Performance Metrics

Arconomy calculates a comprehensive set of performance metrics for every backtest, giving you a detailed picture of how your strategy performed across multiple dimensions — profitability, risk, consistency, and efficiency. This page explains every metric, how it is calculated, and what it tells you about your strategy. You will also learn how to export your results and compare metrics across multiple backtest runs.

Profitability Metrics

Profitability metrics measure the raw financial outcomes of your strategy. They answer the fundamental question: did the strategy make money?

Net Profit

The total profit or loss generated by the strategy over the backtest period, after deducting all costs including commissions, spreads, slippage, and swap charges. Net profit is expressed in the account currency. This is the single most important headline number, but it should always be evaluated in the context of the risk metrics below — a strategy that generates high profit while taking extreme risk is not necessarily better than one that generates moderate profit with controlled risk.

Gross Profit and Gross Loss

Gross profit is the sum of all winning trades before costs. Gross loss is the sum of all losing trades before costs. The ratio of gross profit to gross loss (the profit factor, described below) is a useful indicator of how much room the strategy has to absorb increased costs or worse execution conditions.

Return on Capital (%)

The net profit expressed as a percentage of the starting account balance. This normalises the result so you can compare strategies tested with different starting balances. Arconomy calculates this as: (Net Profit / Starting Balance) x 100.

Annualised Return (%)

The return on capital projected to a one-year period, accounting for compounding. If your backtest covers six months and returned 15%, the annualised return will be higher than 30% because it accounts for the compounding effect of reinvesting gains. This metric uses the formula: ((1 + Total Return) ^ (365 / Days in Backtest)) - 1. The annualised return allows you to compare strategies tested over different time periods on an equal footing.

Profit Factor

The ratio of gross profit to gross loss: Profit Factor = Gross Profit / |Gross Loss|. A profit factor greater than 1.0 means the strategy is profitable. A profit factor of 2.0 means the strategy earned twice as much on its winners as it lost on its losers. Generally, a profit factor above 1.5 is considered good, and above 2.0 is considered strong. However, very high profit factors (above 5.0) in short backtests may indicate overfitting rather than genuine edge.

Expectancy

The average amount you can expect to win or lose per trade, calculated as: Expectancy = (Win Rate x Average Win) - (Loss Rate x Average Loss). A positive expectancy means the strategy is expected to be profitable over a large number of trades. Expectancy is expressed in the account currency per trade. Dividing expectancy by the average loss gives you the expectancy ratio, which indicates how many units of reward the strategy generates per unit of risk.

Risk Metrics

Risk metrics measure the downside potential and volatility of your strategy. A strategy is only as good as its risk profile — these metrics help you understand the worst-case scenarios you might face.

Maximum Drawdown

The largest peak-to-trough decline in your account equity during the backtest, expressed in both currency and percentage terms. Maximum drawdown represents the worst period of consecutive losses your strategy experienced. It is calculated by tracking the running high-water mark of equity and measuring the deepest valley below it. For example, if your account grew from $10,000 to $15,000 and then declined to $12,000 before recovering, the maximum drawdown would be $3,000 or 20%.

Average Drawdown

The mean of all individual drawdown periods during the backtest. While maximum drawdown tells you the worst case, average drawdown tells you what a typical drawdown period looks like. A strategy with a large maximum drawdown but small average drawdown may have experienced one unusual event, whereas a strategy with a large average drawdown is consistently experiencing significant declines.

Maximum Drawdown Duration

The longest period of time the account spent below its previous high-water mark before recovering. This measures how long you would have had to wait during the worst losing period before the strategy recovered to its previous peak. Long drawdown durations can be psychologically challenging and may indicate that the strategy struggles in certain market conditions.

Sharpe Ratio

The risk-adjusted return of the strategy, calculated as: Sharpe Ratio = (Annualised Return - Risk-Free Rate) / Annualised Standard Deviation of Returns. Arconomy uses the daily returns of the equity curve to calculate the standard deviation and annualises it by multiplying by the square root of 252 (the typical number of trading days per year). The risk-free rate defaults to 0% but can be configured in the backtest settings. A Sharpe ratio above 1.0 is generally considered acceptable, above 2.0 is good, and above 3.0 is excellent.

Sortino Ratio

A variation of the Sharpe ratio that only penalises downside volatility. Instead of using the standard deviation of all returns, the Sortino ratio uses only the standard deviation of negative returns (downside deviation). This provides a more accurate picture of risk-adjusted performance for strategies that have asymmetric return distributions — for example, strategies that produce many small losses but occasional large wins. Sortino Ratio = (Annualised Return - Risk-Free Rate) / Annualised Downside Deviation.

Calmar Ratio

The annualised return divided by the maximum drawdown: Calmar Ratio = Annualised Return / Maximum Drawdown (%). This metric directly relates return to the worst-case risk experienced. A Calmar ratio above 1.0 means the annualised return exceeds the maximum drawdown. Higher values indicate a more favourable relationship between return and risk.

Trade Statistics

Trade statistics describe the characteristics of individual trades and help you understand the mechanics of how your strategy generates its returns.

Total Trades

The total number of completed trades (entry and exit) during the backtest. A higher trade count generally provides more statistical significance for the other metrics. Be cautious about drawing conclusions from backtests with fewer than 30 trades.

Win Rate (%)

The percentage of trades that closed with a positive net P&L: Win Rate = (Number of Winning Trades / Total Trades) x 100. Win rate alone does not determine profitability — a strategy can be profitable with a 30% win rate if its average win is much larger than its average loss, and unprofitable with an 80% win rate if its average loss dwarfs its average win.

Average Win and Average Loss

The mean P&L of winning trades (average win) and losing trades (average loss), expressed in both currency and percentage terms. The ratio of average win to average loss is the reward-to-risk ratio. A reward-to-risk ratio above 1.0 combined with a win rate above 50% is a strong indicator of a robust strategy.

Largest Win and Largest Loss

The single most profitable and least profitable trades in the backtest. If your net profit depends heavily on one or two outsized winners, the strategy may be fragile — removing those trades could turn a profitable backtest into a losing one. Similarly, if a single large loss significantly impacted results, your risk management rules may need strengthening.

Maximum Consecutive Wins and Losses

The longest streak of consecutive winning trades and consecutive losing trades. Long losing streaks are psychologically challenging and can trigger drawdowns. Understanding the maximum consecutive losses helps you set realistic expectations for what live trading will feel like.

Average Trade Duration

The mean holding period across all trades. This tells you the typical timescale your strategy operates on. Compare this to the average duration of winners versus losers — if losers are held significantly longer than winners, your exit rules may be allowing losing trades to run too long.

Trades Per Day / Week / Month

The average frequency of trade execution across different time periods. This metric helps you understand how active the strategy is and whether it generates enough opportunities to justify the time and capital allocated to it.

Equity Curve Analysis

The equity curve chart shows your account balance over the course of the backtest. Arconomy overlays several analytical features on the equity curve to help you evaluate consistency and identify regime changes.

• Drawdown overlay — A shaded region below the equity curve showing the depth of each drawdown period. The maximum drawdown is highlighted with a distinct colour.
• Linear regression channel — A trend line fitted to the equity curve with upper and lower bands. A steadily rising trend with tight bands indicates consistent performance. A flat or declining trend indicates the strategy may be losing its edge.
• Underwater chart — An alternative view that shows the equity curve expressed as a percentage below the high-water mark at each point. This makes it easy to see when and how quickly the strategy recovered from drawdowns.
• Monthly returns heatmap — A calendar-style grid showing the return for each month of the backtest. Green cells are profitable months and red cells are losing months. This helps identify seasonal patterns or extended losing periods.

Exporting Results

Arconomy provides several export options so you can analyse your backtest results in external tools, share them with collaborators, or archive them for your records.

CSV Export

Click Export → CSV from the performance metrics view to download a comprehensive CSV file. The export includes:

• A summary sheet with all performance metrics and their values.
• A trade list with one row per trade and columns for entry time, exit time, direction, symbol, entry price, exit price, position size, gross P&L, commissions, net P&L, duration, and the names of the entry and exit rules that triggered.
• A daily equity series with the account balance at the close of each trading day.

The CSV format is compatible with any spreadsheet application and can be imported into quantitative analysis tools such as Python, R, or MATLAB for further research.

PDF Report

Click Export → PDF Report to generate a formatted performance report. The PDF includes:

• A cover page with the strategy name, backtest parameters, and date range.
• A summary page with all key metrics displayed in a clear, tabular layout.
• The equity curve chart with drawdown overlay.
• The monthly returns heatmap.
• A trade summary table with the top 10 winning and losing trades.

The PDF report is designed to be shared or printed. It provides a professional overview of your strategy's performance without requiring the recipient to have access to the Arconomy platform.

Comparing Multiple Backtest Runs

One of the most valuable workflows in strategy development is comparing the results of multiple backtest runs side by side. This is essential when you are iterating on a strategy — adjusting parameters, adding or removing rules, or testing across different date ranges and instruments.

The Comparison View

To compare backtests, navigate to your strategy's backtest history and select two or more completed backtest runs using the checkbox next to each. Then click the Compare button. The comparison view opens with the selected backtests displayed side by side.

The comparison view includes:

• Metric comparison table — Every performance metric for each backtest is shown in a table with columns for each run. Cells are colour-coded to indicate which run performed better on each metric — green for the better value and red for the worse value.
• Overlaid equity curves — The equity curves of all selected backtests are drawn on the same chart, each in a different colour. This makes it easy to see where one variant outperformed or underperformed another.
• Parameter diff — A summary of the differences in backtest configuration between the selected runs — which parameters changed, which rules were added or removed, and which date ranges were used.
• Delta values — For each metric, the absolute and percentage change between the baseline run and each comparison run is displayed, making it easy to quantify the impact of your changes.

What to Look For When Comparing

When comparing backtest runs, focus on these aspects:

• Net profit and Sharpe ratio together. If a parameter change increased net profit but decreased the Sharpe ratio, the additional return came at the cost of more risk. Decide whether the trade-off is acceptable.
• Maximum drawdown stability. If a change reduced maximum drawdown without significantly reducing returns, it is almost always a positive improvement.
• Trade count changes. If a parameter change dramatically increased or decreased the number of trades, the strategy's behaviour has fundamentally shifted. Investigate why.
• Consistency of win rate and average win/loss. If these metrics change significantly between runs with only minor parameter adjustments, the strategy may be sensitive to parameter choice — a warning sign for potential overfitting.
• Out-of-sample performance. If you optimised parameters on one date range and then tested on a different date range, compare the two. A large drop in performance on the out-of-sample period suggests overfitting to the in-sample data.

Interpreting Metrics Holistically

No single metric tells the whole story. A high net profit means nothing if it was achieved with a maximum drawdown that would have wiped out your account. A high Sharpe ratio means nothing if the strategy only took three trades. Here are some guidelines for holistic interpretation:

• Profitability + Risk: Always evaluate net profit alongside maximum drawdown, Sharpe ratio, and Calmar ratio. You want a strategy that generates returns efficiently relative to the risk it takes.
• Statistical significance: Ensure the trade count is large enough to be meaningful. With fewer than 30 trades, the metrics are unreliable. Aim for at least 100 trades for robust conclusions.
• Consistency: Check the monthly returns heatmap and the equity curve's regression channel. A strategy with steady, consistent returns is more reliable than one that depends on a few large winning months.
• Robustness: Compare results across multiple time periods, instruments, and parameter settings. A truly robust strategy performs well across a range of conditions, not just the specific combination it was optimised for.