Is a high information ratio always good?

A high positive historical IR indicates a large average active return relative to measured tracking error. A short sample, tiny denominator, unsuitable benchmark or omitted costs can make it misleading. It does not guarantee future outperformance.

Can information ratio be negative?

Yes. With positive tracking error, a negative arithmetic mean of active returns produces a negative IR. The strategy may still have made money in absolute terms while lagging its benchmark.

Can I use CAGR minus benchmark CAGR in this formula?

That is a different numerator convention. This page uses the arithmetic mean of aligned periodic differences. Do not silently replace it with a difference of compound growth rates or compare the resulting ratios as if their definitions matched.

Does a TradingView trade CSV contain everything needed for IR?

No. You also need benchmark returns and strategy account returns over the same intervals, including the chosen treatment of open positions, cash and costs. Uneven trade percentages cannot simply be paired with monthly benchmark returns.

TradingView Backtesting

Information Ratio for TradingView Strategies: Benchmark and Tracking Error

Information ratio (IR) divides the arithmetic mean of strategy returns minus benchmark returns by the standard deviation of those differences, called tracking error. Use matching dates and return frequencies. For monthly data, the conventional annualized IR is the monthly ratio multiplied by sqrt(12). A positive IR means positive average active return in that sample; it does not establish profitability, skill or future outperformance.

See the decision guide

Align the two return series before calculating

Start with dated strategy equity and benchmark total-return values. With no external cash flows, each period return is ending value / previous ending value - 1. The table below assumes USD account returns after stated trading costs, matching month-end valuations and no deposits or withdrawals. These are hypothetical inputs, not measured TradingView results.

Use the same timezone, valuation time, start and end dates. A market holiday or missing observation is not automatically a zero return; resolve the gap or compare a common complete interval.
Include dividends consistently. Comparing a strategy that receives distributions against an unadjusted price-only benchmark can overstate active return.
Keep months without trades. Mark open positions to market at each boundary; a closed-trade balance can hide changes in their value.
Use the same account currency and document FX conversion, cash interest, financing and fees. Remove external cash-flow effects with a documented return method.

A six-month information ratio calculation

Subtract the benchmark return from the strategy return in each row. Percentage-point units work here because the mean and standard deviation use the same scale. The arithmetic average of the six differences is 0.50 percentage points per month.

Mean active return = (1 - 0.5 + 1 - 0.5 + 0.5 + 1.5) / 6 = 0.50 pp.
Deviations from the mean are 0.5, -1, 0.5, -1, 0 and 1 pp. Their squared sum is 3.5 pp².
Sample tracking error = sqrt(3.5 / (6 - 1)) = 0.836660 pp per month. The n - 1 denominator matches the sample standard deviation convention used here.
Monthly IR = 0.50 / 0.836660 = 0.597614. Annualized IR = 0.597614 × sqrt(12) = 2.070197.
The annualized arithmetic active mean is 6.00 pp and annualized tracking error is 2.898275 pp. Their ratio is also 2.070197. Six months of inputs do not constitute a full year of observed returns.

Month	Strategy return	Benchmark return	Active return
1	2.00%	1.00%	+1.00 pp
2	-1.00%	-0.50%	-0.50 pp
3	3.00%	2.00%	+1.00 pp
4	0.00%	0.50%	-0.50 pp
5	1.50%	1.00%	+0.50 pp
6	2.00%	0.50%	+1.50 pp

Reproduce the example in a spreadsheet

Enter months in A2:A7, strategy returns in B2:B7 and benchmark returns in C2:C7, with returns entered as percentages. Put =B2-C2 in D2 and fill down to D7. This worksheet is an information ratio calculator when all rows contain aligned numeric returns.

Validate both input cells before subtracting: spreadsheet subtraction can silently treat a blank cell as zero. Require at least two complete numeric pairs and positive tracking error before dividing.
Do not substitute STDEV.P without labeling the change. In this six-row example, the population denominator produces tracking error of 0.763763 pp and annualized IR of 2.267787.
Format IR as a plain number, not a percentage. Keep full precision until the displayed result.

Output	Spreadsheet formula	Example result
Monthly active mean	=AVERAGE(D2:D7)	0.50%
Monthly sample tracking error	=STDEV.S(D2:D7)	0.836660%
Monthly information ratio	=AVERAGE(D2:D7)/STDEV.S(D2:D7)	0.597614
Annualized information ratio	=AVERAGE(D2:D7)/STDEV.S(D2:D7)*SQRT(12)	2.070197

Arithmetic means, compounding and annualization

This page uses the arithmetic active-return convention: a_t = r_strategy,t - r_benchmark,t; mean(a) = sum(a_t) / n; TE = sqrt(sum((a_t - mean(a))²) / (n - 1)); IR = mean(a) / TE. Compounded strategy return minus compounded benchmark return is a separate measure and is not the numerator in this calculation.

The example compounds to 7.680932% for the strategy and 4.567837% for the benchmark. Their 3.113095 pp difference is neither the 0.50 pp monthly mean nor the 6.00 pp annualized arithmetic mean.
For m equally spaced observations per year, mean(a) annualizes as m × mean(a), tracking error as sqrt(m) × TE, and IR as sqrt(m) × IR. Monthly data uses m = 12; choose an explicit session or calendar convention for daily data.
Square-root scaling assumes an additive-return approximation and no serial correlation. Overlapping windows, stale prices or persistent active returns can invalidate that scaling. Show the unannualized estimate and frequency when those assumptions are doubtful.
Do not annualize twice, mix monthly tracking error with an annual numerator, or use the number of trades as the frequency of monthly account returns.

Sharpe ratio vs information ratio

In common investment reporting, Sharpe compares returns with a risk-free return, while information ratio compares returns with a selected benchmark. Both require a consistent frequency and a stated risk convention. Neither measures maximum drawdown or proves that a strategy can be executed at its simulated prices.

A profitable strategy can have negative IR if its benchmark did better. A losing strategy can have positive IR if its benchmark lost more.
Benchmark choice matters for a leveraged, short or market-neutral strategy. A broad equity index may describe an opportunity cost but does not automatically isolate trading skill or adjust for factor exposure.
Select the benchmark for the strategy mandate before examining which comparison produces the best IR. Record any change and recalculate both strategies against the same benchmark.

Measure	Numerator	Denominator	Question
Information ratio	Mean strategy return minus benchmark return	Standard deviation of those active returns	How much average active return was earned per unit of benchmark-relative variability?
Sharpe ratio	Mean strategy return minus risk-free return	Standard deviation of excess returns; with a constant risk-free rate, this equals strategy-return volatility	How much average excess return was earned per unit of excess-return variability?

Negative active returns and small samples need context

Subtract 1.00 percentage point from every strategy return in the example while holding the benchmark fixed. Mean active return becomes -0.50 pp; tracking error stays 0.836660 pp because subtracting a constant leaves deviations unchanged. Annualized IR becomes -2.070197. The calculation is valid, but ranking negative ratios alone can reward additional variability.

With the same negative mean, doubling tracking error moves IR from -2.07 to about -1.04. A value closer to zero does not mean that the mean shortfall improved.
If every active return is identical, tracking error is zero and a finite IR is undefined. If both mean and tracking error are zero, the result is 0/0, not zero. Report the raw differences instead.
One observation cannot estimate sample tracking error. Two observations permit a calculation but provide little evidence about a repeatable distribution.
Six monthly observations can produce an apparently large annualized IR. Report n = 6, retain losing periods and evaluate fixed rules on additional data excluded from strategy selection. There is no universal IR threshold that proves skill.
Near-zero tracking error magnifies rounding and data errors. Inspect full-precision returns and the numerator before interpreting a very large ratio.

Use TradingView exports without confusing trades with periods

TradingView exports the trade list separately from summary metrics. A trade return is measured against the trade size and is not automatically the return of the whole account for a calendar month. A trade CSV alone does not supply the aligned benchmark and month-end mark-to-market equity needed for this example.

Save the test dates, initial capital, symbol, costs and strategy properties with the trade export. Reconcile entry/exit records and open positions before building periodic returns.
Backtest Deep Report imports TradingView trade CSV files and reports metrics including net profit, drawdown, Sharpe and Sortino. Its current risk calculation uses trade percentages and trades per year; those figures are not the monthly account-return calculation shown here.
The current report does not expose this information-ratio calculation. Keep the benchmark series and worksheet calculation separate from its supported trade-report results.
For custom Pine statistics, give Pine Script AI Coding Agent the benchmark, account-equity definition, observation dates, costs and sample-standard-deviation rule. Review the generated code, validate it and reconcile its output with a known worksheet before using it.

Where Pineify fits

Backtest Deep Report

Import a TradingView trade CSV to inspect supported profit and risk results. Calculate information ratio separately from aligned strategy and benchmark returns.

Also useful: Pine Script AI Coding Agent. Describe a custom benchmark statistic and its observation rules, then review and validate the generated Pine Script against a manual calculation.

Explore Backtest Deep Report

Related resources

TradingView Backtesting Guides Audit Backtest Accuracy Set a Backtest Date Range Analyze TradingView CSV

Frequently asked questions

Educational information, not investment advice. All example returns are hypothetical. Historical information ratios and simulated results do not establish future returns or execution quality.

Sources and verification

Last verified: 2026-10-17

Published by Pineify. Pineify is an independent product and is not affiliated with TradingView. Sources support the technical explanations; examples do not establish trading performance.