As financial securities become increasingly complex, new breed of rocket scientists have grown to price these complex mathematical models. These experts are know as quantitative analysts:
- Quant Trading experts expose inefficiencies in markets using sophisticated computerised algorithms
- With algorithmic trading we say if you can't quantify your edge then you don't have one.
What is Quant Trading
As financial securities become increasingly complex, new breed of rocket scientists have grown to price these complex mathematical models and enhance them to generate profits and reduce risk. These experts are known as quantitative analysts, or "Quants".Quants are the Goose that laid the golden eggs for Wall Street, once quietly guarded, Quants are breaking free to equip the retail investors with the same technology and levelling the playing field.
Finding Inefficiencies in the Market
Quant Trading experts expose inefficiencies in markets using sophisticated computerised algorithms which can generate huge gains for investors. When algorithmic trading a quant will say if you can't quantify your edge then you don't have one. If you can't measure risk then you can't manage your risk . Tips from any human discretionary fund can useless as they are often based on pure guess work - similar to throwing darts on dart board, non-quants cannot differentiate luck from skill. Similar to a basketball player, we can only determine if a 3 point shot was lucky or skill based on statistical success over 'x' number of throws.
Finding a Fundamental Truth to each Market
All daytrading automated trading strategies are based on finding a fundamental truth about the market. Quants aim to define rules which seek best to model repeatable non-random behaviour. Some Quants might use technical analysis and test if repeatable patterns exist in past market data, some may use arbitrage models and others may use order flow analysis. Whatever method is used the Quant will break down the premise into a series of basic rules and test them on various sets of market data. If the tested rules prove to show an edge well beyond random then the rules can be pursued further and a system can be designed around these core set of rules:
- Algo trading strategies can collect data on order flow and model behaviour of other algorithms, once a participant tips one's hand your algo trading strategy can take advantage and expose and inefficiency time and time again.
- Investors love trend/momentum following but have to wait months to profit. Moreover, long term trades can be pure luck whereas a Quant tends to work on a daytrading timeframe and can make even 100s of trades a day. On a day trading level the same market trends exist, your algo trading strategy determine when is the best probability of catching a strong trend.
When Others Begin Losing Their Head
Volatility is not new, the idea "this has never happened before" is wrong. When the irrational swamps the rational and fear grips the markets most players freeze. These periods are when algorithmic trading excels, cool and emotionless they remain unfazed.
Algo trading is using proprietary statistical measures to create an edge. Each trade made by a Quant should have a positive expectancy and edge. Therefore, every trade is made with confidence knowing each trade has probability of success heavily in your favour. See our winning daytrading strategy: Serenity Bot