- Currency pairs: A Look Through the Fractal Dimension
- Exploiting Order Flow for the Discretionary Quant - Part 1
- Exploiting Order Flow for the Discretionary Quant - Part 2
- Simple Mechanical Trend Following in the Forex Market
- Is a Reward to Risk Ratio Inherently Better Than Another?
- Robots Aren’t What They’re Cracked Up To Be
- Creating a Trading System Using Neural Networks
- Function Based Trailing Stop Mechanisms
- The Seven Deadly Sins of Automated Trading
- Exploiting the Volume Profile
- Building Robust FX Trading Systems
- Know Your Currencies
- Automating FX Trading Strategies
- Grammatical evolution
- Identifying an Edge
- Interview with Salvatore Sivieri
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Exploiting Order Flow for the Discretionary Quant
Keys to the Minimum Viable Strategy
In “Part One”, published in the Jul-Sep 2016 issue of FX Trader Magazine, we outlined the behaviour of price inefficiencies around market structure for GBPUSD, including a basic quantitative model supporting an agile, proof of concept approach that delivers a minimum viable strategy within a continuous improvement framework.
Part Two explores the concepts of Excursion and Expectancy within this model, why they are key to delivering the minimum viable strategy, and how an automation roadmap can accelerate testing and, with caveats, deliver a live trading platform.
The data is not enough. Where does information get added to data, and value added to information?
Using Excursion Analysis – What is Normal Behaviour?
Normal and abnormal adverse price behaviour is a concept popularised by John Sweeney, and it’s important for newer traders who struggle with ‘where to get out’ when price goes against them. Maximum Adverse Excursion (MAE) describes this. Similarly, MFE (Maximum Favourable Excursion) describes normal movement into profit. You need a large enough sample to be confident in both.
The trick is to understand what normal is for the price behaviour exploited by the strategy, so that we can determine patterns of adverse price behaviour that can inform the trading model, e.g. how far price normally travels adversely for all manageable trades.
In Part One, we stated that the ‘definition of done’ for the strategy test is that it will be possible to experiment with expectancy settings based on normal adverse price movements, and to look at best and worse-case scenarios, including the use of a session filter to drill-down into session characteristics for this strategy. The excursion analysis will carry forward into the expectancy analysis.
During the proof-of-concept, adverse price movement was tested to quantify adverse price movement around discrete parameters – 10 to 25 pip stops.
The results show that the frequency of the initial, 10-15 pip adverse price movement for manageable trades supports the idea that, for this strategy, a successful trade should move favourably from the outset. This is clearly a reflection of the price delivery mechanics that underpin the strategy – the market microstructure ‘steel threads’.
This characteristic is present for stats that encompass complete intraday figures, and stats for discrete sessions (e.g. London Open and London Close).
Tables 1 & 2 show adverse price movement for manageable unrepaired trades only. Manageable trades are assigned to the category in which their initial adverse price movement belongs. For example, if price moved against the level 12 pips before moving favourably to first profit, it would be counted in the ‘<15’ MAE row.
The figures were aggregated for each level for 5 years, and the frequency distribution derived.
In addition, separate figures were derived for the following sessions: London Open, London Close, and London Morning (of interest due to the lower volume characteristic of the period).