Trading Expectancy Formula: Worked Example and Expectancy Ratio

Trading expectancy is the average profit or loss per closed trade. The formula is Expectancy = (Win rate × Average win) − (Loss rate × Average loss), with the average loss entered as a positive amount. Illustrative example: 40% of trades win an average of $150 and 60% lose an average of $50, so expectancy is (0.40 × 150) − (0.60 × 50) = $30 per trade. With a fixed risk per trade, dividing by that risk (1R) gives expectancy in R.

Expectancy is one of the per-trade measures in the trading metrics library. On its own, expectancy says nothing about how rough the ride was or whether the sample is big enough.

Trading expectancy at a glance

• Definition: the average result per closed trade, in account currency or in R.
• Formula (currency): (W × AW) − (L × AL).
• Shortcut: net profit ÷ number of closed trades, with breakevens counted as zero.
• Formula (R): average each trade’s result divided by that trade’s initial risk.
• Break-even point: expectancy is zero at a win rate of AL ÷ (AW + AL), before costs and with no breakeven trades.
• Where to read it: “Expected payoff” in TradingView, “Expected Payoff” in MT5 and “Expectancy” in PineConnector Analytics.
• Not the same as: the payoff ratio (AW ÷ AL), which some pages call the “expectancy ratio”.

What is the expectancy formula?

Expectancy = (W × AW) − (L × AL)

• W, win rate: winning trades ÷ all closed trades.
• L, loss rate: losing trades ÷ all closed trades. With no breakeven trades, L = 1 − W.
• AW, average win: gross profit ÷ number of winning trades.
• AL, average loss: gross loss ÷ number of losing trades, written as a positive number.

This page counts a breakeven trade as a closed trade with a zero result: it stays in the trade count but in neither W nor L. TradingView's “Percent profitable” likewise keeps even trades in the total count but not among the winners.^[1] With that convention, W × AW is gross profit per trade and L × AL is gross loss per trade. Expectancy therefore equals net profit divided by the number of closed trades. Use one consistent cost basis for the results, trade classifications and averages. If costs are already included, do not subtract them again. With no winners or no losers, use total result ÷ N instead of dividing a category total by zero; with N = 0, expectancy is undefined. TradingView calculates its Expected payoff as net result ÷ completed trades, and calls it the strategy's statistical expectancy.^[2]

Expectancy in R

Expectancy in R = sum of (result of trade i ÷ initial risk of trade i) ÷ N

Each initial risk must be positive. Only when every trade has the same initial R can you simplify this to (W × AW/R) − (L × AL/R), or currency expectancy ÷ R. Do not divide a currency average by an average risk when risks differ.

R is the initial risk on a trade: the loss you planned to accept at the initial stop, in currency. An R-multiple is the trade's result divided by that initial R. Van Tharp, who popularised the framing, writes that “Expectancy is actually the average R-multiple that your system will give you per trade.”^[3] Scaling each result by its own planned risk lets you compare trades of different sizes. The R-multiple guide covers logging R when a stop moves.

Worked example: expectancy in currency and in R

Illustrative numbers, not a recommendation or a strategy result. A trade log holds 100 closed trades: 40 winners, 60 losers and no breakevens. The winners made $6,000 in total and the losers lost $3,000. Every trade planned to risk $50 at its initial stop, so 1R = $50.

1. Win rate W = 40 ÷ 100 = 0.40. Loss rate L = 60 ÷ 100 = 0.60.
2. Average win AW = $6,000 ÷ 40 = $150. Average loss AL = $3,000 ÷ 60 = $50.
3. Winners contribute 0.40 × $150 = $60 per trade. Losers subtract 0.60 × $50 = $30 per trade.
4. Expectancy = $60 − $30 = $30 per trade. Check: $3,000 net profit ÷ 100 trades = $30.
5. In R: AW = 3R and AL = 1R, so expectancy = (0.40 × 3) − (0.60 × 1) = 0.6R per trade. Check: $30 ÷ $50 = 0.6R.

The same log has a payoff ratio of $150 ÷ $50 = 3 and a profit factor of $6,000 ÷ $3,000 = 2. Neither is expectancy: the payoff ratio ignores how often trades win, and profit factor is a ratio of totals, not an amount per trade. The calculator below sets L = 1 − W, so it assumes the log has no breakeven trades; for a log with breakevens, use the net-profit shortcut.

Calculator

Expectancy calculator

E = (W x AW) - (L x AL)

W is the win rate and L = 1 - W, so breakeven trades are left out. AW and AL are the average win and the average loss, both entered as positive amounts. Use amounts after round-trip costs. Expectancy in R divides E by R and assumes the same initial risk on every trade. For varying initial risks, average each trade's R-multiple instead.

Win rate W (%) 0 to 100

Average win AW Account currency

Average loss AL Account currency, positive

Initial risk R Optional, for expectancy in R

Worked example with the default inputs.

Expectancy per trade (account currency)

20.00

Expectancy in R

0.20

Payoff ratio (AW / AL)

2.00

Profit factor

1.33

Working (0.4 x 200) - (0.6 x 100) = 20.00

Illustrative calculator. Not a recommendation.

Why can a low win rate still have positive expectancy?

The win rate needed to break even depends on the size of wins relative to losses. Before costs, expectancy is zero when W = AL ÷ (AW + AL). With a $150 average win and a $50 average loss, the break-even win rate is 50 ÷ 200 = 25%, as the break-even win rate guide explains.

The reverse also holds. If 80% of trades win an average of $20 and 20% lose an average of $100, expectancy is (0.80 × 20) − (0.20 × 100) = −$4 per trade. The break-even point there is 100 ÷ 120, about 83.3%, so an 80% win rate still loses on average.

Tharp's own teaching example makes the same point in R. A stock trade idea risks $1 per share for a $15 gain and is right 30% of the time, so its expectancy is (0.3 × 15) − (0.7 × 1) = 3.8R.^[3] The example is Tharp's hypothetical, not a typical or expected result. Whether a given expectancy is good is a separate question, covered in what counts as a good trading expectancy.

Is the “expectancy ratio” the same as expectancy?

Check the formula behind “expectancy ratio”. These three calculations need separate labels:

1. Expectancy itself: the average result per trade in currency.
2. Expectancy per unit of risk: currency expectancy divided by the average loss, or by a fixed initial risk shared by all trades. PineConnector's Analytics guide expresses a $10 expectancy as 20% of a $50 average loss, “also called 0.2R”.^[4] Dividing by the average loss gives the same figure as dividing by a fixed R only when the average loss equals that R.
3. Payoff ratio: average win ÷ average loss. TradingView reports the figure separately as “Average profit / average loss” and notes that it does not factor in win rate.^[5]

Reading the payoff ratio as expectancy misleads, because the payoff ratio ignores win rate. With a payoff ratio of 3 ($150 ÷ $50), a 40% win rate gives +$30 per trade, while a 20% win rate gives (0.20 × 150) − (0.80 × 50) = −$10. The payoff ratio is identical and the expectancy has the opposite sign.

What changes expectancy, and what does each change trade away?

Expectancy depends on win and loss rates, their average sizes and any costs not already included. Advice to “raise the win rate” or “widen the reward” describes levers with side effects, not promises.

• Entry filters change W, AW and AL together and shrink the sample. A filter can remove winners as well as losers.
• Targets: a closer target is usually reached more often but pays less per win. A farther target reverses the trade-off.
• Stops: a closer stop makes each loss smaller in price terms but can turn trades that would have recovered into losses. Measure the effect in R.
• Breakeven and trailing stops turn some losses into smaller ones and can cut some wins short. Only the trade list shows the net effect.
• Costs come off every trade. Illustrative: $5 of average cost per trade takes the example from $30 to $25, or 0.5R, and moves its break-even win rate to (50 + 5) ÷ 200 = 27.5%.
• Trade frequency: for a completed sample, total result equals average result × trade count. Adding trades need not preserve the average, even under the same rules; conditions and execution can differ.

Test any rule change on data it was not tuned on. TradingView's strategy documentation describes testing outside the sample used for optimisation.^[6] PineConnector executes the instructions an alert sends. Automation does not add an edge the rules lacked.

Where to find expectancy in TradingView, MT5 and PineConnector

Labels as of 25 September 2026:

• TradingView: “Expected payoff”, in the strategy report (formerly the Strategy Tester^[7]), in the Metrics tab's Return details section.^[6] TradingView defines it as Net P&L ÷ completed trades, closed trades only.^[2] Average PnL uses the same division.^[8]
• MT5 Strategy Tester report: “Expected Payoff”, defined as “the average return of one deal”.^[9]
• MT5 account trading report: “Expected Payoff”, with the same definition; each deal's result is taken after commission, fees and swap.^[10]
• PineConnector Portal Analytics: “Expectancy”, the average gain or loss per trade from the historical win rate and average win/loss. Analytics needs MT5 EA v3.41 or later and is not available for MT4 in the published guide.^[4]

The TradingView strategy report guide and the MT5 Strategy Tester report guide map the neighbouring fields.

Calculate expectancy from a TradingView trade list

1. Open the strategy report's Trades tab and use the Download icon. The CSV holds every available column, whichever are on screen.^[6]
2. Count the closed trades (N), the winners, the losers and any zero-result trades.
3. Use one currency result per closed trade, counting an entry and exit as one trade. TradingView defines Net PnL as the sum of closed-trade results. Do not average the Trades tab’s percentage Return column as if it were a currency amount.^[11]
4. If the export does not supply the currency results needed for this calculation, use the report’s Expected payoff. Otherwise divide their total by N. Cross-check with (W × AW) − (L × AL), where W and L both divide by N.
5. For R, add a column with each trade's planned risk at its initial stop, which the documented columns do not include. Divide each result by its planned risk and average the column.

On the default testing range, the list and its CSV keep only the latest 9000 trades while the Metrics tab covers all of them, so a CSV-based figure can differ on a long test.^[6]

In MT5, the Strategy Tester report lists Expected Payoff along with Total Net Profit and Total Trades, so Total Net Profit ÷ Total Trades gives a cross-check.^[9] The MT5 Strategy Tester tests Expert Advisors.^[12] A Pine Script strategy's rules run in TradingView, so its backtest figures come from the TradingView strategy report. Live trades sent through PineConnector appear in the MT5 account's history and trading report.^[10]

Why does expectancy differ between a TradingView backtest and an MT5 account?

The route runs from idea to measurable rule to TradingView alert to MT5 order. A backtest's expectancy describes simulated fills, and several separate stages sit between those and the account's closed trades:

• the alert condition becoming true, and the alert triggering;
• webhook delivery;
• PineConnector processing the signal;
• the EA's order request;
• broker acceptance, the deal and the position;
• a stop or target held at the broker, versus an exit that exists only in the strategy.

A missed or rejected entry can omit a trade; a missed exit can leave exposure open. PineConnector's recommendations treat backtests and repainting as separate checks: “A good-looking historical chart does not confirm that live signals arrive at the same points.”^[13] TradingView and MetaTrader can use different data feeds, bid/ask quotes, spreads and timestamps.^[14] A TradingView strategy applies no commission unless one is specified, and its default slippage is zero.^[6] Compare the backtest and the account over matching trades, in R where possible, using the backtest-to-demo reconciliation steps.

R is easier to track when the alert fixes the planned loss. Illustrative PineConnector message for the TradingView alert’s Message field; not executed. Replace LicenseID with your intended connection’s ID and EURUSD with the exact broker symbol. This example assumes a USD account and the pip value stated below:

LicenseID,buy,EURUSD,vol_dollar=100,sl_pips=20

The syntax reference treats this as a planned cash loss. Its worked example sizes a USD 100 budget with a 20-pip stop, at an assumed USD 10 per pip per lot, to 0.50 lots before broker rounding. The same page warns that costs, fills, gaps and broker limits can make the actual loss differ.^[15] Treat the planned 100, in account currency, as 1R, unless broker volume rounding changed the size; then recompute R from the filled volume and the initial stop. Log each realised result ÷ R rather than an assumed −1R. Test messages on a demo account first. Before comparing Analytics with a backtest, check which costs each figure includes.^[4]

What expectancy hides: limitations and common mistakes

• Swings and streaks. Logs with the same expectancy can have very different equity paths. With a 60% loss rate and independent trades, the probability of five losses in a row, starting from a given trade, is 0.6⁵ ≈ 7.8%. See losing streak probability.
• Sample size. Assuming independent trades with a stable win probability, 100 trades and a 40% win rate give a standard error of the win rate of √(0.40 × 0.60 ÷ 100) ≈ 4.9 points, so a rough 95% range is 30.4% to 49.6%. With the $150 and $50 averages fixed, varying only that win rate spans expectancy from about $11 to $49 per trade. This is a sensitivity calculation, not a confidence interval for expectancy: the win and loss averages are uncertain too. See how many trades a backtest needs.
• Outliers. Recalculate without the largest win. If $1,000 of the example's $6,000 in wins came from a single trade, the other 99 trades would net $2,000, about $20.20 per trade.
• Changing size. If position size grew, later trades carry more weight in currency results. Dividing each trade by its own initial R normalises that planned size, without removing changes in costs or market conditions.
• Mixed denominators. Setting L = 1 − W while breakevens exist counts them as losses. Keep W and L over the same N.
• History read as a forecast. MetaQuotes says Expected Payoff “is considered to display the expected return of the next trade”,^[9] yet the figure is an average of past trades. PineConnector's Analytics guide says a positive expectancy does not mean the next trade, or a future period, will be profitable. It adds that a short period or a small number of trades can give a misleading picture.^[4]

Frequently asked questions

What is expectancy in trading?

Expectancy in trading is the average profit or loss per closed trade. The figure combines how often trades win with how large wins and losses are: (win rate × average win) − (loss rate × average loss). A positive value means the average past trade made money after whatever costs the figures include. Expectancy describes history, not the next trade.

How do you calculate expectancy from win rate, average win and average loss?

Multiply the win rate by the average win, multiply the loss rate by the average loss, then subtract the second result from the first. Enter the average loss as a positive number. Illustrative: a 40% win rate with a $150 average win, and a 60% loss rate with a $50 average loss, give (0.40 × 150) − (0.60 × 50) = $30 per trade.

What is the expectancy ratio?

Check the formula. Expectancy is the average result per trade. Dividing currency expectancy by the average loss gives expectancy per unit of average loss; dividing by a fixed initial risk gives expectancy in R. If initial risk varies, average each trade’s own R-multiple instead. Average win ÷ average loss is the payoff ratio, which ignores win rate.

Can a strategy with a low win rate have positive expectancy?

Yes, when the average win is large enough relative to the average loss. Before costs and with no breakeven trades, expectancy is positive whenever the win rate exceeds average loss ÷ (average win + average loss). Illustrative: with a $150 average win and a $50 average loss, the break-even win rate is 25%, so a 40% win rate gives +$30 per trade.

Is MT5's Expected Payoff the same as expectancy?

Yes, in currency terms. MetaQuotes defines Expected Payoff in MetaTrader 5 reports as the average return of one deal, which you can cross-check against Total Net Profit ÷ Total Trades. TradingView's strategy report shows the same idea as “Expected payoff”, calculated as net P&L ÷ completed trades. Neither report lists expectancy in R, so divide results by planned risk yourself.

Reviewed 25 September 2026. Facts were checked against the linked sources on that date. Nothing in this article was tested on a trading account and no code was compiled.

Related reading

• Trading metrics: the complete library
• Payoff ratio: average win divided by average loss
• R-multiples: measuring trades in units of risk
• Break-even win rate, before and after costs
• What counts as a good trading expectancy

Sources

1. TradingView – Percent profitable, accessed 25 September 2026.
2. TradingView – Expected payoff, accessed 25 September 2026.
3. Van Tharp Institute – Van K. Tharp, A short lesson on R and R-multiples (PDF), accessed 25 September 2026.
4. PineConnector – Trading analytics: performance metrics and expectancy example, accessed 25 September 2026.
5. TradingView – Average profit / average loss, accessed 25 September 2026.
6. TradingView – Pine Script v6 User Manual: Strategies, accessed 25 September 2026.
7. TradingView – Pine Script release notes, accessed 25 September 2026.
8. TradingView – Average PnL, accessed 25 September 2026.
9. MetaQuotes – MetaTrader 5 Help: Testing Report, accessed 25 September 2026.
10. MetaQuotes – MetaTrader 5 Help: Trading Report (for advanced users), accessed 25 September 2026.
11. TradingView – Net PnL, accessed 25 September 2026.
12. MetaQuotes – MetaTrader 5 Help: Strategy Testing, accessed 25 September 2026.
13. PineConnector – Recommendations: treat backtests and repaints as separate checks, accessed 25 September 2026.
14. PineConnector – Frequently asked questions: price differences, accessed 25 September 2026.
15. PineConnector – Syntax: loss-based sizing, accessed 25 September 2026.

PineConnector executes the instructions you send it. It does not select trades, manage money, or hold funds. Trading carries risk, and past performance of any strategy does not indicate future results.