patterns in losses, and verify that the strategy is behaving as expected.

Comparing Different Configurations

One of the most powerful uses of the Backtest Suite is running the same strategy multiple times with different parameters and comparing the results. When comparing configurations, focus on these key comparisons:

The best configuration is not necessarily the one with the highest net profit. It is the one that offers the best balance of return, risk, and consistency.

Note: When comparing configurations, change only one parameter at a time. If you change multiple parameters simultaneously, you will not know which change caused the performance difference.

Common Interpretation Mistakes

Focusing only on win rate

A 90% win rate with tiny wins and occasional devastating losses is worse than a 45% win rate with large wins and small losses. Always look at profit factor and drawdown alongside win rate.

Ignoring drawdown

A strategy that makes 100% in a year but draws down 60% along the way will be psychologically unbearable for most traders. Drawdown tolerance is personal, but most traders underestimate how painful a 30%+ drawdown feels in real time.

Too few trades

A backtest with 12 trades that all won does not prove the strategy works. It proves it worked 12 times in a specific period. You need a statistically meaningful sample size.

Confusing backtest results with live performance

Backtesting uses perfect historical data with no slippage, no latency, and no emotional interference. Live trading includes all of these factors. Expect some performance degradation from backtest to live execution.

*See also: Running Your First Backtest*
*See also: Optimizing Your Strategy*