Every number on the Mykola dashboard, explained in plain words — each with a worked calculation so you can see exactly how it's derived.
How many round-trip trades you closed.
A trade is one full round trip — a position opened and then closed — no matter how many fills it took to get in and out. It's the sample size behind every other number here: win rate, expectancy and the averages all mean more once you've taken enough trades for them to settle down.
trades = closed round trips Opened and closed AAPL, NVDA, and MSFT = 3 trades
Winning trades vs losing trades.
Trades that finished above break-even count as wins, below as losses. Break-even (scratch) trades belong to neither side and are left out of both. The split is the raw material for Win Rate.
winners = trades with net P&L > 0 losers = trades with net P&L < 0 8 up, 4 down, 1 flat (scratch) = 8 / 4
How often your trades end in profit.
Counts only decided trades — break-even scratches are excluded from both the top and the bottom. On its own it misleads: a 90% win rate can still lose money if the rare losses are huge, so always read it next to Payoff Ratio and Expectancy.
win rate = wins ÷ (wins + losses) × 100 8 wins, 4 losses, 1 scratch (ignored) = 8 ÷ (8 + 4) = 8 ÷ 12 = 66.7%
How often your trading days end green.
Each day's closed trades sum to a single outcome: above break-even the day counts as a win, below as a loss, and flat days sit out — the day-level twin of Win Rate. It smooths single-trade noise: a choppy session of many small trades still counts once, so it reads as consistency rather than volume.
day win % = green days ÷ (green + red days) × 100 12 green, 8 red, 1 flat (ignored) = 12 ÷ 20 = 60%
How many green (or red) days you're riding right now.
Counts consecutive same-outcome trading days back from the latest day in the range — the arrow names the direction. Flat days sit out, like scratches in Win Rate. The two pills beside it show the range's longest winning and losing runs, so you can see whether the current streak is routine or exceptional for you.
streak = same-sign days counted back from the last day last five days: red, green, green, green, green = 4 days ↑
How many wins (or losses) in a row you're on.
Counts consecutive same-outcome trades back from the most recent one — the arrow names the direction. Scratch trades sit out, like flat days in the day streak. The pills beside it show the range's longest winning and losing runs, so the current run reads against your own history.
streak = same-outcome trades counted back from the last last four trades: loss, win, win, win = 3 trades ↑
Your running streak, in days and in trades.
The Current Day Streak and Current Trade Streak side by side: how many same-outcome days and trades you've strung together, each with its direction and the range's longest runs for scale. Reading them together separates one hot session (trade streak up, day streak flat) from a genuinely hot week.
days: red, green, green → 2 days ↑ trades: loss, win, win, win, win → 4 trades ↑
Gross dollars won for every dollar lost.
Add up every winning trade, add up every losing trade, and divide. Above 1.0 the winners out-earn the losers; below 1.0 the strategy bleeds; 1.5–2.0 is a healthy discretionary range. Because it's a ratio of totals, one outsized win can flatter it — glance at Largest Win to sanity-check.
profit factor = gross profit ÷ gross loss wins: +30, +20, +10 → 60 losses: −14, −10 → 24 = 60 ÷ 24 = 2.5
The average dollar result of a trade — your edge per trade.
Net result divided by the number of trades. Positive means each trade, on average, adds money; negative means each one costs you. Multiply it by how many trades you take to project a session or a month. It's already net of commissions.
expectancy = net P&L ÷ trades net P&L = +$36 over 6 trades = 36 ÷ 6 = +$6.00 per trade (≈ +$120 over a 20-trade week)
How big your average win is versus your average loss.
Average winning trade divided by average losing trade. Above 1.0 your wins are bigger than your losses. It pairs with Win Rate: a low win rate is fine if the payoff is high (you win small often is not the only way to make money — you can also win big rarely).
payoff = avg win ÷ avg loss avg win = $20 avg loss = $8 = 20 ÷ 8 = 2.5
Your single best trade.
The most a single trade made. Useful as a reality check on the ratios: if Profit Factor or Avg Win lean heavily on this one trade, the edge is less repeatable than the headline suggests.
largest win = max(net P&L) over winners wins: +30, +20, +10 = max(30, 20, 10) = +$30
Your single best trade against your single worst.
The two extremes as one ratio: above 1.0 your best trade outweighs your worst. It's the outlier check for the averages — if the best trade dwarfs everything else, Profit Factor and Avg Win lean on one lucky print; if the worst dwarfs the best, one bad day can undo many good ones.
extremes ratio = largest win ÷ |largest loss| largest win = +$959.72 largest loss = −$853.45 = 959.72 ÷ 853.45 = 1.12
Your best trading day against your worst.
Each day's closed trades are summed, and the single best day is set against the single worst — the day-level twin of Largest Win / Loss. Above 1.0 your best day outweighs your worst. Read it with Max Drawdown: a worst day near the drawdown means one bad session did the damage; a small worst day means it was a slow bleed instead.
day extremes = best day ÷ |worst day| best day = +$1,404.11 worst day = −$737.59 = 1,404.11 ÷ 737.59 = 1.90
Whether your edge is directional.
Net P&L, trade count and win rate split by direction. A big asymmetry is a finding: if shorts bleed while longs pay, the fix is often as simple as trading the weak side smaller — or not at all.
long: +$812 over 24 trades, 58% win rate short: −$121 over 17 trades, 41% win rate
The typical trade's return on the capital it tied up.
Each trade's net P&L divided by its own entry notional, then averaged with every trade counting equally. Because it ignores size, it can disagree with the account's return: negative Avg ROI while the account grows means the profit comes from the trades you sized up, while the equal-weighted typical trade slightly loses. For fast intraday styles the number is structurally tiny — notional is rented for minutes, not committed.
avg ROI = mean of (net P&L ÷ entry notional × 100) trade A: +$40 on $8,000 → +0.50% trade B: −$50 on $4,000 → −1.25% = (0.50 − 1.25) ÷ 2 = −0.375%
Which ticker pays you and which one bleeds you.
Net P&L per symbol over the range, showing the single best and worst. A persistent bottom name is a candidate to cut; a dominant top name shows where the edge actually lives. Click through the Symbols page for the full table.
AAPL: +$640 over 22 trades (best) TSLA: −$310 over 9 trades (worst)
The average size of a winning trade.
Total profit from winners divided by the number of winners. Compared against Avg Loss it gives the Payoff Ratio — together they describe the shape of your edge.
avg win = gross profit ÷ winners wins: +30, +20, +10 → 60 = 60 ÷ 3 = +$20
Your longest run of consecutive wins.
The most winning trades in a row, in time order. A long streak can flatter your sense of edge and tempt over-sizing — streaks are a normal feature of random sequences, not proof the next one wins.
longest run of consecutive wins W W W L W W (in time order) runs: 3, then 2 = 3
Your average result measured in units of planned risk.
R is profit divided by the risk you planned on a trade (your initial stop distance in dollars). Averaging R across trades makes results comparable regardless of position size — +0.25R per trade is an edge whether you risk $50 or $5,000. Only trades with a recorded planned risk count.
R = net P&L ÷ planned risk trade 1: +$100 risk $100 → +1.0R trade 2: −$50 risk $100 → −0.5R avg R = (1.0 + −0.5) ÷ 2 = +0.25R
What you actually kept, after costs.
Gross trading result minus commissions and fees — the number that hits your account. This is the bottom line every percentage and average on the page ultimately ties back to.
net P&L = gross P&L − commissions gross = +$40 fees = −$4 = 40 − 4 = +$36
Where the account stands after the period's trading.
The balance at the start of the selected range plus the net result of every trade inside it — the equity you end the period with. Needs a balance anchor for the account (set one under Accounts) so the journal knows the starting point; the P&L line is the same Net P&L the rest of the page reports.
end balance = starting balance + net P&L start = $18,913.29 net = +$1,114.34 = $20,027.63
What a typical trading day nets you.
The range's net P&L spread over its trading days. Steadier than per-trade expectancy for planning: multiply by the days you trade a month to project a month. Days without closed trades don't count against it.
avg daily = net P&L ÷ trading days +$1,114.55 over 4 days = +$278.64 per day
How far above and below flat the range swung.
The high- and low-water marks of the running cumulative net P&L — the best and worst the range ever looked mid-flight. A final result far below the peak means giving profits back; a result far above the trough means digging out of holes.
peak / trough = high ÷ |low| of the running P&L peak +$1,910.89, trough −$1,171.64 = 1.63
Trading profit before fees.
The raw result of your entries and exits, before commissions and fees are taken out. Comparing it with Net P&L shows how much trading costs are eating — that gap is the Fee Drag.
gross P&L = Σ (exit − entry) × size sold 100 @ $101 bought @ $100.60 = (101 − 100.60) × 100 = +$40
The fees you paid to trade (shown negative).
Every commission and exchange/regulatory fee charged on the trades, summed. It's the difference between Gross and Net P&L, and scales with how often you trade — a real cost for active styles.
commissions = Σ fees per trade 4 trades × $1.00 each = −$4.00
How much of your gross result fees ate up.
Commissions as a share of the gross trading result. It answers 'how much of what I made went to costs?' — high drag means scalping a thin edge that fees can swallow. Measured against the size of the result, not the account.
fee drag = commissions ÷ |gross P&L| × 100 fees = $4 on a gross of $40 = 4 ÷ 40 = 10%
The most you were up at any point in the day.
Walking your closed trades in time order and keeping a running total, this is the highest that total ever reached. Compared with where you finished, it shows how much open profit you gave back.
peak = max of the running total running: +30, +95, +70 = max(30, 95, 70) = +$95
The most you were down at any point in the day.
The lowest your running total reached as the day's trades closed in sequence. Pairs with Peak P&L to show the full swing you sat through to reach the final number.
trough = min of the running total running: −15, +40, +120 = min(−15, 40, 120) = −$15 (a small early dip)
Total shares or contracts traded.
The sum of every trade's size, regardless of direction. A rough gauge of activity and turnover — useful context for commissions and for spotting days you traded far more (or less) size than usual.
volume = Σ quantity per trade four trades of 100 shares = 100 × 4 = 400
How long you were actively trading — first action to last.
The day's active window: from your first action (the first entry, or the close of a position carried in from an earlier day) to your last exit. Unlike Avg Holding Period it's wall-clock elapsed time, not time in positions — a 6-hour window with twenty 2-minute scalps was mostly spent waiting, not holding.
active = last exit − first action first entry 09:32, last exit 15:34 = 6h 02m
How long you held a trade, on average.
Total time in trades divided by the number of trades, from first entry to final exit. It pins down your style — seconds and minutes is scalping, hours is day trading — and is most revealing split by outcome (see Avg Hold / Win vs / Loss).
avg hold = total time in trades ÷ trades holds of 2m and 4m = (2 + 4) ÷ 2 = 3m
How long you held your winning trades.
Average time in trade across winners only. Read it against Avg Hold / Loss: holding losers longer than winners is the classic 'cut winners early, let losers run' tell.
avg hold / win = time in winners ÷ winners winners held 1m and 3m = (1 + 3) ÷ 2 = 2m
How long you held your losing trades.
Average time in trade across losers only. If this is much larger than Avg Hold / Win, you're likely hoping losers come back instead of taking the stop — a habit worth catching.
avg hold / loss = time in losers ÷ losers one loser held 90s = 90s ÷ 1 = 1m 30s
How long you sit in winners versus losers.
The mean hold time of winning trades divided by the mean hold time of losing trades. Above 1.0 you give winners more room than losers — the 'cut losses, let winners run' shape. Well below 1.0 usually means losers are being nursed while winners get snatched early — the habit Avg Hold / Loss warns about, read as one number.
hold ratio = avg win hold ÷ avg loss hold avg win hold = 11m 32s (692s) avg loss hold = 7m 32s (452s) = 692 ÷ 452 = 1.53
Average profit per share on winning trades.
Profit from winners spread across the shares traded in them — the edge per unit, independent of how big you sized. Comparable across instruments and position sizes.
avg win / share = win profit ÷ winning shares +$200 made on 100 shares = 200 ÷ 100 = +$2.00
Average loss per share on losing trades.
Loss from losers spread across the shares traded in them. Set against Avg Win / Share it's the per-unit version of the Payoff Ratio — are your winners worth more per share than your losers cost?
avg loss / share = loss ÷ losing shares −$100 lost on 100 shares = −100 ÷ 100 = −$1.00
Your per-share edge — what one share wins versus loses.
Average win per share divided by average loss per share. Because both sides are normalised by position size, it reads on entry/exit quality alone: above 1.0 each share gains more on winners than it gives back on losers, regardless of how the positions were sized. Compare with Payoff Ratio to see how much sizing helps or hurts the dollar version.
per-share payoff = avg win/share ÷ avg loss/share avg win/share = $0.15 avg loss/share = $0.22 = 0.15 ÷ 0.22 = 0.68
The typical notional you put on per trade.
Mean entry notional (price × quantity × contract multiplier) across the range's trades, with the largest single position for scale. It's the missing context behind the per-trade percentages: a tiny Avg ROI on a big notional can out-earn a large ROI on a small one, and a Largest far above the average flags the occasional oversized swing.
avg size = Σ entry notionals ÷ trades $8,400 + $9,100 + $7,900 over 3 trades = $8,467
How many of your trading days you actually journaled.
The share of the range's trading days that carry a journal entry. A discipline meter rather than a performance one: the review habit is what turns the rest of these numbers into changed behaviour.
coverage = journaled days ÷ trading days × 100 3 of 4 days journaled = 75%
Your biggest drop from a high-water mark — not the lowest point.
Walk the running total in time order; the drawdown at any moment is how far you are below the highest point reached so far. Max Drawdown is the largest of those drops. It measures the worst losing run you had to sit through, which is why it can exceed the day's lowest balance.
drawdown = running peak − running total running: +60, +180, +120, +240 peak before the dip = +180 deepest drop = 180 − 120 = $60
The typical dip below your equity high-water mark.
Each drawdown episode — from a new equity peak until that peak is regained — is measured at its deepest point, and this averages those depths. Read it against Max Drawdown: the average is the routine dip, the max is the outlier. The Current line is the open episode you're still in (zero when equity sits at its high-water mark).
avg drawdown = Σ episode depths ÷ episodes episodes: −$300, −$1,200, −$1,000 = 2,500 ÷ 3 = −$833.33
One score for how tradeable your results are.
Van Tharp's System Quality Number: per-trade expectancy divided by the per-trade P&L spread, scaled by the square root of the sample size. It rewards steady edges and punishes lumpy ones — below ~1.6 is poor, 2–2.5 average, above 3 excellent. Small samples flatter it; trust it more as the trade count grows.
SQN = expectancy ÷ std dev × √trades $27.18 ÷ $214.17 × √41 = 0.81
How much of the range you spent below your equity peak.
The share of trading days that closed beneath the running high-water mark of cumulative P&L — the duration leg of the drawdown family (Max Drawdown is the depth, Average Drawdown the routine dip, this is how long the holes last). Long stretches underwater are where discipline erodes, even when the depth is modest.
underwater = days below the running P&L peak ÷ days 8 of 20 days closed under the peak = 40% (longest stretch: 5 days)
That worst drop, as a share of the capital you started with.
Max Drawdown divided by the account balance at the start of the day. It puts the drop in proportion to your capital — a $300 dip is 6% of a $5,000 account but 0.6% of a $50,000 one. Measuring against starting capital (rather than the day's running peak) keeps it meaningful even when the worst drawdown comes before the day ever turns green. Shown only when a starting balance is known.
= max drawdown ÷ starting balance × 100 $300 drop on a $5,000 account = 300 ÷ 5,000 = 6%
How much you made for each dollar of drawdown.
Net P&L divided by Max Drawdown — profit earned per unit of pain endured. Higher is better: it says the return was worth the worst slump along the way. Below 1.0 means you made less than your deepest drawdown.
recovery = net P&L ÷ max drawdown net P&L = +$240 max drawdown = $60 = 240 ÷ 60 = 4.0
How much your trade results swing around the average.
The standard deviation of per-trade net P&L — a measure of consistency. Low means tightly clustered, predictable results; high means wild swings. Two traders with the same expectancy but different std dev have very different ride quality.
std dev = √(avg squared distance from mean) trades: +20, +10 (mean 15) = √(((+5)² + (−5)²) ÷ 2) = √25 = $5
Your single worst trade.
The most a single trade lost. Check it against Avg Loss: one loss far bigger than the rest usually means a stop was skipped or widened — the kind of tail risk that quietly undoes a good week.
largest loss = min(net P&L) over losers losses: −14, −12, −4 = min(−14, −12, −4) = −$14
The average size of a losing trade.
Total loss from losers divided by the number of losers. Set against Avg Win it gives the Payoff Ratio; keeping it small and consistent is what stops the rare bad trade from dominating.
avg loss = gross loss ÷ losers losses: −8, −12, −4 → 24 = 24 ÷ 3 = −$8
Your longest run of consecutive losses.
The most losing trades in a row, in time order. This is the stretch your position sizing and your psychology have to survive — sizing so a normal losing streak can't wreck the account is the whole game.
longest run of consecutive losses L W L L L W (in time order) runs: 1, then 3 = 3
Your worst trade measured against its planned risk.
The most negative R-multiple — a loss expressed in units of the risk you planned. −1R means you lost exactly what you intended to risk; worse than −1R means a stop was blown through and the loss ran past plan, the single most important thing to catch.
R = net P&L ÷ planned risk lost $100 on a planned risk of $100 = −100 ÷ 100 = −1.0R (a clean stop-out, right on plan)
Whether the trade was long or short.
Long means you bought first and profit when price rises; short means you sold first and profit when price falls. It sets the sign convention for the trade's P&L and price-move figures.
bought first, sold higher buy 100 @ $100 → sell @ $101 = Long
How many shares or contracts the position was.
The size of the position — the multiplier on every per-share figure. Bigger size amplifies both the win and the loss, which is why risk is managed through size, not just stop distance.
quantity = shares (or contracts) traded bought and sold 100 shares = 100
The average price you opened at.
If the entry filled in pieces, this is the size-weighted average of those fills. It's the baseline the exit is measured against to get the trade's profit.
entry = Σ(price × size) ÷ total size 60 @ $100.00 + 40 @ $100.15 = (6,000 + 4,006) ÷ 100 = $100.06
The average price you closed at.
The size-weighted average of your closing fills. The gap between entry and exit, times size, is the gross result of the trade.
exit = Σ(price × size) ÷ total size sold 100 @ $101.50 = $101.50
The trade's return as a percent of what it tied up.
Net profit divided by the cost basis (entry price × size). It normalises the result so a $150 win on a $10,000 position and on a $1,000 one aren't mistaken for the same thing.
ROI = net P&L ÷ cost basis × 100 +$150 on 100 @ $100 (= $10,000) = 150 ÷ 10,000 = 1.5%
Profit or loss per share on this trade.
The trade's net result spread over its size — the edge per unit, free of how big you went. Handy for comparing trades of different sizes on equal footing.
per share = net P&L ÷ quantity +$150 on 100 shares = 150 ÷ 100 = +$1.50
The trade's result in units of the risk you planned.
Profit divided by the dollars you planned to risk (your initial stop distance × size). +2R means you made twice what you were willing to lose; −1R is a textbook stop-out. Thinking in R keeps results comparable no matter the position size.
R = net P&L ÷ planned risk made $300, planned to risk $100 = 300 ÷ 100 = +3R
How long the trade was open.
Elapsed time from the first entry fill to the final exit fill. Aggregated across trades it becomes the Avg Holding Period; on a single trade it's a check that the hold matched the plan.
hold = exit time − entry time opened 09:30, closed 15:45 = 6h 15m
The dollar size of the position.
Price × quantity × contract multiplier — the total market value the trade controlled. It's the exposure behind the trade, which can dwarf the margin actually posted to open it.
notional = price × quantity × multiplier 100 shares @ $100 (multiplier 1) = 100 × 100 × 1 = $10,000
How many fills made up the round trip.
The count of individual order fills that built and closed the position. More fills than expected can mean a large order was sliced up, or a position was scaled in and out — and usually means more commission.
executions = fills to enter + fills to exit 1 buy fill + 1 sell fill = 2
The best the trade ever looked while open.
The peak unrealized profit the position reached before you closed it — how much was on the table at the high-water mark. A large MFE next to a small final result means you gave a lot back; pairing it with the exit is how you judge whether you left money behind. Measured from your own executed fill prices (no intraday tick feed), so it samples the path at fills rather than every print.
MFE = peak of the running P&L long 200 sh, ran +$1.55/sh before fading = +$310 (+$1.55/sh)
The worst the trade ever looked while open.
The deepest unrealized loss the position sat through before it closed — how far underwater it went. A winning trade with a large MAE was a near-miss that happened to work; a recurring large MAE is a sign your stops or entries need work. Measured from your own executed fill prices (no intraday tick feed), so it samples the path at fills.
MAE = trough of the running P&L short 200 sh, ran −$0.60/sh against you = −$120 (−$0.60/sh)
How much of the peak (MFE) you actually kept.
Net P&L divided by MFE. 100% means you exited at the high-water mark; a low number means most of the open profit was given back before you closed; a negative number means the run-up fully reversed into a loss. The single best read on exit timing.
capture = net ÷ MFE kept $80 of a $100 peak = 80%
Profit left on the table from the high-water mark.
MFE minus Net P&L — the dollars the position was up at its peak but didn't keep. Zero means you nailed the exit; a large give-back next to a small win is the same story Capture tells, in dollars.
give-back = MFE − net peaked at $310, closed +$208 = $102
Share of the hold the trade spent in the red.
The fraction of the holding period the running P&L was below zero, time-weighted across the fills. A winner that was underwater most of the way was a near-miss; a low number means the trade worked from the start. Sampled at fill times (no intraday feed).
underwater = time below 0 ÷ total hold 10 min red out of a 30 min hold = 33% of hold
Average shares per execution.
Quantity divided by the number of fills — how finely the order was sliced. Tiny average fills on a small position usually mean extra commission for little benefit.
avg fill = quantity ÷ executions 200 shares over 2 fills = 100 sh
The price move in exchange tick increments (futures).
A tick is the smallest price step a futures contract trades in. Measuring the move in ticks, signed by direction, is how futures traders talk about a trade independent of dollar size — and each tick has a fixed dollar value per contract.
ticks = price move ÷ tick size ES moved 2.00 points, tick = 0.25 = 2.00 ÷ 0.25 = +8 ticks
The raw price-point move (futures).
The plain price change from entry to exit, signed by direction — ticks before they're divided into increments. Multiplied by the contract's point value and size, it gives the trade's dollar result.
points = exit − entry (signed by side) ES long, $4,800.00 → $4,802.00 = 4,802 − 4,800 = +2.00 points
The price move in pips (forex).
A pip is the standard small unit of an FX rate — 0.0001 for most pairs, 0.01 for JPY pairs. Measuring the move in pips, signed by direction, is the forex equivalent of ticks: size-independent and comparable across trades.
pips = price move ÷ pip size EUR/USD 1.1000 → 1.1015, pip = 0.0001 = 0.0015 ÷ 0.0001 = +15 pips