Win Rate vs Expectancy and Profit Factor

A trading strategy can win seven trades out of ten and still lose money. The missing information is how much it makes on winners and gives back on losers.

Win rate measures the frequency of profitable trades. Expectancy measures the average result per trade. Profit factor compares total winning dollars with total losing dollars. Together, they help explain a strategy's historical results, but none establishes what it will earn next.

For NQ and MNQ traders reviewing a systematic TradingView strategy, start by calculating all three from the same trade list, date range, position-sizing rules, and cost assumptions.

What win rate actually measures

Win rate is winning closed trades divided by all closed trades, multiplied by 100. Forty winners from 100 closed trades gives a 40% win rate.

TradingView calls this metric Percent profitable. Break-even trades stay in the total trade count but do not count as winners. Open positions are excluded. That matters when comparing reports that classify scratches differently. TradingView's win-rate definition

A win rate tells you nothing about the dollar size of each outcome. A $5 winner and a $500 winner each add one winning trade to the count.

What expectancy tells you

Historical expectancy is the average profit or loss per closed trade:

Expectancy = total net profit or loss ÷ number of closed trades

It can also be calculated as:

Expectancy = win rate × average win − loss rate × average loss

Use decimal rates and the positive magnitude of the average loss. If there are break-even trades, calculate the loss rate separately rather than assuming it equals one minus the win rate.

TradingView's Expected payoff metric uses net PnL divided by completed trades. An expectancy of $18 describes the sample's average, not a payment to expect from each future trade. TradingView's expected-payoff definition

What profit factor tells you

Profit factor = total profits from winning trades ÷ absolute total losses from losing trades

A profit factor of 1.50 means the sample produced $1.50 in winning-trade profits for every $1 in losing-trade losses. Above 1 indicates a positive result on the measurement basis used; below 1 indicates a negative result. TradingView's profit-factor definition

This ratio does not describe the order of trades, maximum drawdown, or how much capital was required. With no losing trades, the denominator is zero, so there is no ordinary finite profit-factor calculation.

Two strategies with very different results

These hypothetical examples use 100 closed trades each and exclude transaction costs. They are not AORDS results.

Strategy A wins 70% of the time:

• 70 winners averaging $40 produce $2,800

• 30 losers averaging $100 lose $3,000

• Total result is −$200

• Expectancy is −$200 ÷ 100 = −$2 per trade

• Profit factor is $2,800 ÷ $3,000 = approximately 0.93

Strategy B wins 40% of the time:

• 40 winners averaging $180 produce $7,200

• 60 losers averaging $80 lose $4,800

• Total result is $2,400

• Expectancy is $2,400 ÷ 100 = $24 per trade

• Profit factor is $7,200 ÷ $4,800 = 1.50

Strategy B has the better result in this sample because its average winner is large enough to offset more frequent losses. Its lower win rate alone does not disqualify it. Equally, its positive average does not tell us whether its drawdowns or losing streaks would fit a particular account.

Include costs without subtracting them twice

Suppose Strategy B incurs an illustrative $6 per completed trade in combined fees and execution costs. Its 100 trades cost $600, reducing net profit to $1,800 and expectancy to $18.

Assume every pre-cost winner is larger than $6, so costs do not change any trade's winning or losing classification. With each result reduced by exactly $6, average winners become $174 and average losers become $86. Profit factor becomes $6,960 ÷ $5,160, or approximately 1.35. The win rate remains 40% here, although costs can turn small winners into losers in other samples.

Check how the reporting platform handles costs. TradingView's Gross profit and Gross loss metrics incorporate configured commissions into individual trade results. Do not deduct the same commission again from an already-net result. Gross profit calculation, Gross loss calculation

Use the actual fee schedule and clearly stated slippage assumptions. TradingView can simulate both commission and slippage, but simulated fills remain assumptions rather than evidence of executable prices. TradingView's trading-cost documentation

Compare NQ and MNQ on a consistent basis

NQ has a $20 index-point multiplier; MNQ has a $2 multiplier. Both have a minimum tick of 0.25 index points, making one tick worth $5 for NQ and $0.50 for MNQ. CME NQ specifications, CME MNQ specifications

With identical fills and one contract, the same point-based outcomes therefore produce ten times the pre-cost dollar expectancy on NQ. That difference comes from exposure, not better entries.

Multiplying every trade result by the same positive number leaves win rate and profit factor unchanged. Changing size selectively across trades can change dollar expectancy and profit factor substantially. Costs and execution can also prevent clean scaling between contracts.

Compare fixed-size results or use R multiples, where each result is divided by that trade's predefined initial dollar risk. State the risk definition and retain the dollar drawdown alongside normalized results.

Check how much evidence supports the averages

Twenty trades and two thousand trades can both show a 60% win rate. They do not provide equal information, and a large trade count alone does not establish reliability. Trades clustered in one market regime can repeat similar conditions.

Inspect whether a few unusually large winners dominate expectancy and profit factor. Compare separate time periods and review the losing periods as carefully as the winning ones. There is no universal profit-factor cutoff or trade count that proves a durable advantage.

Parameter selection matters too. TradingView identifies overfitting and selection bias as backtesting risks and describes testing on data kept outside the optimization sample. Preserve those unseen periods rather than repeatedly tuning until they look attractive. TradingView's strategy-testing guidance

Read the report as a whole

Before comparing systematic strategies, confirm:

• The instrument, dates, session, and position-sizing rules match

• The trade list reconciles with the reported metrics

• Commissions, slippage, and fill assumptions are documented

• Drawdown, trade sequence, and open-position exposure are visible

• Results from unseen data are distinguishable from optimization results

Then use win rate for outcome frequency, expectancy for average trade economics, and profit factor for the balance between aggregate gains and losses.

Explore AORDS, a rule-based TradingView indicator for NQ and MNQ. Start with the past performance page, and check the dates and methodology behind any historical figures before comparing them with your own test. Historical results do not guarantee future performance.

Futures trading involves substantial risk of loss and is not suitable for every investor. This article is educational and does not provide personalized investment advice.