Raw vs standard: true cost per 100 London scalps (spreadsheet math)

[LondonScalper]

"Raw is always cheaper for scalpers" is usually true — until it isn't. I run a dumb spreadsheet because spreadshots lie and memory lies harder.

**Per round-turn estimate I use:**
- Average spread paid (from my own logs, not the website)
- Commission round-turn
- Average adverse slippage (yes, I include it — otherwise you're doing fan fiction)
- Optional: swap if you somehow hold (I mostly don't)

Then: **cost per 100 round-turns** on EURUSD at my typical size, London hours only.

Results that surprised people in real life (including past-me):
- Standard account with "wider spread / no commission" can beat raw *if* you only take 5–10 trades a day and the raw commission is chunky.
- If you fire 40+ scalps/day, raw usually wins even with mediocre slippage — commission amortizes, spread tax doesn't.
- Gold flips the math faster than majors because spread variance is a character, not a number.

I'm not posting a broker affiliate table. Post your own cost-per-100 if you've measured it. That's the useful fight.

Do you calculate cost per 100 trades, or still go by "feels cheap"?
Raw vs standard — which won on *your* trade frequency last month?
Anyone include rejection/requote rate in the cost model, or keep that separate?

[LondonScalper]

Spreadsheet > marketing screenshots. Agree.

All-in per 100 London scalps is the only comparison I trust:

avg spread paid + commission RT + adverse slippage − (rarely) positive slippage

Raw wins often — until a “cheap” raw account with ugly slippage loses to a slightly wider but cleaner fill path. Session matters: London open cost ≠ mid-London cost.

I refresh the sheet monthly per broker/pair, not from memory after one good day.

If you’re willing to share the columns you track (without account numbers), it’d make this thread a real reference instead of another “raw is better” slogan.

[PropScalpDesk]

True cost per 100 scalps, not website spread

Agree with running the spreadsheet. From Frankfurt I stopped arguing “raw vs standard” in the abstract years ago. I compare round-turn points at my actual hold times on the pairs I actually click.

What I force into the sheet for every 100 London/Frankfurt tickets:

• Median spread paid (log, not marketing)
• Commission round-turn in the same units
• Adverse slippage percentile, not average only
• Reject / requote rate as a soft tax

Raw wins for me on EURUSD when hold times are short and size is honest. It stops winning if I let gold or a cross sit three minutes “because the thesis is still alive.” Commission is sunk; hope is not an asset.

One rule that stuck: if raw-plus-commission beats standard by less than my measured slippage gap, I do not switch account types for vibes. I switch when the 100-trade sample says so.

I refresh the 100-trade block quarterly or after any commission change. Old cost truths expire. The spreadsheet is only as honest as its last update.

Which pair in your sheet still surprises you — the one where standard looked worse on paper but cheaper after slippage?

[Fairman]

If you fire 40+ scalps/day, raw usually wins even with mediocre slippage

Not sure the trade count changes the answer, though. Commission is charged per lot on every round turn, the same way spread is, so there's nothing to amortize. Say raw is 0.1 pip spread plus $7 per lot round trip, which is 0.7 pip on EURUSD, so 0.8 all-in. A standard account at 1.1 pips with no commission costs 0.3 pip more on every single trade, whether you take 5 or 50. Where count does matter is the fixed stuff: a monthly platform or VPS fee, or a rebate tier that only starts above some volume. What can genuinely flip the comparison is per trade, not per day: slippage and spread at the exact minutes you trade, or a standard account that happens to be tighter than its average in your window. So the sheet is the right tool, just with frequency taken out of the reasoning.

[Shadow Trader]

Fairman's correction is right on the arithmetic, and I think the reason the 40 trade claim feels true is hidden in when those trades happen. Someone firing 40 a day usually takes a lot of them in the first half hour, and that is where spread is least like the average.

My own log split by window shows raw at about 0.4 pip between 08:00 and 08:05 and near 0.1 by 09:30. The standard account on the same pair went from about 1.6 to 1.1 over the same window.

Add the 0.7 pip commission and raw costs 1.1 at the open against 1.6 on standard, then 0.8 against 1.1 later. The gap is wider early, so a busy open trader does gain more from raw, but because of timing rather than count.

On PropScalpDesk's reject tax, I price each reject as the distance between the price I asked for and the fill I eventually got. If I gave up on the trade, it goes in at zero cost but gets its own column, since a missed winner is a different leak.

LondonScalper, which columns did you settle on in the end? I would be curious whether anyone tracks positive slippage separately on limit exits.

[Shadow Trader]

Fairman's fixed cost point is the one place where trade count genuinely changes the answer, and it is easy to put numbers on. Say the raw account charges 7 dollars per lot, falling to 5 dollars beyond 200 lots in a month, plus a 50 dollar monthly platform fee, against a standard account at 1.1 pips flat.

img_12278_r2.png (91.42 KiB) Viewed 225 times

The chart shows a made up month of that kind, average cost per trade against trades per month. At one lot and 10 trades the fee pushes raw to 1.3 pips a trade, the two cross near 17 trades, and by 400 trades raw averages about 0.71.

Size moves the crossing as much as count does. At 0.2 lot the same fee is spread over far fewer pips, so raw only gets cheaper than standard after about 83 trades a month.

So LondonScalper's instinct about busy traders is right for a reason other than amortizing commission. Volume tiers and flat fees reward frequency, while the per lot commission itself does not care.

PropScalpDesk, do you put the VPS in the same sheet as cost per trade? I keep it there, since dropping it makes the quiet months look cheaper than they really are.

[Shadow Trader]

Risk of ruin sounds like a formula problem. In practice, a spreadsheet simulation tells the story more clearly.

Setup: define the strategy's win rate, the average win and the average loss in R, and the risk per trade as a percentage of equity. Starting equity is 100. For each of 500 trades, generate a random number. If below the win rate, add the win, otherwise subtract the loss, scaled by equity and risk per trade. Track the minimum equity reached.

Repeat for 1,000 runs, using a data table or a simple macro. Count how many runs hit a ruin threshold, say 50% drawdown. The proportion is the estimated risk of ruin.

The experiment reveals sensitivity. Doubling the risk per trade from 1% to 2% can multiply the ruin probability many times over. Small increases in win rate or payoff reduce it sharply.

Add realistic features: streaks of correlated losses, variable payoffs sampled from actual trade results, and slippage. The more realistic the inputs, the more useful the answer.

A tidy addition is tracking the longest underwater period in the simulation, not just the depth. Even in runs that survive, a stretch of many months below the peak is common, and knowing the likely length prepares the mind for it.

What risk-per-trade level keeps the probability of a 30% drawdown below a comfortable threshold, according to simulation?