2008 Jun 08 - Sun
Stocks & Commodities, 2008/06
In a recent issue of Technical Analysis of Stocks and Commodities, there was an interview
with Tom Busby. A number of his comments struck home with some things I've learned. He
also introduced a few more things about which I should think.
He noted that trading can be a twenty four hour operation. There is always some market
open to trade. The world starts off with the Nikkei and the Hang Seng in the far east. In
Europe, primary markets are CAS, FTSE, DAX and the Swiss. I'd say in today's market the
IPE, with the Brent Crude Futures, is also important. Here in the west, we have the morning
New York market and the afternoon California market.
Busby made mention that 'market open' is an important event. As such, it is important to
know the time each of the markets open. I've been working on an algorithm that selects a
series of instruments, selects a direction and lets the instruments run. I've been
wondering what to set for an exit though. Busby, in the interview,
suggests exiting once a third of ATR
(Average True Range) has been reached. I'm not sure why he would use ATR (which accounts
for any opening gap) rather than just the daily average range. Assuming one gets in
sometime in the open, and exits by the end of the day (in order to eliminate what gaps in
the wrong direction can do to one's portfolio), then using ATR doesn't seem quite right.
Anyway, To set the tone for a trading day, he suggests some benchmark indexes to be
watched. Seven, which he calls the Seven Sisters are:
- S&P
- NASDAQ
- Dow Jones Indexes
- DAX
- Crude Oil
- Long Bonds
- Gold
As for micro-signals, he uses three kinds, with each needing to be in the same direction:
- Volume
- Tick (gainers vs loser)
- Trend
To finish things off, he suggests splitting an entry into three parts:
- Tick Part: the trickiest part of the entry based upon the three variables above
- Trade Part: with confidence building, try to make twice the reward vs risk
- Trend Part: capture the full movement of the day
2008 May 31 - Sat
Decision Trees, Automated Trading, Simulations, and Strategies
A paper called
Stock Picking via Nonsymmetrically Pruned Binary Decision Trees by
Anton V. Andriyashin discusses a method for picking stocks for inclusion in a portfolio. By
integrating technical analysis with binary decision trees, the author indicates that
"BNS clearly outperforms the traditional approach according to the backtesting results and
the
Diebold-Mariano test for statistical significance", where BNS is Best Node Strategy. David
Aronson of Evidence Based Technical Analysis fame may call the use of some the technical
indicators as 'so much snake oil', the paper, at its heart, does describe a methodology for
selecting a potentially profitable portfolio if one can use alternate forms of trading
signals.
Alternate forms of decision tree based automated trading can be found in two papers by
German Creamer and Yoav Freund called
Automated Trading with Boosting and Expert Weighting and
A Boosting Approach for Automated Trading. These represent algorithms used in the
Penn-Lehman Automated Trading Project. Anyway, the two papers get down
and dirty with some of the indiators they use in their trading simulation. Their
bibliography references a number of good sources of information.
In the PLAT paper, here are a few strategies worthy of further investigation:
- Case-based reasoning applied to the parameters of
the SOBI strategy (see text for SOBI description).
- Predictive strategy using money ow (price movement
times volume traded) as a trend indicator.
- Market-maker that positions orders in front of the
nth orders on both books.
- Mixture of a Dynamically Adjusted Market-Maker
which calibrates by recent volatility, and a trendbased
predictive strategy.
- Sells on rising prices, buys on falling prices.
- Trades based on relative spreads in the buy and sell
books, interpreting small standard deviation as a
sign of codence.
- Simple predictive strategy using total volumes in
buy and sell books.
Peter Stone's group has done well with the PLAT simulations. His papers, with this one
as a example,
Two Stock-Trading Agents: Market Making and Technical Analysis have many good
implentable ideas for an automated trading strategy. Outside of the world of finance,
general algorithmic bidding and optimization strategies are described in
The First International Trading Agent Competition: Autonomous Bidding Agents. Another
interesting Peter Stone paper called
Designing Safe, Profitable Automated Stock Trading
Agents Using Evolutionary Algorithms They discuss the concept that common trading
rules have weaknesses under various trading conditions. By identifying the conditions,
and adaptively switching among rules, trading results can be improved. One more Peter Stone
supported effort is the poster:
Safe Strategies for Autonomous Financial Trading Agents:
A Qualitative Multiple-Model Approach.
Through the use of evolutionary reinforcement on data to which us mere mortals have no
access, M.A.H. Dempster has a number of related papers. The bibilographies may be good
sources of further inspiration:
In a sort-of-related paper, Robert Almgren and Julian Lorenz provide an insight into
Adaptive Arrival Price. A couple of extracts from their abstract:
- Electronic trading of equities and other securities makes heavy use
of .arrival price. algorithms, that determine optimal trade schedules
by balancing the market impact cost of rapid execution against
the volatility risk of slow execution.
- We show that with a more realistic formulation of the
mean-variance tradeoff, and even with no momentum or mean reversion
in the price process, substantial improvements are possible
for adaptive strategies that spend trading gains to reduce risk, by
accelerating execution when the price moves in the trader.s favor.
Now for a really un-related paper:
A market-induced mechanism for
stock pinning. The authors suggest that some stock prices can be pinned at strike
prices on option expiration dates. As various market participants cover their positions
with options and the related underlying securities, some interesting market dynamics unfold.
2008 May 30 - Fri
The Joy of Volatility
I initially had this embedded in my follow on article, but I think the information in
this paper bears further scrutiny and testing, in regards to what could be classified as
what I think is called pairs trading. I guess the secret is in the selection of the pairs.
The paper is by
Dempster/Evstigneev/Schenk-Hoppé, and called
The Joy of
Volatility. They take a coin flipping strategy to picking a couple of assets. They
show that the volatility is a positive benefit to portfolio profitability in a dynamic
rebalancing strategy versus a buy and hold mentality. A couple of key quotes though:
Poverty is the inevitable fate of the passive investor.
Consider making an investment according to a simple active management style:
buying or selling assets so as to always maintain an equal investment in both. On average,
wealth will double in 80 periods and grow without limits. This investment style rebalances
wealth according to a constant proportions strategy. It succeeds, where buy-and-hold fails,
because of the volatility of asset returns.
However, as with any investment advice, a word of caution is in order:
Constant proportions strategies do well in the long term but, over short time horizons,
their superior performance cannot be guaranteed!
2008 May 28 - Wed
Put Me To Sleep Reading Material
Someone in some data provider's forum was making mention of doing order flow analysis in
Excel through Interactive Brokers, and the person felt that they weren't getting enough
data. Which is true, Interactive Brokers sends data based upon what is necessary for
someone viewing a screen, not based upon some automated data hungry automaton looking to
crunch full data feeds.
That got me to thinking and to reading more about order flow analysis. This gets in to
market orders, limit orders, bid/ask spreads, order books, market makers, rational traders,
uninformed traders, instantaneous impact of variable sized market orders, as well as whole
raft of other micro-economic activity that comes with high frequency trading.
Marco Avellaneda and Sasha Stoikov and recently released a paper entitled
High-frequency trading in a limit order book, with another version of the
same thing here. They develop some interesting equations on determining a bid/ask
spread in the midst of a moving market, based upon a market maker's inventory and risk
capability. I'm wondering if that is what BATS does for their trading capability.
Karl Ludwig Keiber has a paper called
Price Discovery in the Presence of Boundedly Rational Agents. In the paper, he
discusses some market maker concepts and what they deal with. Momentum as well as mean
reversion are discussed in the context of bid/ask spread and price discovery. There is a
minor discussion regarding adverse selection during a transition from momentum to reversal
trading on page 25 which may be of some value. The cross over between reversal and momentum
is a weakness in my trading.
Bruce Mizrach has a paper called
The next tick on Nasdaq. Although a recently published paper, he uses data from 2002.
The paper goes into some history of market making, limit books, and how Nasdaq grew up.
Some of his interesting observations:
- This paper asks a
surprisingly simple but neglected question: does the entire
order book help predict the next inside quote revision?
- Lillo and Farmer (2004) find
that orders on the
London Stock Exchange follow a long memory process.
- Bouchaud et al. (2002), while
analysing the Paris Bourse,
found a power law for the placement of new limit orders
and a hump shape for the depth in the order book.
- Weber and Rosenow (2005)
find a log linear relationship between signed market order
flows and returns on Island.
- I find,
for example, that the number of bids or offers is more
important than the quoted depth.
- In general, I find that the bids
(offers) away from the inside increase the probability of a
down (up) tick.
- The last result I obtain is that this volatility decreases with
larger market capitalization and the presence of more
market makers.
- Traders call the market makers or ECNs that frequently
appear on the inside market the .ax., and they claim that
taking note of the ax's activity is informativey.
- for example, the advice from the
Daytrading University at http://www.daytrading-university.com/ samplesson4ways.htm.
..Even with the ECN routing that mm.s [market makers] use to hide their order flow, there.s
still plenty of profitable trading to be
had by correctly: (1) Avoiding buying when a major mm/ax is selling (e.g. if you see MSCO
and MLCO both sitting on the inside
ask you probably shouldn.t buy if their bid is three levels outside the market) and (2)
.Shadowing. the ax.s buying/selling behavior, if
you see that all else looks okay, e.g. no suspiciously strong ECN buying/selling on
INCA/ISLD...
- The presence of a particular participant does
not by itself indicate that they are significant contributors
to subsequent quote revisions though.
- Looking more closely at individual participants,
there
are some interesting results. When ARCA takes the inside
bid, the next tick is more likely to be a downtick than an
uptick in 65 of 71 cases.
- When
ARCA takes the inside ask, there is an uptick in 63 of
73 instances
- The effect of specific participants in the small cap
market differs from the large caps. ARCA has a negative
impact from the bid in all 41 cases in which it
is statistically significant.
- A vector autogression can be inverted into its moving
average representation, and one can then compute
impulse responses functions. In our model of trades and
quotes, these have the interpretation of market impact
functions, or the effect on stock returns of an unexpected
buy order arriving into the market.
- It can also be explained in an order driven market
by what
Biais et al. (1995) call the .diagonal effect. in which they
observe that a limit order that improves the inside bid (ask)
is more likely to be followed by another limit order which
increases (decreases) the inside bid (ask). A similar
diagonal effect for trades is present as well. The negative
serial correlation in the small caps suggest that the quote
revision process for that group can be explained without
assuming informed traders,
- As in
many auction designs, additional buy (sell) side interest
makes the next price change more likely to be an uptick
(downtick). Biais et al. (1999) observe this behaviour even
in an environment in which quotes are only indicative.
Similarly, in the period in which quotes are firm, the
authors find that additional depth on one side of the book
helps predict the appearance of additional liquidity on the
same side of the book.
- The number of buyers and sellers, I find,
is almost always more important than quoted depth.
- Aggregate depth, either at the
inside market, or as
a weighted average of the demand curve, is also helpful,
and this information is surprisingly persistent. In general,
the results are more successful for large cap stocks than
small caps.
- Quotes away from the inside
are generally not informative. Large numbers of buyers
(sellers) at tiers away from the best bid (offer) are more
likely to result in a downtick (uptick).
- The model of trades and quotes presented also
produces
dynamic estimates of market impact. The impact of a buy
order can be determined beyond its impact on the current
spread. The estimates appear to vary sensibly with
standard measures of liquidity.
I wonder if the above snippets could be coded as in an expert system.
In
Relation between Bid-Ask Spread,
Impact and Volatility in Order-Driven
Markets by Wyart/Bouchaud/Kockelkoren/Potters/Vettorazzo, the BATS philosophy of
infinitesimal market-making can be expressed in terms of spread and the instantaneous impact
of market orders. They indicate that there is an empirical correlation between the spread
and the volatility per trade. As mentioned in one of the other papers, they confirm that
the main determinant of the bid-ask spread is adverse selection. They also confirm that
volatility comes from trade impact. The paper has an extensive bibliography worth looking
into. There is an interesting corrolary in the conclusion, namely that "when the
volatility
per trade is large, the risk of placing limit orders is large and therefore the
spread widens until limit orders become favorable."
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