This Simple Options Strategy Crushes SPY (27% CAGR, 2.4 Sharpe)

This strategy beats the S&P 500 with a

27% annual return, a sharp ratio of 2.4.

And get this, it uses just one

indicator, one. It's called the forward

factor strategy, and you've almost

definitely never heard of it. It trades

forward volatility using a calendar

spread in a way that slashes risk and

draw downs. I've traded the strategy as

a core part of my portfolio for years,

and it's played a major role in growing

that portfolio into the mid7 figures it

is now. Today, I'm giving you

everything. The 19-year back tests, a

real trade example, and even the

calculator so you can run it yourself.

And once you see how simple it is,

you'll wonder why no one's talking about

it. What is the trade? Here's the setup.

It's a simple rule-based trade. Open a

calendar or double calendar spread using

one indicator. Then hold it until right

before the front contract expires.

That's it. No constant tweaking, no

guessing games. The edge comes from a

single indicator we'll define in a

minute. This idea comes from academic

research showing a persistent bias in

the term structure of implied

volatility. Basically, the market's

forward view of volatility is often off,

and that miss can be harvested with

plain vanilla option structures. The

paper that put this on my radar is term

structure forecast of volatility and

options portfolio returns. It finds that

the forward implied volatility built

from two nearby expirations

systematically misestimates what the

shortdated volatility will actually be

later. When that forward V is too low

relative to the front period being long

forward volatility tends to pay. The

good news, you don't need exotic

products to express that. A calendar

spread does the job. So the core premise

is simple. If the market's forward quote

for next period's volatility were

perfectly fair, then when the front

contract expires and the back becomes

the new front, it's one period

volatility should match that earlier

forward quote. It often doesn't. That

gap is big and persistent enough to

build a one rule strategy around it. Our

one indicator will tell us when to put

on the spread, which we then hold until

the front expiry and close. Let's break

this down step by step. Because to

understand forward volatility, we first

need to understand the mechanics behind

how this trade works. Let's start with

an intuitive explanation of forward

volatility. Imagine volatility like

rainfall over time. You have a weather

forecast for January. That's your front

period implied volatility. Then you have

a forecast for January and February

combined. That's your back period

implied volatility. Now, from those two

forecasts, you can actually back out how

much rain is implied just for February.

That February only number is the forward

volatility from January to February.

With options, we use the same concept,

but instead of rainfall, we're dealing

with variance. That's volatility

squared. Why variance? Because variance

over non-over overlapping time periods

add together, just like total rainfall

over separate periods. That's why we

don't average implied volatilities

directly. Instead, we combine variance

times time. So if the near-term expiry

called T1 say around 30 days and the

next one called T2 around 60 days then

the forward volatility between T1 and T2

is the unique number that makes the

total variance line up correctly across

those two windows. Volatility represents

standard deviation per year. And because

the variances over separate time windows

add up just like our rain example we

always do the math in variance space

then square root the result to get back

to volatility. We'll keep this

practical. One formula, then an example.

Don't get scared by the math. This will

be straightforward to follow and you'll

never have to calculate it yourself.

There's a free Python script in the

description, or if you're not into

Python, a free calculator on

oquants.com. Now, let's cover the

mathematical calculation. Sigma 1 equal

the annualized IV for the front period

with time to expiry as T1 in years. We

let sigma 2 be the annualized IV for the

back period with time to expiry T2 in

years. And this also assumes that ts2 is

bigger than t1. We then define the

variances which are just the square of

the volatilities. The annualized forward

variance for the window between t1 and

ts2 is v2 * t2 minus v1 * t1 / t2 minus

t1. Then the forward volatility itself

is simply the square root of the forward

variance. So we get back to volatility

space. Here's a key detail. Time must

always be expressed in years. For

example, 30 days is equal to 30 over 365

and 60 days is equal to 60 over 365.

Let's do an example to make this stick.

Let's say the 30-day implied volatility

is 45%. Let's also say that the 60-day

implied volatility is 35%. Our time in

years is defined as 30 over 365 and 60

over 365. We then compute the variances

by squaring the volatilities. Now we

calculate the forward variance which

gives us a value of 0.0425. 0425 which

is our annualized forward variance

measure. Then we take the square root of

that number to give us 20.6% our

annualized forward volatility between

the 30 and 60-day expirations. Given

those two IV points, the market is

implying about 20.6% volatility for the

30-day period that starts after the

front expires. That means that the

expected volatility of the 60-day option

in 30 days when it becomes a 30-day

option is 20.6% 6% as currently priced

by the market. You can repeat this

process for any pair of expirations. For

example, 30 to 90, 60 to 90, and

anything in between. So that's how we

derive forward volatility. The market's

implied view of volatility for a future

window of time. Now, here's where it all

comes together. This one ratio not only

flips losing trades into winners, it

also tells you exactly when to enter.

It's called the forward factor. All

right, now that we understand forward

volatility, let's talk about the one

indicator that drives the entire trade.

The forward factor or simply FF. The

forward factor tells you how hot the

front period implied volatility is

relative to the forward implied

volatility by the next expiry. In short,

it measures the gap between what's

happening now and what the term

structure says comes next. And that gap,

that imbalance is the edge we're trying

to harvest. For all our calculations and

all the research we'll discuss here, we

use earn implied volatility. That's

implied volatility with the earnings

implied volatility removed. Here's why.

Xarn IV is the option market's implied

volatility after stripping out the extra

premium tied to scheduled earnings

announcements. Why do we use this?

Because earnings cause a temporary

ticker specific spike in near-term IV.

If we didn't remove that spike, we'd be

comparing apples to oranges. a front

period IV that's juiced by earnings

versus a calmer, less effective back

period. By using X earn IV, we put all

expireies and all tickers on an equal

playing field. So, the forward factor

truly reflects term structure imbalance,

not just the result of a one-off event.

Does doing this kill earnings trades?

Not at all. The calendar still work

through earnings when the Xarn FF is

elevated. In those cases, you're trading

the underlying curve misalignment, not

just the earnings event. However,

research from the paper shows that these

calendar spreads tend to perform better

when avoiding earnings altogether. For

simplicity and consistency, it's easiest

for most traders to avoid trading this

strategy through earnings. Meaning, no

earnings events should occur between

your entry and the second expiration. If

that holds true, the Xarn IV will simply

be the same as the regular implied

volatility. The forward factor is

defined as the front period IV minus the

forward IV between the first and second

period divided by the forward IV between

the first and second period. The forward

factor measures the relative difference

between the front period IV and the

forward volatility. As we defined

earlier, it quantifies how the near-term

implied volatility compares to the

implied forward volatility over the

interval T1 to T2. It's expressed as a

percent difference, how much the front

exceeds or legs the forward. So, how do

we interpret this? If FF is greater than

zero, the front IV is greater than the

forward IV. This typically happens when

term structure is in backwardation,

signaling near-term stress or fear.

Historically, this setup has shown very

strong performance with a high sharp

ratio and robust returns. These are our

main focus in this video. If FF is less

than zero, it means the front IV is less

than the forward IV. This typically

occurs when the term structure is in

contango, which is the default state in

comm normal markets. These tend to be

less profitable, though still solid,

just not as strong as the positive FF

setups. So why does this matter? When

the front period is too hot versus the

forward, a long forward volatility

stance tends to pay off. And the beauty

is you can approximate long forward

vault with plain vanilla options. To go

long forward vault, we can use a

calendar spread. Sell the front where IV

is rich and buy the back where IV is

cheaper. So why does a positive FF

capture this mispricing? In panicky

markets, traders rush to buy near-term

protection or speculation. This drives

up the front period IV while the back

period rises much less. When you compute

the forward, the market's implied vault

for the next one period window. It often

comes out much lower than the front. If

that gap is large, it suggests the

market may be underpricing future risk

once the current stress rolls off. Your

calendar spread is positioned to profit

if either one front IV deflates into

expiry where you benefit from theta and

IV crush on the short leg and or two the

back period gains exposure to the

short-term risk as it becomes the new

front where your long leg benefits. The

larger the FF at entry, the bigger the

misalignment you're capturing. Let's

work through a high FF example using our

earlier numbers from the forward

volatility example where we have a

forward V equal to 20.66% 66% and a

front period IV of 45%. We can then

compute the forward factor by

calculating the percent difference

between the front IV and the forward,

giving us a value of 117%.

The market is implying that the next

30-day window after the front expires

will run at 20.66% implied volatility.

Even though today's front period implied

volatility is 45%. That's steep

backwardation. The front is hot and the

forward looks very calm. The bottom

line, a 117% forward factor signals a

major term structure misalignment.

That's exactly the setup where a long

calendar spread is a strong bet on

rising forward volatility. A positive

forward factor means the front is hot.

We lean into long calendars. Let me show

you why that isolates the forward slice.

A calendar spread, whether long or

short, is fundamentally a bet on forward

volatility between two expirations. A

long calendar where we sell the near

period and buy the far period is long

forward vault. A short calendar where we

buy the near period and sell the far

period is short forward vault. Let's

review why this is by revisiting our

variance intuition. Remember variance is

volatility squared and adds through

time. The back period variance is equal

to the front window variance plus the

forward window variance. Whereas the

front period variance is just equal to

the front window variance. By selling

the front and buying the back, you're

effectively removing the front slice and

keeping exposure to only the forward

slice. That's what makes it long forward

volatility position. Flip it and you're

short that forward slice. Let's explore

the long calendar spread that is long

forward volatility. Again, the position

is sell a near expiry, for example, 30

days, and buy a further expiry, for

example, 60 days. The view is that the

market is underestimating volatility in

the next window. We want forward

volatility to rise. So, what helps

forward volatility? If front IV drops,

this raises our forward volatility. The

now cools off. If back IV goes up, that

also raises our forward volatility.

Future risk is repriced higher. Either

way, we gain on the long calendar. What

hurts is if the front IV goes up. This

lowers the forward vault. Signals more

near-term stress. Back IV going down

also hurts the long calendar. This also

lowers our forward vault with the future

risk again repriced lower. Either way,

the long calendar loses. Let's explore

the short calendar spread that is short

forward volatility. So again the

position is to buy the near expiry and

sell the further expiry. The view is

that the market is overstating

volatility in the next window. So

forward volatility should fall. Again

what helps forward volatility to fall is

if front IV goes up that lowers our

forward volatility or if back IV goes

down again lowers our forward

volatility. What hurts is anything that

makes forward volatility go up. So front

IV going down is going to raise our

forward volatility and back IV going up

is also going to raise our forward

volatility. Either way, the calendar

spread loses if that happens. Both long

and short calendars are path dependent.

Large price moves can push your P&L

outside the tent and outside that zone,

your Greek exposures flatten, meaning

your sensitivity to forward volatility

fades. However, the main lever you're

turning is still forward volatility. We

want it to go up for a long calendar and

we want it to go down for a short

calendar. That diminishing exposure

outside the tent is simply a risk we

accept when trading vanilla listed

options instead of OTC products that

trade forward vault or forward variance

directly. Okay, now that we know all

about forward volatility, forward

factors, and calendar spreads, let's hop

into the real back test results and data

setup. We didn't just take the paper's

word for it. We tested the idea with

real investable spreads under realistic

trading frictions. We rebuilt the Ford

volatility logic using options you can

actually trade, then layered in

slippage, commissions, and capacity

limits so the results reflect what a

live trader could actually see. We ran

the tests on two families of time

spreads. The first is the long at the

money call calendar. Buy the further

dated at the money call, sell the near

dated at the money call. Same strike,

different expiries. This is the clean

classic calendar spread. We also

explored the long 35 delta double

calendar. We sell strikes off the front

periods options chain around plus 35

delta for calls and negative 35 delta

for puts. We accepted a plus or minus 5

delta difference if needed to hit the

listed strikes at those same strikes by

the back period options. Effectively,

you're running two calendars at the

wings. Short front long back at the 35

delta call side and short front long

back at the negative 35 delta put side.

This structure taps into skew. You're

typically selling richer front period

skew and owning relatively cheaper back

period skew while widening the profit

tent versus a single at the money

calendar. For both structures, we tested

3D combinations each with a small buffer

so we don't skip trades due to listing

granularity. We tested the 3060, the

3090 DTE, and the 6090 DTE. Again, we

allow a plus or minus 5DTE buffer around

each target. This means anything between

25 and 35 would count as 30. That keeps

the data set realistic when the chain

lists 29 or 32 days instead of a perfect

30 days. We only traded names with a

20-day average option volume above

10,000 contracts per day. This keeps

fills plausible and capacity reasonable.

Tests were run since 2007, covering

nearly 19 years of options history.

Plenty of cycles, panics, lowvall

regimes, enough to make distributional

statements with confidence. slippage and

commissions applied to every trade. We

capped the number of simultaneous

spreads per symbol based on open

interest and trade data. So the back

test can't unrealistically soak up the

book. This avoids the paper only

performance that vanishes when you size

up. We entered when the target DTE and

for double calendar the target deltas

line up within the allowed buffers and

we enter the spread all at once. At

exit, we close the entire position as a

spread on the day the front contract

expires, practically just before close.

So why two structures? The at the money

calendar is the cleanest proxy for being

long forward volatility at the center.

The 35 delta double calendar adds skew

harvesting and a wider payoff geometry,

which can improve win rate and tighten

the link between returns and our

indicator when the forward slice is

mispriced. Bottom line, this is not a

toy simulation. It's a large liquid

multi-deade data set of actual calendar

spreads traded with buffers, frictions,

and capacity constraints and managed

with mechanical entry and exit rules

that match how you'd run the strategy in

the real world. For both data sets, we

have 124,000 observations for the 30 to

60DTE pair, 95,000 observations for the

30 to 90DTE pair, and 88,000

observations for the 60 to 90D pair.

That's more than 300,000 fully

specified, fully costed spread

instances. Enough to make distributional

statements with decent confidence. We

can see from the return distributions

that in all cases, just blindly trading

all of these positions results in a

negative mean return. That's an

important baseline. The calendar is not

a free lunch. If you don't condition on

the term structure, transaction costs

and slippage will grind you down if you

fire every time. We can see that the

3090dt spread has the best negative

return and that all distributions are

positively skewed. That means mostly

losers but some larger winners. We also

notice that the mean return of the

double calendar is better than its at

the money call counterpart and its win

rate is also higher. That tracks with

expectations. We are selling skew in the

front as well as expanding our profit

area by making the profit tent of the

calendar wider. This of course comes at

the cost of more potential commissions

and slippage due to more contracts being

traded. The worst possible return is the

debit paid meaning 100%. But we will see

that some are larger than 100%. And this

is due to slippage and commissions. In

practice, wide markets near expiry and

paying spreads twice entry and exit can

push measured losses a few points past

100. This is a measurement artifact. Not

that the broker took more than your

debit, but slippage and commissions turn

the roundtrip cost into something a tad

larger than the entry debit in some

cases. Looking at the returns group by

quarter and plotted over time, the

strategy looks relatively consistent

with no obvious seasonality. Again, the

3090 being the one that looks like it

has the best return, although still mean

negative. That's encouraging. The edge,

if any, shouldn't depend on a particular

period or quarter. Term structure

dislocations occur throughout the cycle.

This is the hinge. Above simple FF

cutoffs, returns flip positive. I'll

give you the exact thresholds and a dead

simple rule. Looking at the scatter plot

with a regression line of the calendar

spread return versus the forward factor,

we see a clear relationship. The higher

the forward factor, the better the long

calendar spread return. Visibly, we can

see from the graph that a factor at or

above around 0.1 to 0.2 is when returns

start becoming positive. We can also see

that this relationship is stronger with

the double calendar. More evidence that

the double calendar may be better to

trade the strategy along with the

previously established better negative

mean return. This is the signature we

want. Conditioning on forward factor

reframes the trade from let's open

calendar spreads and hope to harvest a

repeatable misalignment. The fact that

the double calendar slopes more steeply

in the scatter signifies that the

relationship between its payoff and

forward factor is slightly stronger.

Looking at a plot of return on the

y-axis and forward factor deciles on the

x-axis, we can clearly see the

relationship that higher positive

forward factors lead to better calendar

spread returns. For the long at the

money call calendar, we see that for the

3060 pair, an FF above 0.14 leads to

positive returns. For the 3090 pair, an

FF above 0.03 leads to positive returns.

And for the 6090 pair, an FF above 0.41

leads to positive returns. For the long

35 delta double calendar, we see for the

3060 pair, an FF above 0.11 leads to

positive returns. For the 3090 pair, an

FF above 0.01 leads to positive returns.

And for the 3090 pair, an FF above 0.14

leads to positive returns. Let's create

a model from these values and see how

this changes our outcome. The simplest

model is only to take trades when the FF

is above the threshold we just saw for

each DTE pairing and structure.

Everything else stays the same. For the

long at the money call calendar, taking

trades only above the FF cutoff, the

3060 now has a positive 9.2% mean

return, a win rate of 47%, and an

average of 50 trades per month. The 3090

now has a positive 4% mean return, a win

rate of 50%, and an average of 138

trades per month. The 6090 now has a

positive 39.7 mean return with a win

rate of 49.5%

but an average of only four trades per

month. For the long double calendar,

taking only trades that are above the FF

cutoff, the 3060 now has a positive 7.9%

mean return with a win rate of 54.6%

and an average of 62 trades per month.

The 3090 now has a positive 3.8% 8% mean

return with a win rate of 56.7%

and an average of 166 trades per month.

The 6090 now has a positive 16.9% mean

return with a win rate of 51.1% and an

average of 36 trades per month. A few

important reads here. Number one,

filtering flips the mean return sign for

every bucket. This is exactly what we

hoped for. Two, the 6090 at the money

call calendar bucket looks massive on

mean return but very sparse on trade

count. And three, the double calendars

generally have slightly higher win rates

at comparable thresholds. All of these

models look good, but to get something

more tradable, I would like to get this

number to a point where the average of

trades per month is around 20. Since

even with a big portfolio, this will be

plenty of trades and we will likely

increase our return by reducing the

number of trades for all except the long

at the money call calendar where we

actually need to raise our number of

trades per month by lowering the

threshold. This will likely reduce our

return but decrease our variance and

give us more trades. For the long at the

money call calendar, adjusting the FF

threshold to around 20 trades per month,

we get the following results. The 3060

with an adjusted FF of 0.23 23 now has a

13.9% mean return, a win rate of 47.8%

and an average of 20 trades per month.

The 3090 with an adjusted FF of 0.23 now

has a 9.3% mean return, a win rate of

50.3% and an average of 19 trades per

month. The 6090 with an adjusted FF of

0.2 now has a 24.7% mean return, a win

rate of 45.4%,

and an average of 21 trades per month.

For the long double calendar, adjusting

the FF threshold to around 20 trades per

month, we get the following results. The

3060 with an adjusted FF of 0.23 now has

a 14.2% mean return, a win rate of 56%,

and an average of 20 trades per month.

The 3090 with an adjusted FF of 0.23

now has a 10.4% mean return, a win rate

of 57.9%, and an average of 19 trades

per month. The 6090 with an adjusted FF

of 0.2 2 now has a 22.6% mean return, a

win rate of 55.8% and an average of 21

trades per month. We can clearly see

that using the forward factor is a

predictive signal of long calendar

spread returns with all time frames and

positions being profitable. We also

notice that the 6090 seems to look best.

Plotting the modeled mean returns group

quarterly, we see a much more

consistently profitable strategy than

just trading them blindly. We also see

that the 6090 time frame still looks

best. Now that we've established

profitability with a solid number of

opportunities, we can move to sizing the

trades and back testing the results.

Let's look at the Kelly fraction for

each strategy combination. Note that

Kelly is typically much larger than what

we actually want to trade. In practice,

we prefer fractional Kelly to manage

draw down convexity and parameter

uncertainty. For the long at the money

call calendar, the 3060 has a Kelly

fraction of 16.1%. The 3090 has a Kelly

fraction of 20.1% and the 6090 has a

Kelly fraction of 18.4%. For the long

double calendar, the 3060 has a Kelly

fraction of 25.7%.

The 3090 has a Kelly fraction of 31.5%

and the 6090 has a Kelly fraction of

29.1%.

Based on the Kelly fraction and models

above, it seems like the double calendar

is the better, more profitable

structure. Further, it looks like the

3090 is the best time frame based on the

Kelly fraction. We can now back test

these strategies to see how they would

have performed historically. The back

test starts with $100,000 and allocates

positions based on the highest forward

factor first until the portfolio is

full. Once again, these positions are

held until right before the front

contract expires, then closed as a

spread. Make sure to check out the link

below where you can download a full

document with the back test results

along with the script to find these

trades yourself. The portfolio

construction details matter. Allocating

to the highest FF concentrates capital

into the most mispriced names. Closing

as a spread just before expiry avoids

pin risk and other expiry headaches. Now

the results. Let's start with the full

Kelly results. For the long at the money

call calendar. The 30 to 60DTE pair

achieves a 21.5% kegger and a 1.58

sharp. The 3090dt pair achieves a 22.6%

kegger and a 1.93 sharp. The 6090dt pair

achieves a 28% kegger and a 1.72 sharp.

Second, let's look at the double

calendar results. The 3060 pair achieves

a 21.9% kegger and a 2.11 sharp. The

3090 pair achieves a 21.5% kegger and a

1.97 sharp. And finally, the 6090 pair

achieves a 27% kegger and 2.27 sharp. At

full Kelly sizing, both structures

perform well across maturities. The

6090day window clearly leads in kegger

for both while the double calendar posts

slightly higher sharp ratios overall.

Now let's look at the halfkelly results.

First the long at the money call

calendar. The 3060 pair achieves a 20.5%

kegger and a 1.92 sharp. The 3090 pair

achieves a 21.9 kegger and 2.06 sharp.

The 6090 pair achieves a 27.8 kegger and

a 1.97 sharp. Second, looking at the

long double calendar, the 3060 achieves

a 21.5 kegger and 2.07 sharp. The 3090

achieves a 21.4% kegger and 2.04 sharp.

And the 6090 achieves a 27% kegger and

2.38 sharp. At half kelly, performance

stays remarkably close to full Kelly

levels while meaningfully improving

sharp ratios, showing that reducing

position size smooths the equity curve

and boosts efficiency. The 6090 DTE pair

continues to dominate. Finally, let's

look at the quarter Kelly results.

First, the long at the money call

calendar. The 3060 pair achieves a 16.9%

kegger and 2.37 sharp. The 3090 pair

achieves a 20% kegger and 2.64 sharp.

The 6090 achieves a 26.7% kegger and 2.4

sharp. Second, let's look at the long

double calendar. The 3060 achieves a 20%

kegger and 2.31 sharp. The 3090 achieves

a 20.3% kegger and 2.34 sharp. And

finally, the 6090 achieves a 26.5%

kegger and 2.42 sharp. At quarter Kelly

sizing, sharp ratios are the highest

across the board. Kegger barely declines

relative to full sizing, but the

stability and efficiency of returns

improve substantially. This highlights

the practical takeaway. Fractional Kelly

sizing, especially around a quarter or

less, delivers the best trade-off

between growth and smoothness. The

headline is straightforward. Both

structures work across all three DT

pairings, and the differences in long

run performance are modest. The 60 to

90day window consistently shows the

strongest blend of kegger and sharp with

the 3060 window being the most sensitive

to structure choice. That pattern

reinforces the central point of this

video. The edge lives in the forward

factor, not in some ultrarecise choice

of spread or deep pairing. The real

advantage is in its ability to identify

cheap, meaning cheap forward v calendar

spreads. You'll also notice something

important about sizing. Moving down from

full kelly to half kelly, then to

quarter Kelly, barely dense kegger, but

sharp improves meaningfully. That's

because smaller bets let you run more

concurrent positions, meaning better

diversification. Smooths the equity

curve and reduces draw down convexity.

The practical takeaway favor fractional

Kelly quarter or less and spread risk

across names rather than leaning hard

into a few. Between the two structures,

the at the money call calendar is the

simplest to enter, manage, and scale. If

you want the cleanest forward vault

proxy with minimal moving parts, start

here. The double calendar often posts a

slightly higher risk adjusted profile in

some windows because you're selling

richer front period skew and owning

relatively cheaper back period skew

which can widen the profit tent. The

trade-off is a touch more complexity

execution risks and more costs. Bottom

line for execution simplicity and cost

reduction just pick the call calendar

since differences between the structures

are marginal and it has simpler

execution and better costs. We know this

works. Let's theorize why this works so

we have confidence in the edge and then

let's go through some easy to follow

rules for actually running this strategy

live. This edge shows up when the term

structure imbalance becomes extreme.

That's exactly what our forward factor

is designed to detect. Let's break down

why this works, where it comes from, and

why it's tradable in practice. Very few

participants explicitly trade forward

volatility. Can you think of many retail

traders or outlets that are advocating

trading forward volatility? Also, most

of the flow in options markets is

concentrated in the near-term expireies

driven by hedging, short-dated

speculation and event trading. The

crowding can push near-term implied

volatility too hot while leaving the

forward slice, the next period implied

by the term structure underpriced. In

periods of backwardation, localized

panic, sector shocks, or heavy

headlines, traders tend to pile into

near-term protection or speculation.

They bid up the front period contracts

but ignore the fair relationship to the

next expiry. That leaves the back period

relatively calm and the forward implied

volatility, the bridge between the two,

gets compressed to levels that don't

make sense once the front rolls off.

High forward factor readings often show

up in mid-li liquidity single names, the

ones large institutional funds will tend

to skip. With sensible liquidity filters

and position caps, these pockets remain

harvestable for the retail trader.

They're big enough for retail and

midsize accounts, but too small to move

the needle for large volatility funds,

which keeps this edge alive. When the

front period IV is bid beyond what the

forward implies is reasonable, or

conversely, when it's lagging too far

below, time spreads naturally serve as

curve balancers, helping bring the term

structure back towards fair value. It's

not about one magical DTE pair. We

tested 3060, 3090, and 6090. And while

6090 often looks best, the Ford factor

is a general detector of term structure

dislocations wherever they appear. If

there's a real mismatch between front

implied volatility and forward implied

volatility, the trade exists whether

it's 714, 1030, 1221, 2035, or anything

in between and beyond. The general

recommendation from experience and

testing is if the forward factor is

above 0.2 when measured using X earn

implied volatilities or avoiding

earnings, the setup is typically

tradable. Obviously, the higher the

better. Here's the key takeaway. Forward

factor isn't tied to a particular

calendar template. It's tied to a

mispricing structure, meaning calendar

versus double calendar or versus

diagonal. Strikes at the money versus

the 35 delta and maturities are just

tools to monetize that same imbalance.

Size smaller and trade more names. Size

at fractional Kelly, typically quarter

or less, materially improve sharp ratios

while only modestly reducing the

long-term return. A practical sizing

guideline is to cap each position at

around 2 to 8% of portfolio equity with

4% being a good default. It's also good

to spread risk across many uncorrelated

tickers. Here is a simple playbook to

follow. One, we want to find liquid

tickers with elevated near-term implied

volatility and term structure and

backwardation. This will lead to high

forward factors. Compute the forward

factor between your chosen expirations

using implied volatilities or avoiding

earnings altogether. If your forward

factor reads at 0.2 or higher, we can go

long the calendar or double calendar

where we're selling the front and buying

the back. We want to size this with

around 2 to 8% of portfolio capital with

4% being a good default. Stick to

quarter Kelly or less. We want to fill

positions starting from the highest

absolute forward factor readings

downward, staying within our overall

portfolio caps. Close as a spread just

before the front expiry, then redeploy

into fresh signals. The forward factor

is simple, intuitive, and rooted in the

time additivity of variance. When the

short-term contract is bid far beyond

what the longerdated contract implies

for the next period, you're being paid

to act as the term structure balancer.

If you'd like this process computed and

screened for you automatically with a

live screener, daily alerts, and

historical forward factors, you can find

it all at Oakquots. All right, that's

enough theory. Let's walk through an

actual trade so you can see exactly how

to find, validate, and place one of

these setups today. Let's take a look at

AES, which tops the list on the Oakquant

screener for forward factors.

Alternatively, you could find similar

setups using your brokerage platform.

Simply screen for tickers with very high

near-term implied volatility versus much

lower implied volatility in further

expirations. That means we're looking

for a backwardated term structure. Now,

we always want to check for earnings. In

this case, earnings are estimated for

October 30th. We could trade through it

using X earn volatilities, but to keep

this example simple and practical, let's

avoid earnings altogether. To do that,

we'll choose expirations that end before

earnings. We'll look to sell the October

17th 10 DTE 14.5 strike at the money

call at an implied volatility of 61.97%.

and we'll look to buy the October 24th

17D 14.5 strike at the money call at an

implied volatility of 52.11%.

Plugging these values into the

calculator, either the downloadable

Python script or the free forward factor

calculator on Oakance, we find a forward

volatility of 33.37%

and a forward factor of around 86%.

That's well above our 20% threshold,

making it a strong trade candidate to

put on the trade. We sell that front

month October 17th 14.5 strike call for

51 and we buy the October 24th 14.5

strike call for 61. That's a net debit

of just 10. This illustrates the real

advantage of using forward factor. It

helps us find extremely cheap calendar

spreads where we're effectively buying

forward volatility at a big discount.

Now, we simply hold until that October

17th expiry date and close the spread

just before the front month expires.

We'll have to see how this one plays

out, but setups like this with high

forward factor and low entry cost are

the bread and butter of this strategy.

If you haven't already, make sure you

either download the script below or

check out.com to access the live

screener, calculators, and community.

Thanks for watching.