Automation and artificial intelligence have redefined the way we think of traditional processes. Initially, time-consuming, repetitive tasks were given to automated machines to allow people to focus on creative work. At least, that's how the public perceived the role of AI and automation would be.
Instead, AI has attempted to take over creative and analytical jobs first.
From graphic design to, yes, algorithmic trading, AI has shown its dominion and absolute mastery over the world of statistics and numbers. This was always a given.
But does its data-driven mastery mean it can predict the future? How did we get to this point? Are algorithms really the future?
Here's what we've found out at Peccala
Automation and AI Integration in Trading
Automated trading uses algorithms and computer programs to execute trades based on pre-determined rules and mathematical models. Its goal is to make the trading process more accessible and reduce the effects of human error and emotional bias. Today, around 70-80% of all stock trades in the US in 2020 are executed using some form of automated trading.
Yet, trading had always been based on mathematic models and pre-determined rules. The critical difference lies in execution and data processing speed. Algorithms today can collect, analyze, and execute trades from millions of data points in seconds. On the other hand, our conscious minds can only handle 40 to 50 bits of information a second.
Automated trading and AI quickly edge us out when it comes to sheer processing power. The rise of affordable and accessible technology has also made it easier for smaller traders and investment firms to implement global trading systems.
A Brief History of Automated Trading: From Mathematical models to Robo Advisors
Mathematic Models and decisions based on pre-determined rules have always existed in some way, shape, or form.
Here's a brief timeline of automated trading throughout the decades:
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The Early Days of the 70s through the 80s
The early 70s through the 80s saw the introduction of many quantitative financial applications developed to generate signals, time-series analysis, and mathematical models aimed at "beating the market." Examples of this software include but are not limited to The Nasdaq Intermarket Trading System, Jim Simons' Renaissance Technologies, NYSE's MDS I, II, and SuperDOT system.
All these applications significantly improved trading execution speed and volume, incentivizing quantitative analysts to improve their existing models.
High-Speed Connection in the 90s
As technology improved, the barriers to entry for retail investors lessened over time, and during the 1990s, quantitative models were made available for the average investor to deploy.
Like almost every other industry, the internet also played a massive role in popularizing automated trading. This enhancement allowed traders to begin running their models and systems on live data to see their portfolio move in real-time.
Today, real-time streaming is a necessity and a given, a much more prevalent, expected feature than the privilege that it was in the late 1990s.
High-Frequency Trading in the 2000s
By the early 2000s, a new form of rapid-fire trading had emerged: high-frequency trading. While it only accounted for fewer than 10% of orders, the rapidly decreasing execution time would set the stage for excessive growth. From 2005 to 2009, HFT volume grew about 164% and perpetuated market makers, liquidity providers, and arbitrage specialists in their bid to take advantage of statistical trends and probabilities to generate short-term profits in rapid succession.
Robo-Advisors
While 2008 may have been a year of pain for most investors, one great thing did develop as a direct result: the robo-advisor. This automated tool earmarked the beginning of a revolutionary device that would change how people view and handle their money. The robo-advisor appealed to investors given its computerized features, logical investment approach, and high-level transparency.
Robo-advisors are proofed against internal biases and emotions that have bankrupted many experienced traders.
Yet, throughout all these periods (despite the countless advancements made throughout the years), automated trading would face challenges, criticism, and controversies. System malfunction and cyber attacks are regular occurrences. The solution? More focus on security.
The Democratization of Investment Opportunities in Cryptocurrencies
Crypto was born to break down the barriers of centralized institutions such as banks. They're an alternative investment option so popular with retail investors.
Yet, while many people were hyper-focused on the implications of robo-advisors in equities trading, a certain few opted to study its impacts on the digital currency world. Why digital currency? Because it creates support for those without access to a bank, it facilitates cheaper and faster payments and international transfers and works 24 hours a day, 7 days a week. With rapidly changing prices, investors have seen opportunity in the digital currency world - and they're looking at automated traders to help make sense of this new market.
Critical issues have plagued this industry, from security and liquidity to value assessment. The industry's innately volatile market is perfect for developing and improving a trading algorithm. The following section will elaborate on why.
Why Do We Need Automation in Crypto Trading?
At Peccala, we believe that automation allows the following advantages for crypto trading:
- Efficiency and Accuracy
Because actions are programmed, you can execute trades and price adjustments quicker than you would have had you opted for manual methods.
According to a 2019 report from BitWise Asset Management, less volatility would make 30% of investors more comfortable investing in cryptocurrency. While automated traders don't reduce the market's volatility, we do help tame it – a feat arguably more impressive given the capital gain made possible.
- Scalability
Because manpower isn't an issue, automated traders could easily handle many assets simultaneously. A computerized trading algorithm would treat it roughly the same way, whether it's less than $100 or somewhere north of a million. Peccala right now, for example, has a total AUM of roughly 3.1 million USD. Yet, the process has remained approximately the same. In fact, just last week, we reached a milestone and generated $1 million in realized profits for our users.
Talk about scalability!
- Customized Investment Strategies
Algorithmic trading, as we've said, depends on several pre-determined rules and strategies for an algorithm to follow. Different rules mean different systems. With the correct algorithm, you can choose a plan that better suits your goals and risk tolerance.
In the same report we cited earlier, better regulation would persuade 58% of investors to invest in digital currencies. Until the government has understood and fully regulated the industry, maybe you'd want to look at automated investment strategies more suited for the conservative investor.
- 24/7 Monitoring
Finally, automation has granted traders the privilege of time. We need to eat, sleep, and enjoy life, so no human could theoretically monitor the markets 24/7– but a robot can.
