Measuring intraday performance after costs

What you will learn

• How to express costs per trade in R and as a share of gross profit
• How to calculate the break-even win rate including costs
• How to break results down by time of day and setup
• How to judge results against the time you spend at the screen

Explanation

Win rate, expectancy, profit factor and maximum drawdown from a journal are taught in "Risk Management". Day trading adds one problem: costs are paid on every trade, and many small trades turn small costs into a large share of results.

Gross and net

Gross result is the profit or loss from the price movement, measured from planned prices or chart prices. Net result is what is left after commissions (broker fees), exchange fees, spread and slippage. Record both for every trade. Taxes are a separate step; in Germany capital gains are generally subject to the flat-rate withholding tax (Abgeltungsteuer), and loss-offsetting rules differ by product and have changed in recent years. This is general orientation only: check with a Steuerberater.

Cost in R and the cost ratio

Cost per trade in R = average cost per trade ÷ 1R

Cost ratio = total costs ÷ gross profit

A cost ratio close to 100% means the market movement you captured is almost fully paid to brokers and liquidity providers.

Break-even win rate with costs

With average winner W (in R), average loser L (in R) and cost per trade c (in R), the net expectancy per trade is p × W − (1 − p) × L − c, where p is the win rate. Setting this to zero gives:

Break-even win rate = (L + c) ÷ (W + L)

Compare your actual win rate with this number. A small margin above break-even can disappear with normal statistical variation.

Breakdowns that matter for day traders

• By time of day: for example first hour, midday, last hour. Many day traders find their results differ strongly by time window.
• By setup: the setups from earlier lessons (opening range, pullback, mean reversion) each need their own statistics.
• MAE and MFE: maximum adverse excursion (how far a trade went against you) and maximum favourable excursion (how far it went in your favour). They show whether stops are too tight or targets too close.
• Per day: share of profitable days and average net R per day.
• Per screen hour: net result divided by hours spent, compared with other uses of your time.

Small samples mislead. Treat fewer than about 30 trades per category as a first impression, not a conclusion.

Worked example

Hypothetical example: 40 trades over 20 days, 4 screen hours per day, 1R = €100. 18 winners average +1.5R gross; 22 losers average −1.0R gross. Average cost per trade €10 (commission €4 plus spread and slippage €6). Taxes excluded.

1. Gross: 18 × 1.5R − 22 × 1.0R = 27R − 22R = +5R = €500.
2. Costs: 40 × €10 = €400. Cost per trade: €10 ÷ €100 = 0.1R.
3. Net: €500 − €400 = €100, or €100 ÷ 40 = €2.50 = 0.025R per trade.
4. Cost ratio: €400 ÷ €500 = 80%.
5. Break-even win rate without costs: 1.0 ÷ 2.5 = 40%. With costs: (1.0 + 0.1) ÷ 2.5 = 44%. Actual: 18 ÷ 40 = 45%. The margin is one percentage point.
6. Per screen hour: €100 ÷ 80 hours = €1.25 per hour.
7. Time breakdown: 22 first-hour trades netted +€300; 18 later trades netted −€200. A first idea to test, not yet proof.

Common mistake

Practice activity

On paper, hypothetical data: 30 trades, 1R = €50. 12 winners average +2.0R gross; 18 losers average −1.0R gross. Average cost €7 per trade.

1. Calculate gross and net results in R and euros.
2. Calculate cost per trade in R and the cost ratio.
3. Calculate the break-even win rate with costs and compare it with the actual win rate.

Key takeaways

• Record gross and net for every trade and review from broker statements.
• Express costs in R; the break-even win rate is (L + c) ÷ (W + L).
• Break results down by time of day, setup and MAE/MFE, but respect small samples.
• Compare net results with the screen time they require.

Next step

The next lesson looks at a cost that statements do not show: fatigue and overtrading during the session.