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.