At Quantfish we use mathematical modeling, measurement and research to forecast anticipated future market behavior with a statistically significant degree of confidence. By designing our own proprietary trading systems from the ground up, we endeavor to constantly capitalize on opportunities in the financial markets as and when they arise.
The word ‘quant’ is derived from quantitative, which essentially means working with numbers, whereas ‘fish’ refers to the fishing for, or extracting of ‘alpha’ in the markets. Alpha (α) is a term used in investing to describe an investment strategy’s ability to beat the market.
We do not utilize fundamental data, market sentiment, or any non-quantifiable input to inform our trading models. Quantfish trading is purely data-driven and uses advanced statistical and mathematical models based on large sets of historic price data to establish the probability of certain future market movements. A lot of computational power and extensive research is required to design & develop successful models, and we use only state-of-the-art hardware and software to aid the process.
Our private equity portfolios consist of major indices as well as forex pairs combined with the least amount of correlation to ensure optimum diversification within each fund. To further enhance diversification to allow for smoother equity curves, our strategies are optimized to perform well in bull and bear markets and make use of trend following, breakout and mean reversion principles.
Testing and Trading Software
We do not utilize any 3rd party software such as Tradestation or Amibroker to test or trade our strategies. Our proprietary system trading software (AlphaWOLF) has been meticulously developed ‘in-house’ from the ground up over several years. AlphaWOLF software connects directly with our broker’s API and allows us to easily design and deploy new strategies, as well as portfolio level backtesting, optimization, visualization and fully automated live trading.
Our software’s trading ‘engine’ is used for strategy testing, optimization as well as live trading. Using the exact same ‘engine’ for all these applications has many advantages. It reduces errors and discrepancies and replicates live trading as close as possible compared to historic test results. Spread variation and slippage during live trading might still slightly deviate from historic testing results, but these are simulated during testing and optimization to imitate live trading within a 3% margin of error. [ More Info ]
What we do:
- Private equity fund management (By invitation only)
- Financial & investment models
- Data Science
- Machine learning & classification eg. KNN, SVM, K-Means etc.
- Quantitative & statistical analysis
- AI & Advanced algorithms
- Software, App & E-commerce development
- Api integrations
- Python, Java, PHP, SQL
What Is Quantitative Analysis?
Quantitative analysis uses mathematical and statistical modeling, measurement and research to attempt to forecast anticipated future events with a certain degree of confidence. All subjects involving numbers can be quantified, but the vast amounts of available historic market data make it especially suited for use in the financial industry. Quantitative analysis can be well applied to analyze trading & investment opportunities, such as when to purchase or sell securities. In the financial world, analysts who rely strictly on quantitative analysis without any regard for fundamentals are frequently referred to as ‘quants’ or quantitative traders.
What Is Alpha?
Alpha (α) is a term used in investing to describe an investment strategy’s ability to beat the market, or its ‘edge’. Alpha is thus also often referred to as the ‘excess return’ of that investment compared with a suitable market index. Alpha is used in finance as a measure of performance, indicating when a strategy, trader, or portfolio manager has managed to beat the market return over some period.
What is Machine Learning?
Machine learning is a subset of data science that provides the ability to learn and improve from experience without being programmed. Machine Learning concepts have existed for a long time, however restrictions in computing power limited its use in the past. Lately, this has become much easier to do with the emergence of huge amounts of processing power and big data. Large amounts of data can be fed to ML algorithms to create accurate models for use in classification or prediction.
What is Data Science?
Data science combines multiple fields, including statistics, scientific methods, artificial intelligence (AI), and data analysis, to extract value from data. Data science is a multidisciplinary approach which encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns to draw informed conclusions. From risk analysis to algorithmic trading, data science is invaluable to the Finance Sector.
What is Algorithmic Trading?
Algorithmic or ‘system’ trading refers to the use of computer algorithms to automate trading decisions. Mathematical models are built that monitor market sentiment, price action and trade activities in real-time to detect any factors that can force security prices to rise or fall. The models or ‘strategies’ can be designed with a predetermined set of instructions, or dynamic machine learning models on various parameters such as timing, price, volatility, volume and various other factors for placing trades without the trader’s active involvement.