A Beginners Guide for DIY Investors
The gap between Wall Street and Main Street started shrinking in the early 2000’s but has accelerated since Covid.
What used to be the exclusive playground of hedge funds and institutional giants — blazing fast computers, complex trading systems, and algorithms, is now accessible to everyday investors with a decent internet connection, curiosity, and discipline. This shift is not only about access to new tools, but t’s also about a new way of thinking. It is about replacing gut feelings with rules, replacing panic with process, and replacing “hunches” with historical data.
Welcome to the world of algorithmic trading.
For the DIY investor, algorithmic trading can seem intimidating: lines of code, statistics, probabilities, and AI bots. But the truth is, algorithmic trading is just a fancy term for something quite simple; it’s using predefined rules and data to decide when to buy or sell.
This guide is for the self-directed investor who wants to start and understand what algorithmic trading really is, how it works, what tools are out there, the strategies that can be used, and whether it’s worth your time and money. Like all good things in investing, it’s not a magic bullet. But with the right mindset and risk management, it can be a valuable tool in your investing toolkit.
What is Algorithmic Trading?
Algorithmic trading is sometimes called algo trading or automated trading because of its use of computer programs to follow a set of instructions to place trades. These instructions can be as simple as “Buy when the price moves above the 200-day moving average” or as complex as multi-variable machine learning models reacting to real time market conditions.
At its core, algorithmic trading replaces the manual trade, the time when you click “buy” or “sell” with a set of pre-programmed rules that do it for you. Think of it as an autonomous vehicle, but for your portfolio, as you navigate down your investing journey.
There are generally two types of algorithmic systems:
- Rules-based systems: These follow logical steps based on market data. If condition A occurs, then do action B. For example, if the stock moves above its 50-day moving average and volume spikes, buy. This is a very simple example. But as conditional loops become nested and multi variable, they all followed pre-defined steps.
- AI-driven systems: These use adaptive models and are often powered by machines, learning to find patterns in vast sets of financial data. They don’t just follow rules; they learn and adjust over time. Development in this area has been exploding since 2024 and is rapidly changing
Common inputs used in algorithms include:
- Price trends
- Trading volume
- Volatility levels
- Timing of trades
- News sentiment
- Technical indicators like
- MACD or RSI
Whether you’re working with a simple spreadsheet-based system or a sophisticated neural net, the goal remains the same: to make decisions based on data, not emotion.
How Does Algorithmic Trading Work?
You can think of algorithmic trading like a factory assembly line. It works in stages as each important step builds upon the prior steps to the final product and goal of a well-executed trade. Here’s a breakdown:
1. Market Data Ingestion
The process begins by pulling in lots of data. This includes both historical data (used for backtesting and model building) and real time market feeds that drive actual trade decisions. Market data includes prices, volumes, volatility, order book depth, and even news headlines or social sentiment. Some algorithms work off minute-by-minute data; others look at daily or weekly signals. The quality, speed, and reliability of this data stream can make or break your strategy.
2. Signal Generation
This is the logic layer, where the sausage is made. It’s where your algorithmic trading strategies interpret the data and generate a buy or sell signal. This can be as simple as a moving average crossover, or as complex as a machine learning model evaluating dozens of variables. For example, a momentum strategy might buy a stock when its price rises faster than a specific threshold over a rolling period (say, 15% over 44 days) and volume exceeds average.
3. Trade Execution
Once a trade signal is triggered, the algorithm passes the output to the execution engine. Here, speed and precision matter. The trade is routed through a broker or trading platform, typically via an API, and placed on the exchange. Some systems will use smart order routing to get the best price across multiple venues, while others may break up large trades into smaller chunks to minimize market impact. In high-frequency or intraday trading, fractions of a second in execution time can affect returns.
4. Backtesting and Refinement
This is your test lab. Before running a strategy with real money, you’ll test it against years of historical data to see how it would’ve performed under various market conditions. As much as we would like to be true, we must never forget that just because a system performed well in the past doesn’t mean it will work in the future. That’s why good backtesting includes out-of-sample testing, stress scenarios (like 2008 or 2020), periods of slow rising and falling markets and fast rising and falling markets, as well as reasonable assumptions about trade slippage and transaction costs.
5. Ongoing Monitoring and Adjustments
Algorithmic trading isn’t “set it and forget it.” Even fully automated systems require monitoring. APIs break. Data feeds stall. Market regimes change. The most successful DIY traders treat their systems like living, breathing tools while refining them over time, adjusting for new conditions, and learning from mistakes. Even the best algorithm is only as good as its ability to adapt. You must want to commit the time to not only developing a system but continually commit to the ongoing maintenance.
Popular Algorithmic Trading Strategies for DIY Investors
While hedge funds may run hyper-optimized black box systems, most DIY investors benefit from keeping it simple. Here are several algorithmic trading strategies that are both approachable and powerful:
1. Momentum Trading
This strategy assumes that stocks which have been moving in one direction will continue, at least for a while. There is no shortage of models that utilize this approach because it is one of the few confirmed anomalies of the market. The algorithm looks for surges in volume, price momentum, or breakouts from resistance levels, in relation to pre-defined time periods.
2. Mean Reversion
3. Arbitrage and Statistical Arbitrage
These strategies look for small price discrepancies across correlated assets. For example, if two stocks typically move together but one suddenly diverges, the system might bet on them converging again. The data requirements for this strategy can be quite large.
4. Trend Following
Another approach (6-9 months) might attempt to ride extended moves in one direction. It’s often built on moving average crossovers, price channels, or volatility breakouts. This approach can focus on any time, from short-term (minutes to days) up to longer-term (6-9 Months). As with most other approaches, the short the time period, the more important latency and exact trade execution becomes.
5. Machine Learning Models
These use adaptive algorithms trained on historical market data. They continuously learn from new inputs, trying to improve signal accuracy over time. It’s complex but becoming more accessible through platforms like QuantConnect.
Tools DIY Traders Can Use to Access Algorithmic Trading
Here’s the good news: you don’t need to be a professional quant to use these tools. A wide range of platforms now make algorithmic trading accessible for retail investors:
Low-Code / No-Code Platforms
QuantConnect, Alpaca, and TradeStation offer drag-and-drop interfaces or simplified coding tools to build and test strategies. Users are not required to write any code.
Broker APIs
Platforms like Interactive Brokers, TD Ameritrade, and Robinhood offer APIs so you can connect your custom strategies to real markets.
Trading Bots and Marketplaces
You can buy or rent pre-built Bots from marketplaces. These range from basic trend followers to sophisticated AI models.
Coding Your Own
If you’re comfortable with a programming language like Python, R, or JavaScript, you can build your own system from scratch. This gives you complete control, but it does require more technical skill. Advances in platforms have almost made this approach not worth it except for the experienced developer.
If you do not want to write code at all, and utilize well written, fully backtested models, then we have found TuringTrader.com an excellent place to start. The developer of this site has experience in developing real world consumer electronics and quantitative finance models.
Is Algorithmic Trading Profitable?
Yes, algorithmic trading can be profitable. However, it’s not guaranteed, and it does require commitment.
Here is an important fact – most systems work, until they don’t. That’s because market conditions will change. What worked last year may fail this year. Success depends on your ability to adapt, test, and manage risk.
Factors That Influence Profitability:
- Data Quality: Garbage in, garbage out. Poor or delayed data leads to bad decisions.
- Overfitting: A strategy that performs perfectly on past data might be too tailored — and fail in the real world. We tend to continually search and tweak for the “perfect” variable or time frame to see it the system can generate the “perfect” curve.
- Underfitting: On the flip side, strategies that are too simple might miss important signals.
- Execution Speed: The faster you can execute, the more likely you’ll capture the intended trade.
- Position Size: Taking on too much risk in a single trade can wipe out weeks of gains.
Most importantly, algorithmic trading demands realistic expectations. Don’t expect to beat the market every month or even every year. Instead, aim for consistency, discipline, and capital preservation over the long term.
Benefits and Risks of Algorithmic Trading
Benefits:
- Emotion-Free Decisions: The algorithm doesn’t panic during a market drop or get greedy during a rally.
- Speed and Efficiency: Your trades can be executed in milliseconds — far faster than you can click a mouse.
- Scalability: Once built, the same algorithm may be able to trade across multiple stocks or even asset classes.
- Automation: No need to sit in front of the screen all day.
Risks:
- Over-Optimization: It’s tempting to tweak until a strategy looks perfect – only to watch it fall apart in real time.
- Technical Failures: A broken API, internet outage, or server crash can leave your trades hanging.
- Regulatory Gray Areas: If you sell or share your algorithm, you may face registration requirements.
- Overconfidence: A winning streak can lead to bigger trades, which can blow up faster when they go wrong.
This is why stop losses, position sizing, and constant monitoring are key elements of smart risk management.
Many Traditional Investing approaches:
- Buy-and-hold strategy
- Focus on fundamentals, long term performance
Less frequent trading, lower fees
Algorithmic Trading:
- Rules-based or data-driven
- Can be short-term or long-term
- Can have a higher frequency of trades but does not have to.
Both can have their place.
Algorithmic trading may suit those who are analytical, data-oriented, and enjoy building systems. Traditional investing may suit those who prefer simplicity and patience. You can even combine both, using algorithms for tactical positioning while maintaining a long-term core portfolio.
Regulatory and Tax Considerations
Don’t think that just because you are not clicking a button, you don’t need to pay taxes and follow guidelines like you did.
Key Areas to Understand:
- SEC & FINRA: Automated strategies must follow all standard rules — including best execution and fair-trading practices.
- Pattern Day Trading Rules: If you make more than three same-day trades in a five-day window with less than $25,000 in your account, you may be flagged.
- Wash Sale Rule: If you sell a stock at a loss and buy it back within 30 days, the loss is disallowed for tax purposes.
- Short-Term Gains: Frequent trading often means gains are taxed as ordinary income, not the lower long-term capital gains rate.
1. Start with Paper Trading
Before you commit real money, test your strategy in a simulated environment. Platforms like QuantConnect and TradingView let you do this easily. You’ll learn the rhythm of your system without the pain of real losses.
2. Choose a Simple Strategy
Don’t start with deep learning or market-neutral arbitrage. Begin with something like a moving average crossover or a basic trend following strategy. These are intuitive, time-tested, and easy to implement.
3. Use the Right Tools
- For No-Code: TradeStation, Alpaca, MetaTrader
- For Programmers: Python + Pandas + Backtrader or QuantConnect
- For Visualization: TradingView
Remember: your tools don’t need to be perfect, and they never will be, but they need to be familiar with them. Spend more time understanding the platform than chasing the flashiest features.
4. Backtest with Historical Data
Use at least 5-10 years of market data. Make sure you test in different market conditions: bull markets, bear markets, sideways chop. Avoid cherry-picking ideal timeframes. This isn’t a highlight reel… it’s the full season.
5. Think Risk First
Before you run any live strategy, decide on:
- Maximum position size
- Use of stop losses
- Expected drawdowns
- How you’ll respond if the system breaks
In short: build guardrails before you hit the gas.
6. Go Live (Slowly)
Start small. Consider using a micro-account or only a portion of your total capital. This allows you to monitor performance without risking your future.
FAQ: Common Questions About Algorithmic Trading
Can I use AI to trade stocks?
Yes. Many platforms allow you to use machine learning algorithms or purchase AI-enhanced bots. But AI models require large data sets, technical skills, and careful tuning. As we mentioned above, the features and capabilities being developed by incorporating AI are changing almost daily.
Is algorithmic trading legal?
Absolutely, as long as you follow regulatory rules, don’t manipulate markets, and don’t offer unregistered advice or tools to others.
What is the best algorithmic trading software?
There’s no one-size-fits-all. Beginners often prefer Alpaca, QuantConnect, or TradeStation. Coders may gravitate toward Python + Backtrader or Interactive Brokers API.
What are the 5-3-1, 90%, and 3-5-7 rules in trading?
These are shorthand systems for managing time, trades, or expectations:
- 5-3-1 Rule: This rule is meant to simplify the learning curve by narrowing your focus instead of chasing dozens of setups across every asset. By sticking to a defined window for trade execution, you can better understand how your strategy performs in specific market conditions. It reduces the mental fatigue that comes from monitoring too many trades, which often leads to manual trade mistakes driven by impulse rather than strategy. It says to focus on 5 currency pairs, 3 trading strategies, and 1 time of day to trade.
- 90% Rule: This cautionary statistic highlights the steep learning curve in trading and the consequences of poor risk management. Many new traders skip proper backtesting and jump straight into live trading without understanding position sizing or stop losses. The rule isn’t a guarantee, but it’s a reminder that trading success is more about process and discipline than prediction or luck. It says that 90% of traders lose 90% of their capital in the first 90 days.
- 3-5-7 Rule: This rule encourages patience and discourages the common mistake of abandoning a strategy after a short losing streak. Strategies, especially algorithmic trading strategies, need to be assessed over time to smooth out random variance in the markets. Evaluating a system too soon can lead to false conclusions; while giving it time provides context on how it performs across multiple market conditions. This refers to waiting 3 days, 5 weeks, or 7 months to evaluate a strategy.
Do I need to be a programmer?
How much money do I need to get started?
Can I trade with algorithms in IRAs?
Yes, but you’ll be limited to strategies that don’t involve margin or shorting. Many trend-following and momentum strategies can still be effective in tax-advantaged accounts.
How does algorithmic trading handle market news or events?
Some systems ingest news sentiment or event triggers. Others may freeze trading during earnings reports or economic data releases. Always consider how your system reacts to surprises.
What’s the difference between high-frequency trading and retail algorithmic trading?
Is Algorithmic Trading Right for You?
Not everyone wants to be a trader. And that’s okay.
But if you:
- Enjoy testing ideas using data
- Prefer logic over gut feel
- Are willing to invest time into learning
- Can handle drawdowns without panic
…then algorithmic trading might be a great fit for part of your portfolio.
It doesn’t replace traditional investing; it’s a complement. Think of it like having a second pair of eyes, that can go deeper and do not blink when the market falls 5% in a day.
But let’s be clear: algorithmic trading is not easy. It’s a craft. You’ll spend hours debugging, refining, and rethinking. And you’ll still have losing streaks. That’s part of the journey. What matters is that you approach it thoughtfully, with humility, and with risk management front and center.
Final Thoughts
In many ways, algorithmic trading is less about technology and more about discipline. It removes the need to be right in the moment. Instead, you’re betting on the idea that a well-reasoned system, run consistently over time, can outperform random decisions driven by fear or greed.
It’s the same philosophy that underpins all great investing: have a plan, stick to the plan, and don’t let emotions blow it up.
Algorithmic trading is just one more way to live that out.
Whether you build your own bot or borrow someone else’s, whether you write Python or drag-and-drop blocks — the key isn’t the code. It’s the mindset.
Remember, investing is personal, and every investing strategy has tradeoffs and opportunity costs. We must always be brutally honest with what we want, and if we are willing to do what it takes to achieve the desired outcome. What worked for your neighbor or coworker does not mean it is right for you. Before making any changes, preparation and approaching it with realistic expectations is the key. Spend a few minutes with us to see if we are a good fit for each other.
The Porter Investments Strategies were developed by our President and founder, Bob Porter. His prior work at Fidelity Investments allowed him the opportunity to advise and study a diverse group of investors.
