For traders who are just starting to explore algorithmic trading, the best approach is to begin with simple, rule-based strategies. These strategies do not require advanced math or machine learning, and they can be built using basic Python scripts. By understanding these foundational strategies, beginners can learn how markets behave, how trading rules are executed by a bot, and how to manage risk effectively.
Below are the most popular and beginner-friendly algorithmic trading strategies.
1. Moving Average Crossover Strategy
This is often the first strategy new algorithmic traders learn because it is simple and easy to automate.
How it works:
- Choose a short-term moving average (e.g., 20-day MA).
- Choose a long-term moving average (e.g., 50-day MA).
- Buy signal: short-term MA crosses above long-term MA.
- Sell signal: short-term MA crosses below long-term MA.
Why beginners like it:
- Easy to code
- Removes emotional decision-making
- Works well in trending markets
This strategy teaches the basics of signal generation and backtesting.
2. RSI Overbought/Oversold Strategy
The Relative Strength Index (RSI) is a simple and useful indicator for detecting market reversals.
How it works:
- RSI > 70 β asset may be overbought β generate a sell signal
- RSI < 30 β asset may be oversold β generate a buy signal
Why itβs beginner-friendly:
- Only one indicator to track
- Good for range-bound markets
- Easy to automate and tune
Beginners learn how oscillators work and how to avoid buying too high or too low.
3. Breakout Strategy
The breakout strategy aims to capture strong moves when the price escapes a defined range.
How it works:
- Identify recent support and resistance levels
- Buy when price breaks above resistance
- Sell when price falls below support
Benefits for beginners:
- Simple rules
- Works well in volatile markets (e.g., crypto)
- Can be improved with filters like RSI or volume
This strategy teaches traders how to detect market momentum.
4. Simple Mean Reversion Strategy
Mean reversion assumes that price will return to its average after moving too far in one direction.
How it works:
- Calculate a moving average
- If price drops far below the average β buy
- If price rises far above the average β sell
Why beginners like it:
- Easy to visualise and code
- Works well in sideways markets
- Good foundation for advanced stat-arb strategies
5. Grid Trading Strategy
Grid bots are especially popular in crypto because prices move up and down within ranges.
How it works:
- Set multiple buy and sell levels above and below the current price
- The bot automatically trades small movements within that grid
- Profit comes from capturing small fluctuations many times
Why itβs beginner-friendly:
- Requires no complex indicators
- Easy to automate
- Generates consistent small gains in choppy markets
This strategy teaches users about automation, order placement, and market structure.
6. Dollar-Cost Averaging (DCA) Automation
Although simple, DCA is still considered a basic algorithmic strategy.
How it works:
- Invest a fixed amount at regular intervals
- Automatically buys regardless of price
Why beginners use it:
- Reduces emotions and timing risk
- Very easy to automate
- Good for long-term accumulation
This is often the first automation new traders implement.
Conclusion
Basic algorithmic trading strategies are the perfect starting point for beginners because they are easy to understand, easy to code, and easy to test. Once traders gain confidence with these strategies, they can move toward more advanced topics like machine learning, statistical arbitrage, and high-frequency execution.