PreamblePreface- Systematic trading and investing- Who should read this book- Overview - what is comingIntroduction- 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 frameworkPART ONE: THEORYThe 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 institutionsThe 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-- OverbettingSystematic 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 contrarianPART 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 newsPART THREE: THE FRAMEWORKAn ''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 '+'' 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'' frameworkInstruments - 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 pointsSignals - 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-- SummaryFrom 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- SummaryTotal 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- SummaryRisk 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- SummaryTailoring- 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 capitalPART FOUR: PRACTICEExample 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- ExtensionsExample 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- ExtensionsExample 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- ExtensionsAppendicesAppendix A: ResourcesFurther readingData sourcesBrokers and platformsCodingAppendix B: FormulasBacktesting- Accounting- Costs- Judging the results-- Sharpe ratio-- T- test-- SkewFittingIterative binary grid searchPortfolio 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 portfolioSignals- 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 signalFrom 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.