Futures Trading Statistics to Track: Expectancy, Profit Factor, Sharpe Ratio and Drawdown

Win rate hides the money

A strategy that wins 80% of the time sounds safe until you look at the losers. Take a hypothetical: the average win is $100 and the average loss is $500. Per trade, that is (0.80 x $100) minus (0.20 x $500), which comes to negative $20. You feel right most days and still lose money. Flip it: a strategy that wins only 40% of the time, with a $300 average win and a $100 average loss, produces (0.40 x $300) minus (0.60 x $100), or positive $60 per trade. Chasing win rate pushes people to grab small profits and hold losers, and that pattern feels good right up until one loss erases weeks of work.

Expectancy: what a trade is worth on average

Expectancy = (win rate x average win) minus (loss rate x average loss). Hypothetical example: 45% winners averaging $200, 55% losers averaging $100. That is $90 minus $55, or $35 per trade. If you risk $100 per trade, that is 0.35 per dollar risked.

The formula is only as good as its inputs. Use results after commissions and slippage, not before. Averages from a small sample are noisy: 30 trades is not a statistically proven edge, and 50 simulated trades is not live proof, because a simulated fill says nothing about queue position or slippage at the exchange. Treat expectancy as an estimate with wide error bars until you have a lot of trades across different conditions.

Profit factor: gross profit over gross loss

Profit factor = total winning P&L divided by the absolute value of total losing P&L. Using the same hypothetical over 100 trades: 45 wins x $200 is $9,000, 55 losses x $100 is $5,500, so profit factor is 1.64 and net is $3,500, which matches the $35 per trade above. Below 1.0 loses money. Many traders want a clear margin above 1.0 after costs, because a number barely over 1.0 disappears with a little extra slippage.

Be suspicious of very high profit factors, especially in backtests. A handful of outsized winners or an over-fitted rule can produce them. Two quick checks: remove your single best trade and recompute, then recompute with an extra tick of slippage per trade. If the number collapses, you were measuring luck or fragility, not edge.

Sharpe ratio: return relative to volatility

Sharpe = (average return minus risk-free rate) divided by the standard deviation of returns. Traders often drop the risk-free term for short-horizon futures work. A hypothetical with daily returns: mean 0.10% and standard deviation 0.80% gives 0.125 per day. Annualizing with the square root of roughly 252 trading days (about 15.9) gives about 1.98.

Know what it hides. Sharpe treats upside swings as risk, and it assumes returns are roughly symmetric. A strategy that collects small wins and occasionally takes a huge loss can show a flattering Sharpe until the tail event arrives. You also need enough days of data for the standard deviation to mean anything.

Maximum drawdown and Calmar ratio

Maximum drawdown is the largest peak-to-trough drop in equity over your sample. It is the worst you have seen, not the worst that can happen. Calmar = annualized return divided by maximum drawdown. Hypothetically, 35% annual return with a 10% maximum drawdown gives 3.5, while 15% with the same drawdown gives 1.5. Both numbers describe the past, so size your account and risk assuming a deeper drawdown than your history shows. The recovery math in the edge calculator is a quick way to see what a given drawdown demands.

A tracking routine that holds up

1. Export every trade with commissions and fees included, plus the stop and target you planned.
2. Compute expectancy, profit factor, and maximum drawdown on the full sample and on the last 50 and 100 trades.
3. Rerun the numbers without your best trade and with extra slippage per contract (ES ticks are worth $12.50, NQ ticks $5.00, MES $1.25, MNQ $0.50).
4. Split results by condition you can define in advance, such as trending versus range sessions, so you know where the edge lives.
5. Write down a rule for what change in these numbers makes you cut size or stop, before the drawdown starts.

If you trade KPL reference prices, log which level each trade used and when it was entered, so the statistics describe what you actually did rather than what you remember. None of these metrics predicts the next trade. They tell you whether your process has earned the right to more size, and they change as the sample grows.