Automated Investing 101

What is it & how can you get started?

Whether you’re brand new to automated investing or already a seasoned professional, the space is evolving quickly and becoming more lucrative than ever.
So… what exactly is automated investing? and how can you get in on it?

With everyone so caught up talking about the metaverse & the NFT craze, many have barely noticed as automated investing has began to gain significant traction among individual investors as it continues to become more accessible to retail investors through platforms like Alpaca, which allows technical individuals to deploy automated investment algorithms via API, Etoro, which enables users to automatically copy the trades of professional traders, and finally Surmount (us), which allows both technical & non-technical users to build, test, and utilize rule-based investment algorithms & machine learning strategies into their existing portfolio(s), as well as automatically copy the trades of profitable traders/investors.
Similar to the 2012–2015 era where we saw commission-free investing apps turn a seemingly intimidating concept of managing your own investments, into what we see nowadays where more people manage their own investments than those who use investment managers, we’re watching the same situation unfold with automated investing.
With each passing month, the space is becoming more accessible through continually expanding open-source repositories and emerging companies/technologies scrambling to consistently become easier to use, ultimately all benefiting the current and future end users.

So first off, what is automated investing?

Put simply, automated investing is exactly what it sounds like — executing orders on your brokerage account whether you’re in front of the screen or not.

As to what is executing orders on your behalf, there are a handful of different options: algorithmic (rule-based), machine learning strategies, and copy trading.

Starting with algorithmic automation, think of it as a computer program ranging from an example that could be something extremely simple like “every time SPY drops below $(x), buy,” to a strategy much more sophisticated that can consider many different parameters such as (but not limited to) macroeconomic, order book, and/or fundamental data, technical indicators, and even just price/volume to determine potential rules.

These rules, regardless of how complex they are, can be broken down into ‘if, then’ statements that tell the investing strategy (often-times referred to as a “bot) how it should act in the market.
From there, you would validate whether or not this particular collection of ‘if, then’ statements is worthy of any further consideration by determining whether it would have been profitable in the past based on historical data. If it turns out to be something that seems like it could be of interest (we’ll touch on evaluating strategies in an upcoming article), then the next best step after making any optimizations to the strategy is to deploy it into a paper account where you can test the strategy with fake money in live markets. While ultimately it depends how often the bot actually places trades, it’s typically best to let the bot run for at least a month at minimum before making any conclusions about its overall potential.
Once you have enough insight as to how the bot actually performs, it’s time to either fix it, trash it, or deploy it into a live (real money) account.

Next up is machine learning automation. For the sake of condensing the article and not getting too technical, let’s think of machine learning trading systems as more flexible versions of rule-based bots that can adapt to changing market conditions, whereas a rule-based strategy is always going to follow the same set of rules (until you change them.)
Similar to rule-based bots, machine learning strategies execute orders when conditions are met; one of the main differences comes down to the fact that a machine learning system develops its own logic on the best way to act in the markets based on parameters you ‘feed’ into training it. Again — we won’t get super detailed, but in short, ML strategies are guided to learn what a good trade is (profitability is a great place to start) then trained on extensive historical data, then trained on a walk forward basis (meaning the model continues to learn utilizing live data for (x) amount of time) until ultimately evaluated and revised or deployed.

Third is the most basic form of automated investing — copy trading. Whether you’ve done it or not yourself, you at least probably know someone who’s copy traded in some form.

Whether on a Discord trading server where someone(s) post their trades for others to follow and/or have TradingView webhooks sending automated signal notifications, or using Etoro or Surmount’s automation platforms where you can opt to subscribe to an individual’s portfolio and automatically follow their trades as soon as they’re executed, many traders have been flowing into this space specifically due to ease of access.
For some, the aspect of knowing another human that has as much “skin in the game” as you do is controlling the account is comforting, but there isn’t necessarily a concrete underlying strategy behind their trades (however, this isn’t necessarily a bad thing as long as they can consistently produce alpha.)

How can you get started with automated investing?

Whether you’re interested in long-term value investing strategies, short-term YOLO bots, or both, there are many great platforms out there that each provide their own specific value offering.
First, it’s important to decide what your goals/aggression levels are — automated day trading (highest aggression), using bots to swing trade (moderate-high aggression), long-term investing (minimal aggression), or a mixture of multiple strategies.
Some platforms specialize only in specific aggression levels of automated investing, like Robo Advisors that only focus on long-term retirement value investing in (primarily) ETFs.
Others focus on specifically one form of automated investing like Etoro’s copy trading platform.
Alpaca focuses on developers who can easily “plug and go” through an infrastructure of APIs.

At Surmount, we strongly believe that automation is the future of investing, and we’re on a mission to enable any individual with access to hands-free investing in a simple & easy-to-use experience.
Users can browse the quant marketplace to access machine learning, algorithmic trading, and copy trading strategies, view all of the strategy’s details to determine whether it could be a good fit, and easily integrate into their existing equities, crypto, or forex portfolio(s) to trade on their behalf, and non-technical users can even build investing strategies without writing a single line of code. If profitable, they can upload the strategy to our marketplace and even get paid when other traders use it!