An indispensable component of quantitative strategies is programming a set of mathematical conditions that decide on the market position (buy/sell) to take in a specific asset, the amount of the order in units and the selection criteria of the different securities that comprise a portfolio. By developing our own trading simulation software we can ensure that the backtesting process resembles real-time trading conditions by taking into consideration the transaction costs involved, delay for different orders to be filled successfully and most importantly the use of actual, high-frequency historical price data across many asset classes.
Apart from fundamental technological advances and the need to incorporate them seamlessly into daily finance operations, one of the reasons why algorithmic trading has became so increasingly prevalent is the fact that it can replicate the principles and execution of virtually every investment strategy, from the most intricate to the simplest ones. By operating an online marketplace of such strategies and offering developers the pertinent simulation software, individuals from quantitative finance can create and lend their algorithms to retail traders by only exposing their historical outcome in risk-reward metrics instead of disclosing their code.
Using basic marketplace search operations retail traders that access our platform can browse different portfoliosaccording to their desired investment strategy (global macro, long/short equity, statistical arbitrage etc.), their preference over selected kinds of securities (physical stock, foreign exchange, commodities, cryptocurrencies etc.) and of course the unique nature of their risk appetite as expressed in the amount of borrowed money they put in their portfolio (leverage) and the number of units they elect to order from every asset class (order size).