Artificial Intelligence and algorithmic trading are no longer exclusive to hedge funds and investment banks with nine-figure technology budgets. The tools have democratized significantly over the past decade, and retail traders now have access to AI-powered platforms, screeners, and even conversational AI tools like ChatGPT that can generate, explain, and refine trading strategies on demand. The question is no longer whether retail traders can access these tools. The question is how to use them effectively.

This article breaks down how AI and algorithmic trading actually work, what retail investors can do with them right now, and how to pair AI-generated strategy ideas with disciplined manual execution.

What Is Algorithmic Trading and How Does It Dominate Modern Markets?

Algorithmic trading refers to the use of computer programs to execute trades based on pre-defined rules. These rules can be simple (buy when the 10-day moving average crosses above the 50-day moving average) or extraordinarily complex (machine learning models that process earnings call sentiment, order book depth, and macroeconomic data simultaneously).

The scale is staggering. According to a report from JPMorgan, algorithmic and high-frequency trading accounts for roughly 60-73% of all U.S. equity trading volume on any given day. That means the majority of price action you see on a chart is being driven by machines following programmatic rules, not humans staring at screens and making gut decisions.

Understanding this changes how you should think about chart patterns. When a stock breaks above a key resistance level and volume surges, it is often because dozens of momentum algorithms simultaneously triggered buy orders. When a stock reverses sharply at a round number like $50 or $100, that is frequently the result of algorithmic sell programs activating at pre-programmed price targets. Retail traders who understand the mechanics behind these moves can position themselves to benefit from predictable institutional behavior rather than getting caught on the wrong side of it.

How Retail Investors Are Using AI and Algorithms Right Now

The retail toolkit for AI-assisted trading has expanded dramatically. Here are several practical approaches retail traders are actively using today.

1. Using Conversational AI to Build and Explain Trading Strategies

Tools like ChatGPT, Claude, and Gemini can generate complete trading strategies, explain the logic behind technical indicators, and even help you troubleshoot why a strategy might be underperforming. You do not need to know how to code. You can describe what you want in plain English and get back a structured strategy with entry rules, exit rules, and stop-loss placement guidelines.

For example, you can ask: "Build me a simple mean-reversion strategy using RSI on daily charts. Give me specific entry conditions, a stop-loss rule, and a profit target." The AI will respond with a complete framework. You can then test it manually in a simulator or paste the logic into a backtesting platform.

2. Using AI-Powered Stock Screeners

Platforms like Trade Ideas use AI to scan thousands of stocks in real time and surface setups that match specific technical criteria. Rather than manually scanning charts, traders define the conditions they want (RSI below 30, price near 52-week support, volume spike) and the algorithm does the filtering. Finviz, while not AI-powered in the machine learning sense, uses algorithmic screening logic that allows traders to filter by dozens of technical and fundamental variables simultaneously.

3. Backtesting Strategies with Algorithmic Logic

Platforms like TradingView's Pine Script editor allow retail traders to code simple strategies (or paste AI-generated code) and run them against years of historical data. A strategy that looks good on paper can be tested across hundreds of different market conditions in minutes. Key metrics to evaluate include win rate, profit factor (gross profit divided by gross loss), maximum drawdown, and average holding period. According to research published in the Journal of Financial Economics, most simple technical trading rules produce statistically significant returns only in specific market regimes, which is why backtesting across multiple periods and market conditions matters more than finding a single profitable sample.

4. Sentiment Analysis and Natural Language Processing

Several platforms now offer AI-driven sentiment analysis that processes news headlines, SEC filings, and social media posts to generate a sentiment score for individual stocks or the broader market. Tools like Unusual Whales and Quiver Quantitative aggregate options flow, congressional trading disclosures, and social sentiment data into dashboards that give retail traders institutional-grade visibility into where money is actually moving.

5. Copy Trading and Algorithm Marketplaces

Platforms like eToro allow retail traders to copy the trades of professional or community traders automatically. Meanwhile, algorithm marketplaces allow traders to subscribe to pre-built trading algorithms without needing to write a single line of code. The risk here is that past performance does not guarantee future results, and many algorithms are curve-fitted to historical data in ways that fall apart in live markets.

retail trader algorithms

What Is the RSI Indicator and How Do Algorithmic Systems Use It

The Relative Strength Index (RSI) is one of the most widely used momentum oscillators in technical analysis. Developed by J. Welles Wilder Jr. and published in his 1978 book New Concepts in Technical Trading Systems, RSI measures the speed and magnitude of recent price changes on a scale of 0 to 100.

The classic interpretation is straightforward: readings above 70 suggest overbought conditions and potential reversal to the downside, while readings below 30 suggest oversold conditions and potential reversal to the upside. The midline (50) acts as a momentum threshold. When RSI crosses above 50, it signals that buying pressure is overtaking selling pressure. A cross below 50 signals the opposite.

rsi oscillator

Algorithms use RSI in several ways. Trend-following systems use RSI crossovers of the 50 level as entry signals. Mean-reversion systems use extreme RSI readings (above 70 or below 30) as signals to fade the move. Some institutional models use RSI divergence (price makes a new high but RSI fails to confirm) as a warning signal that momentum is weakening before a reversal occurs.

The reason RSI works as a repeatable signal is partly self-fulfilling. Because millions of traders and thousands of algorithms are watching the same levels, buying and selling pressure clusters around them. This creates the very reversals and breakouts that traders are looking for.

How To Practice Building and Testing an AI-Generated RSI Strategy on the Simulator

This assignment walks you through the full loop: use AI to build a strategy, test it in the simulator, then bring your results back to AI for analysis. This is the kind of iterative feedback process that separates traders who improve from traders who simply repeat the same mistakes.

Step 1: Ask AI to Build You a Strategy

Open ChatGPT, Claude, or any conversational AI tool. Type the following prompt (or something close to it):

"Create a simple daily chart trading strategy using the RSI indicator. Give me specific rules for entering a long position, entering a short position, where to place my stop-loss, and when to take profit. Keep the rules simple enough that I can execute them manually by looking at a chart."

The AI will return a structured strategy. A common output might look something like this:

  • Long Entry: RSI crosses above 50 from below, and price is above the 20-day moving average.
  • Short Entry: RSI crosses below 50 from above, and price is below the 20-day moving average.
  • Stop-Loss: Place stop below (for longs) or above (for shorts) the most recent swing high or low.
  • Profit Target: Exit when RSI reaches 70 (for longs) or 30 (for shorts), or when price moves 2x the distance of your stop-loss risk.

Write down the rules. You should be able to explain every rule to someone else before you start testing.

Step 2: Test the Strategy for 20 Charts on Trading Blitz

Head to the simulator and apply the RSI indicator to the chart. Premium users can use the RSI crossover 50 and RSI crossunder 50 filters to load charts that are near a signal. If you are using the free version, simply load new charts until you find one that is near an RSI 50 crossover based on the historical portion of the chart.  Remember to reset your account's Game History in order to clear out any old data.

Follow the rules exactly as written. Resist the urge to override the system with your own gut feeling. The entire point of this exercise is to test the rules, not your intuition. For each of the 20 charts, record the following:

  • Entry price and date (which bar number in the forward-trading period)
  • Stop-loss level
  • Exit price and reason (stop hit, profit target hit, or RSI reversal signal)
  • Profit or loss in dollars and percentage
  • Whether you followed the rules or deviated from them

Trading Blitz automatically logs your trades in the session trade log, so you can review your profit/loss for each position after you reduce or close it. 

Step 3: Export Your Trade History and Ask AI to Analyze It

After completing 20 charts, compile your trade log into a simple table. It does not need to be fancy. Something like this works perfectly:

Trade # Direction Entry Exit Stop Hit? P/L % Notes
1 Long $42.10 $45.30 No +7.6% RSI crossed 50, trend was up
2 Short $88.50 $90.20 Yes -1.9% RSI crossed below 50 but reversed quickly

Then go back to your AI tool and paste the trade log with this prompt:

"Here is the trade history from testing the RSI-based strategy you provided. I tested it on 20 historical stock charts in a trading simulator. Analyze the results. Identify patterns in the wins and losses. Tell me if there are any rule modifications that might improve the strategy's performance based on the data, and explain the reasoning behind your suggestions."

The AI will analyze your results and often surface things you would not notice on your own. Maybe your short trades dramatically underperformed relative to your longs. Maybe your stopped-out trades all had something in common (e.g., RSI was in a strong trend when the signal fired). The feedback loop between your raw data and AI analysis is where real learning happens.

Note - You can also download your trades in the Trade History section of your account. This will include the stocks' tickers and date ranges that you traded, providing AI some additional context with which to analyze your performance.

Step 4: Refine and Run Another 20 Charts

Take the AI's suggested modifications, adjust the rules, and trade another 20 charts. Compare the two sets of results. This iterative process mirrors what quantitative analysts at professional trading firms do, just with simpler tools and manual execution rather than automated backtesting engines.

Over time, you are not just learning a strategy. You are learning how to think like a systematic trader, which is one of the most transferable skills in this industry.

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Citations

  • Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
  • JPMorgan. (2020). Perspectives on Algorithmic and High-Frequency Trading. JPMorgan Research.
  • Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.
  • Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47(5), 1731-1764.

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