2008 Jun 15 - Sun
Mean Reversion Thoughts
While still putting together the code for a trading solution, I've been thinking about
what algorithms to implement for a trading strategy. I have access to live intra-day tick
and quote data, so mean-reversion aka contrarian strategies seem like interesting
candidates.
In the course of manual trading, I've learned that one needs to keep track of a number of
items: current portfolio costs, current holding costs, existing profit/losses, expected
market direction, current market location, external influences. This is a lot to do
manually. Hence the desire to implment tools to automate, or even semi-automate the
process.
A paper by Subramanian Ramamoorthy called
A strategy for stock trading based on multiple models and trading rules
discusses a state space mechanism for determining how to manage the portfolio composition.
Another item he brings to the foreground is a description of the Sharpe Ratio, a ratio which
helps one to keep profit consistent rather than widely dynamic.
Using different terminology, the makers of NeoTicker have a blog with an article called
Counter-Trend Trading with Simple Range Exhaustion System. The key point, which could
be hard to do, is "most counter-trend traders will try to time their entries as close to the
extreme reversal points as possible to maximize the profits and minimize the risk
exposures". Using multiple time frame charts, and
reading the tape, along with some possibly helpful technical analysis tools, it might be
possible to home in on the zones of reversal.
Working my way into a little scalping in the futures, an older article at Interactive
Brokers explains the birth of the
Dow Mini Futures. Some interesting points:
- "try to identify the leader in a group and how its price movement can help us predict
movement in others in the group"
- "we start to trade it by hand so we can get a better understanding of the nuances in
that particular trade"
- "We have a trader and a programmer trade together for a while and then we start the
process of automation. We define our risk parameters and write the rules that we feel give
us an opportunity to be profitable."
- "In our back testing we saw that if we were patient it would be profitable for us. The
hard part was learning to be patient because our other successful trades were very high
frequency. In the mini-sized Dow we may be in and out of 5 to 10 trades in a less than
minute."
- hedge the mini dow with the underlying basket of stocks
- "We don't have scalping targets. We generate a theoretical value and make markets
based purely on that value If we our pricing is accurate and we should naturally be able to
scalp."
- "In the Dow because the bid-ask spread is so tight most of our profits are generated
from trading."
- "he dow has a much tighter spread compared to the mini-spu. Also it is much easier to
watch the stocks in the underlying basket to ascertain their effect on the future."
- "The Russell tends to be trendier than other indices."
[/Trading/AutomatedTrading]
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Adaptive Arrival Price
A keynote lecture at the April 7th Algorithmic Trading Conference in London was by Mr.
Julian Lorenz of ETH Zurich. The abstract for his lecture reads as follows:
Electronic trading of equities and other securities makes heavy use of "arrival price"
algorithms, that balance the market impact cost of rapid execution against the volatility
risk of slow execution. In the standard formulation, mean-variance optimal trading
strategies are static: they donot modify the execution speed in response to price motions
observed during trading. We show that with a more realistic formulation of the mean-variance
tradeoff, with no momentum or mean reversion in the price process, substantial improvements
are possible by using dynamic trading strategies. We develop a technique for computing
optimal dynamic strategies to any desired degree of precision. The asset price process is
observed on a discrete tree with a arbitrary number of levels. We introduce a novel dynamic
programming technique in which the control variables are not only the shares traded at each
time step, but also the maximum expected cost for the remainder of the program; the value
function is the variance ofthe remaining program. The resulting adaptive strategies
are"aggressive-in-the-money": they accelerate the execution when the price moves in the
trader's favor, spending parts of the trading gains to reduce risk. The improvement is
larger for large initial positions.
I think I'll add 'arrival price algorithms' to my key word searches. The above extract
was from a search on 'mean reversion trading system algorithms'.
[/Trading/AutomatedTrading]
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