We develop quantitative trading systems
Automatic systems
Based on decades of experience trading the markets Kvantly have developed algorithmic based trading systems
Statistic edge
The systems have an statistical edge compared to the marked, and are used for Kvantly’s own capital allocation
Overview
20+ YEARS INVESTING WITH DISCRETIONARY STRATEGIES PAVED THE WAY FOR DEVELOPMENT OF QUANTITATIVE TRADING SYSTEMS.
This work have materialized as Kvantly
Backtesting
All trading algorithms are testes over long time horizons to verify robustness and positive edge
Python
All programming is executed in pure python , and open source libraries
Signals
Trading signals are pushed to the user required platform, such as mobile, tablet and PC
Skin in the game
All algorithms are originally developed to handle Kvantly’s founders own capital allocation
Automatic
Finalized strategies are deployed on secure online cloud servers
AI for Trading
Kvantly let’s the computers do the heavy lifting, thus letting us sleep good at night
Algorithm Development
– Python
Generating an hypothesis
The idea generation arises from many different sources. It may be observations in the market, or financial published research. In most cases it’s a mix several.
Develop & testing of algorithms
After the hypothesis is finalized, it is broken down into firm rules that are coded together in Python.
This creates the basis of the algorithm which is then backtested with relevant equities over different time periods. Important focus at this stage is to avoid; survivorship bias and confirmation bias and overfitting.
Deployment of the strategy
If the strategy provides successful and yields positive risk adjusted returns vs benchmark, it will be deployed. Typically, 50-100 algorithms are discarded before a successful one is discovered. When the strategies are published they will operate fully autonomous.
Algorithm Development
– Python
Generating an hypothesis
The idea generation arises from many different sources. It may be observations in the market, or financial published research. In most cases it’s a mix several.
Develop & testing of algorithms
After the hypothesis is finalized, it is broken down into firm rules that are coded together in Python.
This creates the basis of the algorithm which is then backtested with relevant equities over different time periods. Important focus at this stage is to avoid; survivorship bias and confirmation bias and overfitting.
Deployment of the strategy
If the strategy provides successful and yields positive risk adjusted returns vs benchmark, it will be deployed. Typically, 50-100 algorithms are discarded before a successful one is discovered. When the strategies are published they will operate fully autonomous.
Built NATIVE
IN THE CLOUD
Components in the stack:
Anaconda
Python 3.X
15 + python libraries
Telegram
Nordnet
Pythonanywhere
We are Kvantly
Kvantly researches and develops algorithmic and quantitative trading systems. The main objective is to develop algorithms to handle the capital allocation of the founder’s own equity.
Please contact us if you want to participate in building the next big thing in quantitative finance.
Datadriven decitions is the future