It is commonly believed that low frequency strategies require only low frequency data for backtesting. We will show that using low frequency data can lead to dangerously inflated backtest results even for low frequency strategies. Examples will be drawn from a closed end fund strategy, a long-short stock strategy, and a futures strategy. This presentation was part of the QuantCon 2015 Conference hosted by Quantopian. Visit us at: www.quantopian.com.

1. Beware
of
Low
Frequency
Data
Ernest
Chan,
Ph.D.
QTS
Capital
Management,
LLC.

2. • Previously,
researcher
at
IBM
T.
J.
Watson
Lab
in
machine
learning,
researcher/trader
for
Morgan
Stanley,
Credit
Suisse,
and
various
hedge
funds.
• Principal
of
QTS
Capital
Management,
a
commodity
pool
operator
and
trading
advisor.
• Author:
– Quan%ta%ve
Trading:
How
to
Build
Your
Own
Algorithmic
Trading
Business
(Wiley
2009).
– Algorithmic
Trading:
Winning
Strategies
and
Their
Ra%onale
(Wiley
2013).
• Blogger:
epchan.blogspot.com
About
Me
2

3. GIGO
• Garbage
in,
garbage
out
is
well-­‐known
to
programmers.
• Data
integrity
is
crucial
to
backtesVng
trading
strategies.
– Common
problem:
Historical
prices
backtested
weren’t
the
actual
prices
we
could
execute
at.
– Typical
outcome:
backtest
performance
is
greatly
inflated
compared
to
realisVc
historical
performance.

4. Example
1:
CEF
Premium
Reversion
• Patro
et
al
published
a
paper
on
trading
the
mean
reversion
of
closed-­‐end
funds’
(CEF)
premium.
– ssrn.com/abstract=2468061
• CEFs
with
high
premium
(market
cap-­‐NAV)
will
have
negaVve
returns,
while
those
with
steep
discount
will
have
posiVve
returns.
• Rank
CEFs
based
on
%
premium
and
buy
the
bobom
quinVle
and
short
the
top
quinVle
with
monthly
rebalancing.

5. Example
1:
CEF
Premium
Reversion
• Authors
obtained
fund
price
and
shares
outstanding
data
from
CRSP,
and
fund
NAV
data
from
Bloomberg.
• Sharpe
raVo
is
1.5
from
1998-­‐2011.
• I
repeated
their
backtest
also
using
CRSP
prices,
and
fund
NAV
from
Computstat
from
2007-­‐2014.

6. CEF
Premium
Reversion:
closes
2008/01 2010/01 2012/01 2014/01
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Date
CumulativeReturns

7. CEF
Premium
Reversion:
midpoints
2008/01 2010/01 2012/01 2014/01
-0.25
-0.2
-0.15
-0.1
-0.05
0
Date
CumulativeReturns

8. Midpoints
vs
closes
• The
dramaVc
differences
in
performance
due
to
using
closing
prices
vs
midpoint
between
bid
and
ask
prices
at
the
close.
– You
wouldn’t
think
bid
and
ask
prices
maber
for
strategies
that
rebalance
only
monthly!
• Actual
execuVons
will
use
MOC
(Market-­‐on-­‐close)
or
LOC
(Limit-­‐on-­‐close)
orders.
• Actual
execuVon
prices
will
be
the
close
price
(“closing
cross”)
at
primary
exchanges
where
aucVons
take
place,
not
consolidated
closing
prices
which
most
backtests
use.
– Rf.
Prof.
Joel
Hasbrouck
“SecuriVes
Trading”
NYU
Teaching
Notes

9. Consolidated
closes
• Consolidated
closing
price
represents
the
last
execuVon
price
from
any
one
of
>
50
market
centers
at
which
a
stock,
ETF,
or
CEF
can
be
executed.
• ExecuVon
can
take
place
in
a
dark
pool,
ECN,
or
the
primary
exchange.
• If
we
send
a
LMT/MKT
order,
no
guarantee
it
will
be
routed
to
that
parVcular
market
center
and
filled
at
the
consolidated
closing
price.

10. Primary
closes
• Where
can
we
get
historical
primary
exchange
(“aucVon”,
“official”,
“crossing”)
close
prices?
– Buy
from
the
primary
exchanges.
– Subscribe
to
Bloomberg.
– EsVmate
using
midpoints
from
CRSP.
• This
is
what
I
did.
– Use
Vck
data
and
select
the
trades
with
the
Cross
flag*.
*Hat-­‐Vp:
Chris
at
QuantGo.com

11. Example
2:
Opening
gap
• Rank
stocks
based
on
their
returns
from
previous
close
to
today’s
open:
retGap.
• Apply
fundamental
and
technical
filters
e.g.
eliminaVng
stocks
which
just
had
earnings
announcements.
– See
my
book
“Algorithmic
Trading”.
• Buy
10
stocks
with
the
lowest
retGap,
and
short
10
with
the
highest
retGap
at
the
open.
• Exit
at
the
same
day’s
close.
• Backtest
from
2012-­‐2014.
• Live
trading
from
mid
2013-­‐2014.

12. Opening
Gap:
Backtest
vs
Live
2012/01 2013/01 2014/01
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Date
CumulativeReturns
Backtest with 5 bps cost
Live

13. What
happens
at
the
open?
• Backtest
has
already
used
midpoints
at
close:
very
near
the
closing
crosses.
• Backtest
also
included
5
bps
per
trade
transacVon
cost.
• Live
trading
sVll
underperformed
backtest
substanVally.
• Causes:
– Open
prices
also
need
to
use
aucVon
prices.
• Unfortunately
CRSP
does
not
provide
bid/ask
at
open.
– Need
quotes
at
9:28
(Nasdaq
deadline
for
LOO/MOO
orders)
to
generate
trading
signals.

14. Example
3:
Futures
momentum
• Intraday
momentum
strategy
applied
to
various
futures
(E.g.
RB
or
GC).
• Rank
all
trades
(or
quotes)
in
previous
day’s
trading
session.
– Long
if
last
price
above
95th
percenVle.
• Exit
long
if
last
price
below
60th
percenVle.
– Short
if
last
price
below
5th
percenVle.
• Exit
short
if
last
price
above
40th
percenVle.

15. Futures
momentum
• Compare
backtests
based
on
– 1-­‐minute
trades
bars
from
eSignal,
back-­‐adjusted
conVnuous
contracts.
– BBO
quotes
with
1-­‐millisecond
Vmestamps
from
QuantGo.com
/
Algoseek
data,
actual
contracts.
• 1-­‐min
data
shows
that
strategy
trades
only
1
round-­‐trip
a
day:
low
frequency!

16. Futures
momentum
• In
all
cases,
1-­‐ms
data
produce
much
worse
returns
than
1-­‐min
data.
• 1-­‐ms
data
shows
that
strategy
someVmes
flip-­‐
flops:
rapid
changes
of
last
prices
cause
rapid
succession
of
(losing)
trades.

17. Example
4:
Pair
trading
ETFs
• E.g.
ETFs
EWA
(Australian
stock
index)
and
EWC
(Canadian
stock
index)
are
good
candidates
for
mean-­‐reversion
pair
trading.
• Bollinger
band
strategy
applied
to
spread.
• Backtest
on
daily
closes
(aucVon
or
consolidated
prices):
good
results.
• Why
not
live
trade
intraday,
using
Bollinger
bands
to
set
limit
prices?
– Expect
more
trading
opportuniVes
and
more
profits!

18. Pair
trading
ETFs
• Reality:
Intraday
live
trading
using
InteracVve
Brokers
live
Vck
feed
(250ms
bars)
osen
suffers
mysterious
losses
due
to
mysterious
trades.
• Culprit:
Flip-­‐flopping
due
to
order
book
“mini-­‐
flash
crashes”
– Small
change
in
price
on
one
leg
leads
to
large
%
error
in
spread!
• These
flip-­‐flopping
and
losses
disappear
if
we
use
Yahoo
RealTime
(1s
bars).

19. Pair
trading
ETFs
• Moral
of
story:
if
you
want
to
trade
intraday,
must
use
Vck
data
for
backtest,
even
if
holding
period
is
long
(e.g.
hours).
• What
if
we
restrict
live
data
to
1-­‐sec
or
longer
bars?
– This
would
be
arVficial
and
nonsensical:
why
should
we
only
trade
at
…
10:01,
10:02,
10:03,
…
instead
of
…
10:01:01,
10:01:02,
10:01:03,
…?

20. LF
backtest
requires
HF
historical
data
• CEF
monthly
rebalancing
→
need
Vck
data
to
find
closing
crosses
(aucVon)
prices
(unless
you
have
Bloomberg).
• Opening
gap
stocks
strategy
→
need
Vck
data
to
find
NBBO
at
9:28
am
and
opening
crosses.
• Intraday
low-­‐frequency
futures
momentum
strategy
→
need
Vck
data
to
check
for
intra-­‐1-­‐
min-­‐bar
flip-­‐flopping/mini-­‐flash
crashes.
• Intraday
low-­‐frequency
ETF
mean
reversion
pair
trading
→
need
Vck
data
to
check
for
intra-­‐1-­‐sec-­‐
bar
flip-­‐flopping/mini-­‐flash
crashes.

21. Conclusion
• Whether
a
trading
strategy
requires
low
or
high
frequency
historical
data
depends
not
only
on
holding
period,
but
also
on:
– How
execuVon
prices
are
determined.
– How
trading
signals
are
triggered.

22. Thank
you
for
your
Vme!
www.epchan.com
Twiber:
@chanep
Blog:
epchan.blogspot.com