How does StrategyQuant work?

Author: SQ team

January 16th, 2019

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Random generation is the foundation of StrategyQuant. Strategies generated this way can be further improved (evolved) using Genetic evolution.

 

Random generation of trading strategies

A trading strategy in the initial population is constructed using a combination of price patterns, technical indicators, order types, and other parts to form the entry and exit rules.

StrategyQuant can use all standard technical indicators and oscillators (like CCI, RSI, Stochastic, etc.), time values (like time of day, day of week) and price patterns. These building blocks are then combined using logical and equality operators (and, or, >, <, etc.) to form an entry or exit rule.
In addition, it supports different entry and exit order types (market order, limit order, fixed profit target, exit after X bars, etc.).

With all the possible combinations of rules and orders, StrategyQuant is capable of generating literally trillions of different possible trading strategies.

 

With all the possible combinations of rules and orders, StrategyQuant is capable of generating literally trillions of different possible trading strategies.

The building process itself is completely random – builder randomly picks different building blocks from the available pool and combines them to create entry rule, order type and exit rule.
There are some validity constraints that ensure that, for example price is not compared to time value, etc.
The result is a completely new random trading strategy. Of course, not every randomly created

strategy is profitable, but StrategyQuant can produce and test thousands of new strategies per hour, and there are many profitable ones in this amount.

Genetic Evolution

Genetic Evolution takes the process of finding suitable trading strategies even further.
In this mode StrategyQuant first creates a number of random strategies, which are used as the initial population in the evolution.

This initial generation of strategies is then “evolved” over successive generations using genetic programming technology.

This process imitates the evolution – the algorithm chooses the fittest strategies (using selected performance criteria) in every generation, and the group of fittest candidates is then used to produce new generation of trading strategies.

As in evolution, this should result in better and better candidates, in our case in strategies that are more profitable, more stable, or generally better in the selected performance criteria.

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