Investment Approach
Investment philosophy
The company invests in the global financial markets using the quantitative and full-system approach.
Our investment philosophy is based on four principles that aim to provide our customers with excellent risk-adjusted returns (our investment philosophy is based on four principles, the purpose of which – to provide our customers with an excellent yield, adjusted for risk).
Disclaimer: These investment products come with a significant risk of loss.
Investment process
Due to our experience in researching and developing new trading strategies, we are constantly improving our own investment process in order to offer our clients innovative products.
1. Data collection
At the first stage of the investment process, we collect data from various traditional and alternative sources.
To maximize our chances of finding new sources of alpha analysis, we look at multiple datasets at once, including financial, fundamental, macroeconomic, government, and alternative sources.
This allows us to delve deeper into the operation of financial markets and test our hypotheses in a comprehensive way to implement adaptive strategies in the next stages of our development structure.
2. Data cleaning
We use patented automated algorithms to clean massive amounts of data. Our data scientists oversee automated cleanup procedures to ensure that the datasets are clean and can be further used by our quantitative researchers.
3. Data analysis
To search for alpha signals, our quantitative researchers analyze the datasets processed in the previous step using sophisticated and advanced statistical techniques.
This step is a key difference, as alpha signals are difficult to detect, and as a consequence, it is important to have an excellent team of quantitative researchers who can uncover hidden investment opportunities using massive amounts of data.
4. Testing on historical data
After an accurate and comprehensive analysis of the data, our quanta apply the scientific method to financial data.
We test our hypotheses by experimentation on historical data with long time series data spanning several decades.
By accepting long historical data sets, we reduce bias when testing against historical data and increase the likelihood of our investment strategies being robust in different market conditions.
5. Pilot test
This step is very important because it increases the likelihood that the strategy will perform as expected in real market conditions and gives us time to correct potential technology problems before production begins.
6. Production
After making sure that the profitability of the pilot trade matches the backtesting results and our expectations, our investment group allows our clients to invest in a new investment product.
Disclaimer: These investment products come with a significant risk of loss.