In any complex system we inevitably come across the predominance of some patterns or relationships over others. For example we talk a lot in trading circles about the ‘fractal’ nature of markets or the dominance of certain features such as the Golden ratio or Fibonacci sequence. Now is this just BS for the tea leaf readers or is there something deeper in the significance in certain features of the data?
Part 3 which is the final installment of this series focuses on fine tuning the portfolio and adding realism to the backtest results. Before we can pluck the courage up to launch into our live trading following our exciting back-test results, now comes the time to remove any bullish euphoria and focus on the reality of live trading. We do this to dampen our expectations and ensure our risk weightings are correctly applied for live conditions. This is where the brutal reality of the frictional costs of trading need to take center stage….as we inevitably incur additional costs to that anticipated from our back-tests attributed to the realities of live trading.
In Part 1 of our 3 Part article titled “Creating a Powerful Blend from Scratch”, we commenced the portfolio optimization process by selecting our primary system upon which we would then apply diversification principles across instruments and asset classes to achieve a bigger bang for buck in terms of risk-weighted returns for our finite investment capital. Part 2 of this installment article focuses on how we go about this blending exercise.
In my previous article which introduced you to the world of practical portfolio management, we summarized the key criteria you need to consider when creating a portfolio from scratch. This article was theoretical in nature…..but now it is time to put the theory into practice and take you on an exciting practical journey where we apply these sound principles to create a robust portfolio performer offering sustainable risk-weighted returns.
In a previous post “Don’t be fooled by Randomness”, we discussed how the central feature of a modern efficient market is the dominance of random price action (or noise). To explain how this noise is manifested, think of the behaviour of the traders, institutions and investors who interact with the market. The participants span the spectrum of timescales from the intraday traders and High Frequency Traders (HFT) that interact with the market on the very short timescale to the very long term position traders who interact with the market on a far more selective and infrequent basis. Each of the different participant behavioural groupings vary in the outcomes they are seeking from their interactions with the market including their timing and position sizing. Putting this all together as a collective image over time, we can imagine the maelstrom of interactions and the intractable nature of predicting future price action with fidelity.