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Last Update: October 22, 2026 4 min read


BYauthor-thumberic

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This is part ten of the ASX order book series. The previous post showed that a harvester-style trader can make more per month than a sniper, because volume multiplies a small edge. This post is about the catch.

Every one of those trades pays brokerage. And the spread. And a bit of slippage. For a trader who makes many small trades, those costs aren't a rounding error. In the early months of several journals I read, they were roughly half of gross profit.

The metric nobody looks at

Most people look at net profit. The more useful metric for an active trader is:

text
cost share = commissions / gross profit

If you made $10,000 gross and paid $5,000 in brokerage, your cost share is 50%. Half your edge went to your broker. Change your setup quality slightly for the worse, or your broker slightly for the more expensive, and you're a losing trader.

Across the journals, the cost share told the story of each trader's development better than net profit did:

Stage (synthetic composite)
Cost share
What was going on
First year
40% to 55%
Too many trades, small edge per trade, small size
Specialising
About 25% to 35%
Fewer, better trades. Still a high-turnover style.
Mature
About 10% to 15%
Bigger average win, selective, cheaper execution

The cost share fell not because brokerage got much cheaper, but because the edge per trade grew. That's the real lesson. You can't commission-shop your way to profitability, but you can trade your way to a cost share that no longer matters.

Ticks, price and percentage costs

Here's where the ASX's tick size schedule becomes important. Brokerage is usually charged as a percentage of value (or a flat fee that behaves like one at scale). Price moves happen in ticks. So the cost of a round trip, measured in ticks, depends heavily on the share price.

Assume a round-trip brokerage of 0.10% of value (0.05% each way), which is in the range many active traders pay. Here's the cost in ticks:

Share price
Tick
Tick as % of price
Round-trip cost at 0.10%
Cost in ticks
$0.20
0.5c
2.50%
0.10%
0.04 ticks
$0.80
0.5c
0.63%
0.10%
0.16 ticks
$1.90
0.5c
0.26%
0.10%
0.38 ticks
$2.50
1c
0.40%
0.10%
0.25 ticks
$10.00
1c
0.10%
0.10%
1.0 tick
$40.00
1c
0.025%
0.10%
4.0 ticks

On a $40 stock, you need to make four ticks just to pay for your round trip. On a $0.80 stock, a single tick pays for it six times over. This is why so many scalpers concentrate on lower-priced stocks with large tick sizes relative to price, and why several journals noted that trading high-priced stocks increased their turnover and commissions without improving results.

That's not a rule against large caps. Large caps can offer liquidity and size. But a scalping style that works in a 50 cent stock doesn't automatically work in a $40 stock, because the cost structure is completely different.

The spread is a cost too

If you enter by paying the offer and exit by hitting the bid, you pay the spread on top of brokerage. In a stock where the spread is one tick, a "two tick win" measured from your entry might only be one tick from the mid. Many backtests and many mental models forget this.

Add slippage (the difference between the price you wanted and the price you got) on fast-moving opens, and the true cost of a scalp can be well above the headline brokerage.

Rough arithmetic for one trade

A synthetic example: you buy $50,000 of a $2.00 stock and sell two ticks higher.

Item
Amount
Shares
25,000
Gross profit (2 ticks at 1c)
$500
Brokerage at 0.05% each way
$50.25
Net profit
about $450
Cost share
about 10%

Now the same $50,000, same two-tick win, in a $20 stock:

Item
Amount
Shares
2,500
Gross profit (2 ticks at 1c)
$50
Brokerage at 0.05% each way
$50
Net profit
about $0
Cost share
about 100%

Same skill, same number of ticks, completely different business.

A quick cost calculator

python
def round_trip(price, tick, ticks_won, value, bps_each_way=5, spread_ticks=0.0):
    """Net profit and cost share for a scalp. bps_each_way: 5 = 0.05%."""
    shares = value / price
    gross = shares * tick * (ticks_won - spread_ticks)
    cost = 2 * value * bps_each_way / 10_000
    net = gross - cost
    share = cost / gross if gross > 0 else float("inf")
    return round(net, 2), round(share, 3)

print(round_trip(2.00, 0.01, 2, 50_000))   # cheap in ticks
print(round_trip(20.00, 0.01, 2, 50_000))  # expensive in ticks

Plug in your own broker's rate and the stocks you actually trade. The answer often explains why a strategy that "should" work doesn't.

How the traders reduced the drag

What actually helped, in roughly the order of impact:

  1. Fewer, better trades. By far the biggest lever. Cutting marginal trades removes their costs entirely.
  2. Bigger average win. Holding winners in the best setups means each trade carries its costs more easily.
  3. Trading the right price bands. Matching the style to stocks where a tick is large relative to costs.
  4. Cheaper execution. Active traders talked about the difference between paying around ten basis points or more at some retail brokers and a few basis points with low-cost or direct market access brokers. At high turnover that difference is enormous.
  5. A daily commission budget. Capping the commission you allow yourself to spend each day is a blunt but effective brake on overtrading.

On the CFD side, remember there are financing costs on positions held overnight, and that the economics differ from share trading. Read the product disclosure documents carefully.

The "trading for commissions" day

A phrase that came up more than once in the journals, always with a groan: a day spent "trading for comms." Lots of activity, plenty of small wins and losses, a flat gross, and a real loss after costs. If you see that pattern often, it isn't bad luck. It's a sign that you're trading when your edge isn't present.

The next post looks at the antidote: sizing by setup quality, so that your money is concentrated where your edge actually is.

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Keywords

  • brokerage
  • commission
  • trading costs
  • spread
  • slippage
  • tick size
  • scalping
  • ASX day trading
  • CFD costs
  • DMA broker
  • turnover
  • expectancy
Previous Article
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Oct 23, 2026

Size by Grade, Not by Mood

Most traders size positions by how they feel: bigger after wins, bigger still after losses. The traders whose journals I read converged on grading every setup before entry and sizing from the grade. How to build an A+/A/B grading system, why it adds edge without adding risk, and how to step size up only when you've earned it.

Next Article
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Oct 21, 2026

Win Rate vs Payoff: Two Shapes of a Profitable Trader

A 75% win rate can lose money and a 40% win rate can make a fortune. Expectancy, profit factor and breakeven win rates explained with synthetic examples, plus why the two profitable trader shapes feel completely different to trade and how to improve payoff without wrecking your win rate.

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