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PropScalpDesk wrote: Do you weight exit slippage as heavily as entry

Exits surprised me more. Entries are mostly limits so they behave, but stop-outs in a fast move are where the real cost hides.

Cost adjusted R next to theoretical R is exactly the number people don't want to see. Good one. I'd let slippage be a separate input for entry and exit though, exits into a fast market are usually worse.

PTScalper wrote: Log the Entry: Once the trade clears your visual expectancy rules, simply tap the Spacebar.

Logging at the keystroke is quick, but it records the plan rather than the trade. The row is written before the order exists, so the fill price, the real spread at execution and any slippage aren't in it. I'd keep the spacebar entry as the "intent" record and have the script pick up the resulting ticket a moment later, adding the actual open price and time. The difference between the two is the most honest cost figure you'll get.

Writing to Common\Files for other applications is a good choice. One MQL detail: if the external program has the CSV open when the indicator tries to write, FileOpen can fail. Opening with FILE_SHARE_READ, and having the reader do the same, avoids most of those collisions.

It would also be worth adding a column for minutes to the next high-impact event. When you aggregate later, entries taken five minutes before a release usually belong in a separate group from the rest.

Exits hiding the cost is exactly what my journal showed once I split the columns. Limit entries behave; stop-outs in a fast tape are where p90 slippage lives.

A clean reclaim can still be bad business if the exit venue eats the R. Demote on costs, not only on structure.

Separating structural setup quality from execution quality is the key point. A practical habit: record expected entry (the level on the chart) and actual fill for every trade. The difference, averaged by session, shows exactly how much execution costs at different times, which can change the choice of when to trade.

A further useful split is by order type, since market orders and limit orders often have very different execution profiles.

Related currency pairs sometimes react to the same information at slightly different speeds. EURUSD may move a fraction of a second before EURGBP or before another dollar pair, creating a lead-lag relationship that short-term traders try to exploit.

Such effects exist in the data at very short horizons, but they tend to be small, and they shrink quickly as faster participants arbitrage them away. By the time a retail trader sees the move in the leading pair, the lagging pair has often adjusted.

Measuring the effect requires tick data from both pairs with accurate timestamps. Cross-correlation analysis at different lags shows whether one pair consistently leads and by how much. If the lead is shorter than the combined latency of data and execution, no retail edge exists. Where a longer lead appears, perhaps in thinner crosses, testing with realistic costs and delays determines viability. Many apparent lead-lag signals vanish once spread and slippage are included.