►
Description
Visit the https://archive.devcon.org/ to gain access to the entire library of Devcon talks with the ease of filtering, playlists, personalized suggestions, decentralized access on Swarm, IPFS and more.
https://archive.devcon.org/archive/watch/6/using-the-graph-and-dune-to-power-on-chain-actions/
Indexing protocols like The Graph and Dune analytics are great for getting insights on application-specific protocols. However, most are used strategic planning rather than being directly actionable. This workshop teaches developers how to make these insights actionable, by building simple programs in 3 steps:
1. Fetching indexed data streams from public data providers
2. Passing streams through models to generate signals
3. Generating and submitting transactions based on those signals
Speaker(s): Christian St.Louis, Mattias Lightstone
Track: Developer Infrastructure
Keywords: Indexing,Data,Development
Follow us: https://twitter.com/efdevcon, https://twitter.com/ethereum
Learn more about devcon: https://www.devcon.org/
Learn more about ethereum: https://ethereum.org/
Devcon is the Ethereum conference for developers, researchers, thinkers, and makers.
Devcon 6 was held in Bogotá, Colombia on Oct 11 - 14, 2022.
Devcon is organized and presented by the Ethereum Foundation, with the support of our sponsors. To find out more, please visit https://ethereum.foundation/
A
A
The
workshop
that
we're
doing
is
using
the
graph
in
Dune
to
power
on
chain
actions.
My
name
is
Christian.
This
is
Matthias
and
we're
from
component.
You
can
find
us
at
component.fi
and
we'll
quickly
go
through
the
agenda,
so
you
can
get
an
idea
of
what
we're
doing
here.
So
first
things.
First
I
just
wanted
to
mention
that
this
will
not
be
a
code
along.
We
wanted
it
to
be,
but
some
of
the
dependencies
required
you
need
pretty
good
Wi-Fi
to
download
them,
so
you'd
have
to
run
to
the
basement.
Come
back
up.
A
We
don't
want
that.
So,
instead,
we'll
just
discuss
some
of
the
things
that
you
would
have
that
you
would
have
coded
so
one
we
want
to
get
to
know
each
other
I
think
that's!
You
know
why
a
lot
of
us
come
here
is
to
find
like
like-minded
individuals.
Two
we're
gonna
do
an
overview
of
the
web3
data
stack.
So
give
you
an
idea
of
like
the
different
layers
of
the
web3
data
stack
and
how
you
can
use
them.
A
Three.
We're
gonna
do
essentially
an
example
that
goes
through
that
you
would
have
coded
along,
but
instead
we'll
just
kind
of
show
you
how
we
did
it
and
can
give
you
the
information.
So
you
can
do
it
yourself
later
and
that'll
be
data
fetching,
so
the
data
fetching
process,
data
processing
and
modeling.
A
So
what
you
do
with
that
data
after
you
fetch
it
and
then
transaction
execution
so
essentially
a
way
to
grab
data
process
it
model
it
and
then
have
that
do
an
on-chain
action,
a
transaction
for
you
and
then
at
the
end,
we'll
do
breakout
sessions
just
at
your
tables
or
whoever
you're
standing
next
to
instead
of
the
instead
of
the
coding
element,
we'll
just
essentially
design
your
own
automation.
Speaking
with
your
table,
but
we'll
walk
you
through
it.
We'll.
A
Cool,
so
this
is
actually
my
email,
I,
don't
know
if
you're
familiar
with
Quest
trade,
that's
a
Canadian
company
for
for
trading
stocks
and
using
derivatives.
I
got
margin
called
a
lot.
As
you
can
see
it
wasn't
that
much
money,
it's
okay,
but
I'm,
not
a
good
Trader,
basically,
and
that's
kind
of
my
motivation
for
for
what
we're
doing,
because
I
got
margin
called
so
much
I
know
I'm
a
bad
Trader.
A
You
know
maybe
automating
things
and
having
you
know
logic
do
it,
for
me
is
probably
a
better
idea.
So
that's
specific
to
D5,
but
of
course,
there's
many
other
ways
that
you
can
take
what
we're
going
to
show
you
and
use
it.
You
know
across
anything
that's
built
on
the
blockchain
okay,
so
we
want
to
start
with
everyone
just
kind
of
talking
to
each
other
at
their
tables.
A
A
You
know
not
in
web
3,
you
can
talk
about
that
too,
a
time
when
maybe
you
suffered
a
lot
of
impermanent
loss
or
you
can,
you
can
say
good
things
too,
I
mean
or
a
time
when
you
should
have
sold
the
nft
at
the
top.
You
know
that
kind
of
thing,
so
yeah
take
five
minutes
and
chat
with
the
folks
around
you.
B
B
So
we'll
start
with
this
nice
little
visual
from
Andrew
Hong
on
Twitter.
This
was
made
about
eight
months
ago
and
so
here's
a
breakdown
of
a
lot
of
the
different
sort
of
layers
of
of
the
web3
data
stack.
You
know
mostly
mostly
relating
to
to
ethereum
and
evm
chains.
So
obviously
you
have
your
your
sort
of
base
layer
where
you're
actually
running
clients,
interacting
with
you
know
the
ethereum
blockchain
state
directly.
Reading
logs
call
data
Etc,
you
have
nodes
as
a
service.
B
B
Some
apis,
you
know,
are
that
data
processing
is
happening
in
the
background
and
you
don't
necessarily
know
so
things.
Those
are
things
like
you
know:
covalent
etherscan,
some.
You
know
manual
tagging
systems
like
through
nanson,
and
then
we
have
a
few
that
we
use
pretty
regularly.
So
we
wanted
to
make
sure
they
made
it
on
there.
B
So
and
a
lot
has
changed
in
the
last
eight
months.
Eight
months
so
people
know
crypto
Stacks,
David
mihai.
They
run
like
crypto
fees
and
L2
fees
and
all
of
those
different
front
ends.
They
built
a
little.
You
know,
data
processing
stack
to
make
it
easier
to
build
front
ends
with
with
subgraph
data.
The
Dune
is
releasing
their
API.
We
mentioned
the
Dune,
but
API
changes
things
pretty
significantly.
We
use
the
massari
standardized
D5
subgraphs,
pretty
much
on
a
daily
basis.
B
They've
created,
you
know
an
open
standard
for
different
kinds
of
of
D5
Primitives
across
chains.
If
you
want
a
little
more
control,
ethl
and
true
blocks
are,
are
pretty
great
they're.
Both
you
know
open
tools
allowing
you
to
index
your
own
index
from
your
own
node,
and
even
the
ectl
data
is
available
in
in
a
bigquery
database.
You
don't
actually
have
to
do
your
own,
your
own
indexing.
If
you
don't
want
to
D5
llama,
you
know
they
pump
out
a
ton
of
new
apis
and
new
protocols.
B
Their
adapters
are
a
pretty
cool
system,
and
then
we
have
another
one
spec
they're.
They
do
allow
you
to
sync
your
own
postgres
database
with
you
know:
typescript
Transformers,
bringing
in
call
data
or
sorry
bringing
in
you
know,
transaction
data
indexing
it
and
then
bringing
it
into
your
own
postgres
database
to
live
beside
your
own
data.
A
Cool,
so
this
is
the
process,
as
I
mentioned,
that
we're
going
to
be
going
through
so
first
thing.
We're
going
to
do
is
we're
going
to
do
data
fetching
and
for
each
of
these
sections
we're
going
to
talk
about
the
alternatives
to
what
we're
doing
so,
to
give
you
like
a
little
bit
of
flavor
so
you're
not
stuck
with
with
with
one
thing
and
then
we'll
give
we'll
run
through
the
example
that
we
actually
did
but
yeah
just
an
overview
of
data,
fetching
processing,
modeling
transaction
execution.
A
A
B
B
So
obviously,
we
went
over
that
data
stack
and
there's
there's
so
much
there.
So
many
different
choices
to
be
made
and,
like
any
other
choice,
you're
making
when
building
a
product,
it's
mostly
about
trade-offs,
engineering
choices.
So
we
we
wanted
to
talk
about
some
considerations
to
keep
in
mind.
There
are
definitely
others,
but
some
big
ones
are
available
Fields.
So
they
actually
have
the
information
that
you
need
to
use.
The
data
format
are
going
to
have
to
Wrangle
all
this
data
to
to
get
it.
B
Have
it
usable
in
your
automations
scope?
Does
it
cover
the
chains
and
protocols
that
you
want
to
use?
Are
you
going
to
have
to
you
know,
merge
different
data
streams
in
order
to
get
information
from
Cross
chain
cross
cross
protocol?
Is
it
event
driven
or
is
it
pulled?
You
know
flexibility.
Can
you
extend
the
model
stability?
Are
they
going
to
change
their
interface
on
you,
while
you're
running
your
automations
and
break
everything,
because
for
some
reason
we
still
don't
Version
Control
apis
everywhere
latency?
B
If
your
your
application
requires
is
real
time,
you
can't
be
operating
an
hour
behind
blocks,
trust,
reliability
and
redundancy
open
openness
of
the
actual
transformation
functions,
so
you
know
understanding
how
that
data
was
was
transformed
from
you
know,
raw
inputs
to
the
data
you're
looking
at
now,
and
then
openness
of
the
data
itself.
You
know
if
you
want
to
be
able
to
inspect
the
internal
State.
Can
you
just
get
a
dump
of
the
database
and
check
it
out?
B
A
We'll
start
with
Dune,
so
very
simple:
query
here:
we're
actually
just
using
a
dune
created
or
user
created
table
which
gives
you
prices
of
different
erc20s
on
layer,
one
ethereum,
and
here
you
can
see
we're
basically
just
pulling
in
the
the
time
which
we're
changing
to
hour,
because
we
want
hourly
hourly
price
and
then
we're
doing
the
average
of
the
price
of
eth
over
that
hour
and
then
we're
doing
the
max
price
and
the
Min
price.
During
that
hour.
For
the
last
30
days,.
A
Cool
so
yeah,
as
we
said,
we
don't
have
access
to
the
Dune
API
right
now.
If
someone
from
Dune
is
here,
please
let
us
know
we'd
love
to
use
it,
but
so
instead
we're
using
this
this
open
source
SDK
that
grabs
essentially
the
the
data
from
a
query.
It's
limiting
in
the
sense
that
you
can't
actually
run
the
query
using
this.
So
you,
basically
you
know
every
time
you
want
to
do.
A
A
Then
yeah,
if
you
just
scroll
up
a
little
bit
to
data
data,
one
two
three
yeah
just
quickly
mention
this:
it's
not
that
significant,
but
we're
basically
just
changing
the
Json
format
that
we
get
in
the
response
and
we're
turning
it
into
a
panda's
data
frame.
Are
people
here
familiar
with
pandas
data
frame,
a
lot
of
nods,
okay,
cool
rows
and
columns
cool
and
then
yeah
yeah.
B
B
In
fact,
this
data
isn't
actually
hourly
data.
So
we
first,
you
know
we
were
using
graphql
to
query
the
graph,
we're
just
looking
for
the
eth
USD
asset
pair,
we're
ordering
by
Tam
timestamp.
So
we
get
the
newest
data,
we're
ordering
descending
and
we're
grabbing
the
first
thousand
prices,
as
well
as
the
timestamps
associated
with
that.
B
B
A
Yeah
and
one
thing
I'll
I'll
mention
about
that
as
well-
is
it's
actually
great
to
use
more
than
one
data
source
just
so
you
can
actually
check
these
things.
You
know
it's
hard
to
know
what
the
the
source
of
Truth
is.
So
you
know
using
different
data
sources,
check
them
against
each
other.
You
know,
maybe
before
you
run,
something
is
always
a
good
idea
and
another
thing
is:
we
didn't
really
mention
this,
but
with
the
data
we're
pulling,
you
know,
you
know.
A
So
before
we
jump
in
back
into
the
code,
there's
just
a
few
considerations,
kind
of
like
just
what
I
said
with
like
there's
so
much
composability
when
it
comes
to
data,
there's
also
infinite
composability.
When
it
comes
to
modeling,
you
know
you
could
use
instead
of
using
a
Time
series
prediction,
you
could
do
conditional
logic
like
you
know.
Maybe
there's
someone
you
follow
that
trades
really
well
and
anytime,
that
they
make
a
trade.
You
want
to
mimic
it
right
away.
A
B
Again,
I
know
we're
focused
on
on
D5
for
that
presentation,
this
presentation,
because
that's
mostly
what
we
interact
with,
but
you
know,
if
you're
really
into
stable
diffusion,
and
you
want
to
see
text
generated
images,
you
could
always
grab.
You
know
text
from
your
lens
protocol
interactions
and
generate
an
image
based
on
it
and
mint
and
nft
on
chain.
If
you
wanted
to
something
like
that,
you
know
that's
really
up
to
you,
because
all
this
computation
is
done
off
chain.
The
the
sky
is
the
limit
foreign.
A
A
A
Not
Financial
advice,
cool
yeah;
these
are
the
results,
so,
as
you
can
see
the
so,
the
red
here
is
the
test
set
and
we
are
basically
you
know.
The
prediction
is
basically
saying
it's
trading
sideways.
Maybe
I
think
it
probably
goes
down
about
10
ten
dollars
roughly.
If
anyone
knows
the
crab
strategy
on
and
squeeze,
this
would
be
a
good
time
to
use
it.
I
guess
yeah.
These
are
just
the
results.
B
Yeah,
so
the
most
web,
three
relevant,
probably
the
most
complicated
Step
in
automation
here-
is
transaction
execution.
So
there's
a
number
of
ways
to
do
it
so
bringing
these
insights
back
on
chain.
So
there's,
there's
tons
of
considerations
to
think
about
which
chains
you're
operating
on
is
the
environment
adversarial.
B
So
are
you
you
know
if
you're
working
in
D5,
then
you're,
probably
looking
out
for
Mev
and
sandwich
attacks,
anybody
trying
to
exploit
your
model
if
you're,
you
know
minting
nfts
for
art
purposes
and
not
for
financial
purposes,
it's
probably
less
of
a
big
deal
that
someone
front
runs
you.
What
kind
of
timeline
are
you
operating
on,
and
you
know
some
things
have
much
larger
delays
than
others,
a
key
management.
Do
you
want
to
manage
your
own
Keys?
What
kind
of
permissions
do
those
keys
have?
Should
your
logic
be
transparent?
B
So
you
know
if
you're
running
a
proprietary
training
algorithm,
you
probably
want
that
to
be
executed
off
chain
and
just
have
the
results
posted
to
the
chain.
But
if
you're
running
something
that
you
want
to
be
transparent,
you
you
would
likely
want
the
the
you
know
computation
to
be
visible.
On
chain,
you
know
what
happened
of
a
transaction
fails
if
it's
or
it's
congested.
These
are
things
that
are
annoying
for
manual
transactions,
but
if
you're
not
taking
them
in
account
when
you're
automating
transactions,
you
can
get
something
stuck
in
the
mempool
forever.
B
You
can
potentially
end
up
not
being
able
to
to
transact.
If
you're,
not,
you
know
replacing
that
transaction
and
replacing
that
nonce.
So
there's
there's
a
bunch
of
options.
There
are
almost
certainly
others
as
well,
and
these
are
not
necessarily
mutually
exclusive,
they're,
just
kind
of
ideas
of
part
of
it.
So
you
know
libraries
that
wrap.
On
top
of
you
know
the
wallet
apis
that
translate
Json,
RPC
ethers,
web3js
web3pi,
which
we're
using
in
this
example.
B
You
can,
you
know,
use
traditional
cloud
service
providers
or
you
know,
tenderly
has
their
new
web
three
actions,
which
is
effectively
lambdas
for
for
for
web
3
interactions
to
be
triggered
by
events,
keeper
networks.
So
you
can
some
keep
your
networks
support
like
more
complex
off-chain
logic.
Some
only
support
triggering
you
know:
transactions
with
a
function
signature.
B
There
are
transaction
execution
Services,
so
something
like
open,
Zeppelin,
Defender
relay
and
inferior
transaction
Services.
They
handle
a
lot
of
these
questions
that
are
kind
of
annoying
for
for
a
small
development
team
to
deal
with
so
things
like
Key
Management.
They
allow
you
to
hook
into
a
KMS
system.
B
They
also
handle
you
know
gas
figuring
out
gas
fees.
So
if
a
transaction
fails
and
the
first
or
a
transaction
is,
is
waiting
too
long
in
the
mempool,
because
it
doesn't
have
a
high
enough
gas
price
assigned,
then
it
will
actually
ramp
those
up
aggressively
and
it
tries
to
figure
out.
You
know
how
much
gas
do
we
need
to
be
able
to
execute
this
transaction
in
a
certain
amount
of
time.
That
being
the
variable
you
set,
you
can
you
need
to
make
the
decision.
B
You
know
if
you're
not
actually
doing
execution
and
you're
just
posting
data
to
the
chain,
then
you
might
want
to
actually
more
like
an
oracle
model.
So
just
posting
data
you're,
not
necessarily
thinking
about
execution,
then
you're
more
thinking
about
launching
execution
that
is
you're
more
thinking
about
how
do
I
get
a
reliable
like
reliable
posting
of
results
to
the
chain.
B
You
can
go
through
a
multi-sig.
If
you
don't
trust
your
automation,
you
could
have
them
as
one
of
the
signers
of
a
you
know,
one
of
two
multisig
where
they,
you
know,
basically
are
queuing
up.
That
transaction,
send
yourself
a
notification
and
then
accept
or
reject.
That
might
be
the
most
thing
that
most
people
are
more
comfortable
with.
You
could
potentially
run
things
run.
The
actual
run
some
of
this
Logic
on
an
L2.
A
B
B
And
for
this
example,
we're
just
using
you
know,
web3
dot
Pi.
This
is
probably
the
thing
that
people
are
actually
the
most
familiar
with
were
fully
we're.
Just
you
know,
setting
some
some
variables,
we're
selecting
our
chain
being
girly,
we're
grabbing
an
RPC
provider.
There's
some
middleware,
that's
required
for
for
Gurley.
Here,
we're
grabbing
a
private
key
from
environment
variables,
which
of
course
means
we
have
to
manage
that
ourselves.
And
then
we
are,
you
know,
executing
transaction.
B
A
B
We
were
supposed
to
show
just
put
putting
it
all
together,
yeah.
It's
again,
you
know
three
steps
here,
really:
oh
grab
graph
and
dune.
You
know
formatted
data
check
with
the
model
whether
we
should
buy
graph
buy
based
on
the
graph
model,
whether
we
should
buy
based
on
the
Dune
model-
and
you
know,
if
both
are
true,
we
are
buying.
A
Yeah
and
one
more
thing
to
say
here
is
so
this
makes
it
look
easy
and
that's
actually
what
we're
working
on
at
component
so
working
on
something
called
blueprint
that
abstracts
away
all
the
stuff
we
just
had
to
do,
although
it's
really
fun
to
learn
how
it's
done,
and
you
can
definitely
do
it
that
way,
and
you
know
to
see
how
it's
done
under
the
hood.
But
what
we
do
with
that
product
is
essentially
try
and
take
you
to
this
step
without
having
to
do
you
know
all
the
other
stuff.
B
This
might
be
a
little
long
with
the
10
minutes,
but
I'm
sure
everyone
who
came
here
has
something
in
webview
that
they're
passionate
about
that
thing
probably
is
generating
quite
a
bit
of
data
and
that's
probably
available
through
one
of
the
many
services
we
we
mentioned
so
we'd
like
to
take
a
little
bit
of
time.
You
know
explore
some
data
sets
think
about
what
kind
of
automation
you
could
build
using
those.
B
A
A
A
D
So
we
think
that
kind
of
defy
investing
has
been
quite
risky
forever,
and
so
the
idea
is
kind
of
a
generalized
pressure
release
valve.
So
if
a
condition
is
met,
whatever
investment
you
have,
if
it
crosses
over
that
threshold
and
pull
pull
out,
you
know
pull
out
to
wallet.
Essentially,
so
you
can
think
of
a
liquidity
pool
you're
invested
in
there.
But
if
the
two
assets
start
deviating
Beyond
a
certain
range
and
you
you
exit
that
position.
A
E
I'll
try
to
make
it
very
quick
yeah.
What
I've
learned
throughout
a
lot
of
oh
worry,
Parable,
loss
experiences
is
that
you
have
to
optimize
your
buys.
That's
a
strategy
for
centralized
Finance
I
mean
centralized
trading,
but
I
it.
It
does
work
for
defy
and
helping,
and
it's
basically
spotting
when
the
price
hits.
E
It
hits
the
threshold
and
it
when,
when
it's
oversold
within
the
Bollinger
Bands,
like
I,
mean
like
in
the
in
the
lower
part
of
the
Bollinger
Bands,
and
you
you
want
to
do
that,
you
want
to
identify
that
buy
signal
throughout
different
temporarily.
These
temporality,
like
the
weekly
time
frame
the
daily
time
frame
and
maybe
not
no
shorter
term
like
the
time
frames,
maybe
the
three
day
time
frame,
I,
don't
know,
but
they
are
identifying,
buy
signals
throughout
different
temporalities
using
the
build
mirror
pens,
those
the
that's,
that's
basically.
F
So
we
discussed
an
idea
of
Auto
rebalancing
crypto
index.
So
let's
say
you
want
to
build
a
simple
index:
that's
like
50,
wrapped
VTC,
50,
wrapped.
If
over
time,
that
balance
is
gonna
be
different.
50
50,
if
the
price
of
Bitcoin
increases,
for
example,
so
maybe
using
some
some
kind
of
feed
like
that
to
calculate
the
balance
of
your
portfolio
and
rebalance
every
hour
week.
Whatever.
A
B
C
Typically,
my
algorithm
is
pretty
good
at,
like
I'm,
identifying
the
Civil
clusters,
who
actually
trust
each
other,
but
like
none
of
the
other,
real
people
actually
trust
any
of
them.
So
maybe
we
can
actually
ingest
this
data
and
then
do
that
number
crunching
identify
those
civil
culture
and
then
his
Dao
could
actually
emit
something
onto
the
contract,
so
I
sort
of
like
those
accounts
as
potential.
Maybe
like
avoid
them.
You
know
things
like
that.
A
Cool.
Thank
you
maybe
one
more.
If
anyone
has
one,
if
not
it's,
okay,
anyone
Keen
to
share
Adrian
you,
you
look
like
you
want
to
share.
You're.
Writing
your
idea
right
on
your
phone.
Okay,
later,
okay,
cool
yeah!
That's
it
so
we'll
just
wrap
things
up
here!
Yeah!
We
don't
have
time
for
that.
I!
Don't
think
so!
We'll
just
go
to
the
last
last
slide.
I
guess
yeah.
B
A
Yeah
and
then
just
to
close
things
out
to
mention
what
we're
doing
again
so
at
component
we're
building
something
called
blueprint
and
all
of
your
ideas,
except
for
maybe
the
last
one
that
sounded
really
cool,
but
it
was
a
little
over
my
head,
but
very,
very
cool,
but
for
the
defy
related
stuff,
at
least,
and
would
love
to
talk
to
you
about
what
you're
doing
to
see.
If
we
can
incorporate
that
in
the
stack.
A
So
if
you
have
any
ideas
that
you
actually
want
to
execute
on
or
you
have
you
know,
friends
that,
like
want
to
build
these
kind
of
data
driven
strategies,
we're
building
tools
that
basically
make
the
entire
process
easy.
So
you
only
have
to
worry
about
the
logic
of
what
you're
building
so
yeah
that
wraps
it
up.
Thank
you.
Everyone,
foreign.