Robert Carver

Systematic Trading

A unique new method for designing trading and investing systems

• Gebundenes Buch

Produktbeschreibung

This is not just another book with yet another trading system. This is a complete guide to developing your own systems to help you make and execute trading and investing decisions. It is intended for everyone who wishes to systematise their financial decision making, either completely or to some degree.

Produktdetails

• Verlag: Harriman House Publishing
• Seitenzahl: 325
• Erscheinungstermin: 14. September 2015
• Englisch
• Abmessung: 250mm x 175mm x 22mm
• Gewicht: 740g
• ISBN-13: 9780857194459
• ISBN-10: 0857194453
• Artikelnr.: 43241977

Autorenporträt

Robert Carver is Professor of Business Administration at Stonehill College in Easton, Massachusetts, and Adjunct Professor at the International Business School at Brandeis University in Waltham, Massachusetts. At both institutions, he teaches courses on business analytics in addition to general management courses, and he has won teaching awards at both schools. His primary research interest is statistics education. A JMP user since 2006, Carver holds an A.B. in political science from Amherst College in Amherst, Massachusetts, and an M.P.P.and Ph.D. in public policy from the University of Michigan at Ann Arbor.

Inhaltsangabe

Preamble Preface
Systematic trading and investing
Who should read this book
Overview
what is coming Introduction
September 2008: The Billion Dollar Day
January 2009: Why (most) humans make poor traders
The black box is simpler than you think
An open source revolution
An open source systematic framework PART ONE: THEORY The good, the bad and the ugly of systematic trading
Humans should be great traders
in theory
The death of rational economic man
Why we run losses and stop out profits
Introducing a systematic rule for trading
Stick to the rules and don't meddle
Overcoming instinct
why 'contra' instinctive behaviour works
Why subjective 'systems' don't work
Commitment mechanisms
how do we stop ourselves 'meddling'?
Automation
the use of dogs in finance and engineering
Systematic trading in financial institutions The commitment problem does not go away...
. but there are benefits
The ideal systematic trading shop
Two more tricks to reduce meddling
Abstraction
Ignorance
Designing systems to discourage meddling
the three virtues
Trust your system
Understand the limits of your ignorance
Sleep at night: Position size is as important than position sign
When is meddling acceptable?
Unacceptable meddling
Acceptable meddling
Irrationality in trading system development
the three sins
Overfitting
Overtrading
Overbetting Systematic strategies
Why do strategies 'work'?
Risk premia
Frictions and barriers to entry
Information less trading
Returns to effort and cost
Behavioural effects
Pure alpha:skill
What makes a good strategy?
Intuitive
Well motivated
As simple as possible
Can be systematised
Categorising the strategy universe
Static versus Dynamic
Buying and selling insurance
Technical vs fundamental
Fast vs Slow
Directional vs cross sectional
Low versus high leverage
Many positions vs few positions
Crowd following vs contrarian PART TWO: THE TOOLBOX
Model selection, calibration and fitting
The perils of overfitting
Distinguishing dud models from good models
Fitting and overfitting
Four rules for effective fitting
Start with a small number of ideas, not with data
Save real data for a rainy day; use artificial data
Don't fit unless there is a gun to your head
If you must fit to real data, be very, very careful
Portfolio allocation
Anecdote: When smart people make stupid decisions
The bad news: Portfolio optimisation is hard
A simple fix: bootstrapping
'Handcrafting' the weights: The heuristic method
Some problems
The good news PART THREE: THE FRAMEWORK An 'open source' framework for systematic trading and investing
Why an open source framework?
Parallels with open source software
Flexibility
Individual seperable components with well defined interface
Underlying logic exposed â+' easily modified
The elements of the framework
Instruments to trade
One or more signals
Forecasts
combinations of signals
Scaled positions
Portfolios of positions
Total capital scaling
money management
Risk measurement and control
Modifying and extending the 'open source' framework Instruments
the building blocks
Asset classes: Stocks, bonds, ETF's, futures, CFD's ...
The character of different instruments
Portfolios as instruments
Spreads
a special kind of portfolio instrument
Summary
key points Signals
looking under the hood
What is a signal?
What properties should signals have?
A signal is a scaled quantity
But what scale
Why it makes sense to have a unit variance signal
Are jumpy signals okay?
Should we allow signals to be as large as possible?
Three signals in detail
Summary From signals to forecast
Combination
Linear versus non linear
Choosing the weights
we need portfolio optimisation
The diversification multiplier
Mapping function
Binary
Linear
Linear with cutoffs (recommended)
Linear with flat spot
Summary
the default system does... Position scaling
The magic number
Position is signal over standard deviation
Expected volatility
How do we measure expected volatility?
Dangers of low volatility
A rule for low volatility
Summary
the default system position scaling is... Instrument weights
more portfolio allocation
Linear weighting for portfolios
Portfolio optimisation amongst instruments
Which grouping for the heuristic?
Multiple dimensions
Portfolios of spreads
The diversification multiplier, part two
Summary Total capital scaling: Risk appetite and money management
How much can you lose?
A brief primer on the Kelly Criteria
From Sharpe to Kelly
The total capital scaling rule
Low risk target, high worst loss; or high risk target, low worst loss?
Upside ratcheting and downside adjustment
Special cases: Interest paying, living off the proceeds and principal protection
Summary Risk measurement and risk control
Some risk management issues
What is risk and how do we measure it?
Risk that's hard to measure
Two key flavours of system for risk management
Built in risk management
Risk managing at a signal level
Risk managing at an instrument level
System level risk management
Maximum estimated risk
Correlation risk
the perfect storm
Jump risk redux
low volatility
Combining them
the worst case scenario multiplier
The clipping problem
Outside the system
the risk envelope
The risk envelope exists to avoid meddling
Measuring the envelope
Applying the envelope
Buying an insurance against poor performance
Summary Tailoring
Speed of trading
Calculating the damage from trading too quickly
Decomposing and calculating the cost of trading
Applying the brakes
how to slow down
Costs and calibration
Some subtleties
Trading with more or less capital
Trading with more capital
Trading with less capital PART FOUR: PRACTICE Example one: Systematic trading for discretionary traders
Why use a systematic framework with discretionary decisions?
Instruments
Signals
Forecasts
Position scaling
A 'portfolio' of trades
Total capital scaling
Risk control
Worked portfolio example
Extensions Example two: Systematic asset allocation; a long only risk parity portfolio
A risk parity system
Instruments to trade
World's dullest signal and forecast
Position scaling
Portfolio construction
the difficult part
Bootstrap method
Heuristic method
Total capital scaling
Risk control
Worked portfolio example
Extensions Example three: Fully systematic futures trading system
A futures system
Instruments
Signals
Momentum
Carry
Combining signals to get forecasts
Cost estimation
Heuristic
Bootstrapping
Position scaling
A portfolio of instruments
Heuristic
Bootstrap
Total capital scaling
the dangers of easy leverage
Risk measurement and control
Worked portfolio example
Extensions Appendices Appendix A: Resources Further reading Data sources Brokers and platforms Coding Appendix B: Formulas Backtesting
Accounting
Costs
Judging the results
Sharpe ratio
T
test
Skew Fitting Iterative binary grid search Portfolio construction
Markowitz portfolio optimisation
Bootstrapped portfolio optimisation
By hand portfolio optimisation
Means
Costs
specific case of means
Linear portfolio weighting and calculating the diversification effect
Nearest portfolio Signals
Random entry stop loss
Flip flop stop loss
Basic moving average crossover
Exponetial moving average crossover
Raw carry signal for generic asset
Raw carry signal futures contracts
Smoothed carry signal From signal to forecast
Individual signal scaling
Linear signal combination and calculating the diversification effect
Forecast mapping functions
Linear with cap
Binary
Cutoff
Position scaling
Volatility estimation
Minimum volatility rule
Final position calculation
Portfolios of instruments
Total capital scaling
Establishing the initial scalar
The auto ratchet down
The manual ratchet up
Risk measurement and control
Natural risk scalar
Vol shock risk scalar
Correlation shock scalar
Total risk scalar
System performance envelope