Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions or strategies using computer-based systems as opposed to manual trading which relies on human intervention to execute.Trading execution has transitioned in large part from manual to algorithmic, with the latter having a wide range from low frequency to high frequency.Minutes to seconds and seconds to milliseconds is what defines the top end of the range for high frequency trading.
Algorithmic execution is prevalent in almost every instance of institutional trading. Quantitative investors such as hedge funds, market makers, and high-frequency traders develop algorithms designed to serve a particular investment strategy. Pension funds, mutual funds, and roboadvisors utilize trade execution algorithms for automation and cost reduction.
Many are the reasons algorithmic execution has become best practice in traditional markets. Entering and exiting a position without moving the market against you, minimize market impact and slippage is in most cases the best way to execute a trading strategy.
By splitting orders and trading them in a random pattern, it is possible to execute a given trading strategy without disrupting the market and changing the parameters the trading strategy needs to operate and be profitable. Efficiency is key when executing trading strategies, taking into account market liquidity, price and a myriad of factors.The goal is to ensure the best possible total price for each trade, with minimal market impact, over the life of trading strategy.
Algorithms will always produce superior results when optimising across these dimensions than a human, saving both time and money.
The trading environment changes constantly and most trading strategies hold under particular circumstances, when those change the trading strategy must adapt and change as well. An algorithmic trading strategy must be constantly revised, updated, adapted to be optimized to the particular trading environment.
Trading strategies may be derived either by using qualitative analysis which employs subjective judgement based on non-quantifiable, often intangible information that belongs to the social and experiential realm, or quantitative analysis which relies upon more exact information and mathematical formulas to derive a more technical decision.
Quantitative algorithmic trading uses complex mathematical formulas to analyze a market, track patterns or trends from prices, volume or liquidity to recognize investment opportunities (strategies) that use a computer-based system (algorithm) to execute automatically in high frequency intervals.
As trading has transitioned from predominantly manual to highly automated and digitalized, markets’ quality has improved, including spreads, liquidity, and transitory price impacts.
Yet, with that improvement has come heightened competition. In fact, today, complex legacy systems - settlement, clearing and governance - hamper returns in traditional markets. Shrinking margins and high costs commonly frustrates investors.
Blockchain technology helps negate some of the high costs, remove middlemen and improve outdated legacy systems. Digital assets based on blockchain technology have become a new emerging asset class ripe for new opportunities using algorithmic trading.
The digital assets market grew from $18 billion in 2017 to $800 billion at its height in 2018 and $2.3 trillion in 2021. This incredible growth represents more than 100 times increase in just a few years. Daily trading volume in the digital assets market has reached $140 billion in 2021, as a reference the average turnover on the New York Stock Exchange is just $53billion, evidencing its growth potential and a clear signal that it is maturing as capital flows in.
Consider that the total amount of mined gold is valued at $7trillion, global equity markets are valued at $70 trillion and digital assets are valued at just $2.3 trillion. As institutional capital flows into digital assets there has been more demand for professional market participants to get involved. Expertise in the use of quantitative algorithmic trading will be integral to capturing returns as the digital assets market value grows.
With its expertise in qualitative and quantitative trading strategies, Coinful Capital is poised to become a leading player in this fledgling digital asset industry.
One of Coinful Capital’s strengths is algorithmic asset management deploying quantitative trading strategies for the digital asset market supported by its proprietary trading technology platform.
The Coinful trading team has over a decade of experience building proprietary trading systems and creating quantitative and qualitative trading strategies, producing outstanding returns and trading profits in traditional asset classes such as equities, futures, options and derivatives as well as digital assets. Previously, the trading team worked with a diverse group of leading traditional financial services providers, among them Morgan Stanley,UBS, Allston Trading, Robeco and CTBC Bank.
Overall, the Coinful trading team has generated over US$1 billion in career trading profits and managed roughly $2.5 billion of investable assets.
"There's a need to professionalize the trading of digital assets," says Carlos Salas, chief investment officer and co-founder of Coinful Capital. "There are a lot of profit opportunities from arbitrage as well as price dislocation and other inefficiencies, but volatility is high given the large presence of retail investors in what is still a very immature market. For institutional investors, the right infrastructure and trading strategies are essential for success."
To execute its algorithmic trading strategies, Coinful Capital leverages its robust customized digital asset trading infrastructure which connects a myriad of exchanges and trading desks to a robust backend platform, allowing execution of automated trading strategies around the clock. The institutional-grade platform allows Coinful Capital to conduct strategy research, data analysis, performance monitoring, simulated trading and risk management.
Coinful Capital's trading principles are to trade only digital assets with liquidity and hedging options. With regards to the former, it's very simple: Unlike a traditional digital assets fund, "if you want to take out your money, you can do it whenever you like. That's the benefit of being liquid," Salas says.
Coinful Capital’s launch of its Coinful Growth Fund I in 2019 now called DGC Fund, a quantitative strategy fund that includes arbitrage, market neutral futures, options delta-neutral, mean reversion and pattern trend-following strategies which "produce returns with limited downside risk in bull, bear and neutral market regimes while building the necessary operational infrastructure for the next market bull run," Matt Chuang, chief risk officer explains.
Coinful Capital has strong risk management policies, where each naked position has a tight stop-loss and an overall fund stop-loss requiring a trading pause to re-evaluate the strategies.
Since live trading began in 2019, Coinful Growth Fund I (DGC Fund) has performed impressively, posting annualized returns of over 30% net in bear market environment and an average monthly return of approximately 3%. On an indexed cumulative monthly performance basis, Coinful Growth Fund I (DGC Fund) has since inception, outperformed the S&P 500, and Eurekahedge 50 on most months.
Coinful Capital’s group of service providers for its Coinful GrowthFund I (DGC Fund) include Lazarus Securities (investment manager), PrismCapital Management (research and consultancy), Signature Bank (fiat custodian),Hex Trust (digital assets custodian), Circle Partners (fund administration),Loeb & Smith (legal advisors) and Cohen & Co (audit), consolidating the institutional-grade investment and trading ecosystem and making it easy for investors to access the high return potential of quantitative algorithmic trading on digital assets.