AlgoTrading101 - Black Algo Method

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BLACK ALGO METHOD Strategy Development Guide Our Guide For Building Long-Term Profitable Trading Robots

Lucas Liew | [email protected]

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Content 1. 2. 3. 4. 5. 6.

Our Aim and Philosophy Main Components of a Strategy Idea Generation Backtesting Framework Optimisation Framework Portfolio of Robots

Disclaimer: This guide serves as a brief overview of our strategy development process. For more information on 1) 2) 3) 4) 5)

Robot Design and Market Theories Complete Strategy Development Process Coding Data Management Live Trading: Implementation, Performance Analysis and Risk Management

Please go to www.algotrading101.com Log: v2.0 v1.2 v1.1 v1.0 -

May 2015 (Current) Oct 2014 May 2014 May 2012

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Our Aim and Philosophy Aim: To build a long term profitable portfolio of trading robots by exploiting persistent market inefficiencies

Our Philosophy: Design   

robots using low frequency mathematical models using market-prudent ideology Market-Prudency: Ideas that are fundamentally sound from a market and economic point-of-view Mathematical Models: These are strategies tested based on sound statistical methods Low frequency: The trading frequency of our models are low (defined as less than once a minute). Our strategy DOES NOT depend on the speed/computing capacity of our hardware/software

Main Components of a Strategy Entry Rules for entering a trade

Exit Rules for exiting a trade

Position Sizing Rules to determine our trading size Main types:  % of capital risked  Volatility-based  Scaling or reduction  Kelly Criterion  Minimum Sizing (Monte Carlo Worst Case)  Adaptive Sizing (Machine Learning)

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Idea Generation Types of Inefficiencies Macroeconomic news: Non-farm Payroll, FOMC Policy… Fundamentals: Revenues, Earnings release, cash flow… Statistical: Correlation, Cointegration… Market Microstructure: State of the limit-order book, Arbitrage…

Types of Strategies Directional Market Neutral (Paired or hedged positions) Derivatives-based/Complex (Strategies involving complex derivatives: CDS, Options, Swaps etc. Generally this refers to instruments involving non-linear and conditional payouts)

Vetting Ideas Factors to consider: Market-Prudent (Logical from economic, markets point-of-view) Identifiable Persistent inefficiency (as opposed to one-off inefficiencies) Effect from transaction cost Survivorship Bias Future structural change

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Backtesting Framework Aim To gain a better understanding of our Robot.

Checking for Trade Accuracy Verify if the code reflect the correct trading rules

Checking for Robustness Official Definition of Robustness: In economics, robustness is the ability of a financial trading system to remain effective under different markets and different market conditions, or the ability of an economic model to remain valid under different assumptions, parameters and initial conditions. In English: A robot is robust if it can remain effective in changing market conditions

Types of Robustness Period Robustness Seasonal Robustness Timeframe Robustness Instrument Robustness Parameter Robustness Optimisation Robustness Portfolio Robustness

Strategic Period Selection Building robots for specific market conditions that exist in selective periods (Eg. Macro conditions: Easing & Tightening)

Black Swans: Stress Testing Stress testing your robots for black swan events

Grading Your Robot Criteria to pass the backtesting phase: Robot succeeds in capturing intended inefficiency (Reality satisfies expectations) Robot survives reasonably through a range of market conditions Bonus: Robot exceeds expectations in performance and/or robustness

Selecting a Performance Metric Performance metric should comprise of 1) Reward 2) Risk and Consistency

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Optimisation Framework Aim To maximize performance and robustness of our portfolio (of robots) without curve fitting

Objective Function This should be similar to our Backtesting Performance Metric. It has to comprise of 1) Reward 2) Risk & Consistency

Parameter Relevance We only optimise parameters that are relevant to the inefficiency we aim to capture.

Parameter Robustness Definition: A robot is parameter robust if it is able to remain effective across minor adjustments in parameter values. In English: Performance of a robot should not change much if we change its parameter values slightly. We prefer a smooth optimisation/parameter space (resembling a plateau) over a spiky one.

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Walk-Forward Optimisation Definition: Walk forward optimisation is a method used in finance for determining the best parameters to use in a trading strategy. The trading strategy is optimised with in-sample data for a time window in a data series. The remainder of the data are reserved for out-of-sample testing. A small portion of the reserved data following the in-sample data is tested with the results recorded. The in-sample time window is shifted forward by the period covered by the out-of-sample test, and the process repeated. At the end, all of the recorded results are used to assess the trading strategy.1 In English: We optimise our robot using one period (in-sample), and apply the optimised parameters to the next period (Out-of-sample). Repeat. The performance of the robot is collated using all the out-of-sample periods.

Walk-Forward Efficiency In addition to evaluating the out-of-sample performance independently, we need to evaluate them in relation to the in-sample performance. Comparing the in-sample and out-of-sample will allow us to understand the effectiveness of our optimisation. This allows us to identify the element of 1) Curve Fitting and 2) Luck/Chance in our robots. We use a metric called Walk-Forward Efficiency for this.

Consistency of Performance There may be patterns to our out-of-sample performances over time. Increasing or decreasing performance could be due market inefficiencies worsening or dissipating respectively. Anomalies in performance should be analysed with regards to change in market conditions. This will give insights into the strengths and weakness of our robots.

Ranking Optimisation No, this has nothing to doing with SEO. Ranking optimisation entails selecting the parameter set that consistently rank well in the parameter space.

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http://en.wikipedia.org/wiki/Walk_forward_optimization

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Portfolio of Robots Aim To design and build a long term profitable portfolio of trading robots

Portfolio Robustness We seek to design a portfolio of robots that are profitable across varying market conditions

Capital Reallocation There are two methods to reallocate capital across the robots: Common Capital Base All robots trade from the same capital base Capital is Distributed Each robot has a certain capital allocated to them. Amount allocated can be evenly or unevenly weighted. Distribution can happen once at portfolio initiation or regularly at fixed interval. The former has a “rewarding effect” on good robots and the latter has a “punishing” effect.

Multiple Parameter Set We can increase parameter robustness aka reduce variance due to parameter selection by selecting multiple parameter sets for a single robot. These multiple sets act the same way as a portfolio of robots.

Robot Correlation To understand our portfolio’s effectiveness across different market conditions, we need to understand the correlation between the robots’ performance. This understanding will allow us to optimise for the highest reward-to-(risk & consistency) for our portfolio.

Portfolio Optimisation As mentioned, we need to optimise for the highest reward-to-(risk & consistency) for our portfolio. Factors/procedures to consider includes: 1) Portfolio Robustness 2) Robot Performance Correlation 3) Capital Allocation and 4) Portfolio Walk-Forward.

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