been tracking profit factor for 2 months.
current: 2.1
sounds good. but it’s not good enough.
what is profit factor #
profit factor = gross wins / gross losses
example:
- 10 trades
- 7 wins averaging +$300 = $2,100 total wins
- 3 losses averaging -$280 = -$840 total losses
- profit factor: $2,100 / $840 = 2.5
interpretation:
- < 1.0 = losing strategy (losses > wins)
- 1.0-1.5 = barely profitable
- 1.5-2.0 = decent
- 2.0-3.0 = good
-
3.0 = excellent
my current stats (may + june) #
total trades: 41
wins: 32 (78% win rate) gross wins: $12,480
losses: 9 (22% loss rate) gross losses: $5,940
profit factor: $12,480 / $5,940 = 2.1
net profit: $6,540
why 2.1 isn’t good enough #
problem: commissions and slippage
right now tracking P&L without accounting for:
- broker commissions (~$2.50 per options spread)
- slippage (worse fills during volatile periods)
- data feed costs ($199/month polygon)
estimated costs:
- commissions: 41 trades × $2.50 = $102.50
- slippage: ~$15 per trade avg = $615
- data feeds: $199/month × 2 months = $398
total costs: $1,115.50
adjusted net: $6,540 - $1,115.50 = $5,424.50
adjusted profit factor: still ~2.0 but closer to 1.9
target: profit factor > 2.5 #
why 2.5 minimum:
leaves room for:
- commissions
- slippage
- bad fills
- occasional catastrophic losses
- market regime changes
current gap: need to go from 2.1 → 2.5
three ways to improve #
1. increase win size
current avg win: $390
target avg win: $450
how:
- let winners run longer (currently closing at 50% profit)
- test 60% profit target instead
- risk: giving back gains if position reverses
2. decrease loss size
current avg loss: -$660
target avg loss: -$550
how:
- tighter stops (currently -150% of credit)
- test -125% stops
- risk: more frequent stop outs
3. improve win rate
current: 78%
target: 82%
how:
- stricter entry filters
- vol detection already helping (blocked 30+ trades in 2 months)
- add correlation filters (already doing this)
- test time-of-day filters (avoid first 30 min of market open)
the math #
scenario 1: increase avg win to $450
- 32 wins × $450 = $14,400
- 9 losses × $660 = $5,940
- profit factor: 14,400 / 5,940 = 2.42
scenario 2: decrease avg loss to $550
- 32 wins × $390 = $12,480
- 9 losses × $550 = $4,950
- profit factor: 12,480 / 4,950 = 2.52
scenario 3: improve win rate to 82%
- 34 wins × $390 = $13,260
- 7 losses × $660 = $4,620
- profit factor: 13,260 / 4,620 = 2.87
best approach: combine all three #
conservative targets:
- avg win: $390 → $420 (+8%)
- avg loss: $660 → $600 (-9%)
- win rate: 78% → 80% (+2%)
projected stats (next 41 trades):
- 33 wins × $420 = $13,860
- 8 losses × $600 = $4,800
- profit factor: 13,860 / 4,800 = 2.89
that’s the target.
implementation plan #
week 1-2: test profit targets
- current: close at 50%
- test: close at 55-60%
- measure: does avg win increase without giving back too much?
week 3-4: test stop loss
- current: -150% stop
- test: -140% stop
- measure: does avg loss decrease without increasing stop out frequency?
week 5+: add filters
- current: vol detection only
- add: time-of-day filter (no trades first 30 min)
- measure: does win rate improve?
tracking metrics #
added to grafana dashboard:
- profit factor (real-time)
- avg win size
- avg loss size
- win rate %
- gross wins / gross losses
monitoring daily. will see trends over 2-3 weeks.
trading update #
friday 7/7:
- SPX put spread: +$405
- IWM call spread: +$270
saturday 7/8:
- QQQ put spread: +$315
july total: +$1,830
goal: +$3,500. need: +$1,670 remaining.
on pace.
A. update #
seeing her tonight (saturday).
she’s coming over at 7pm.
probably cooking together (she teaches me, i’m getting better).
still haven’t told her about the trading desk fantasy.
maybe tonight. probably not.
2:40pm saturday. profit factor analysis done. target: 2.5+. implementing improvements next week.
-AK