Common Algorithmic Trading Strategies Explained With Simple Examples
Algorithmic trading relies on rules, data, and repeatable logic rather than discretion. While every strategy may look different in code, most of them fall into a few major categories that describe how they respond to price behaviour. Understanding these categories helps traders decide what type of logic fits their risk tolerance, execution environment, and market conditions. This guide breaks down the most commonly used algorithmic trading strategies and explains them with simple, realistic examples that match how Indian markets behave.
Trend Following Strategies
Trend following tries to capture directional movement. The logic is simple: if prices keep making higher highs and higher lows, the strategy stays long, and if they reverse, the strategy exits or flips short. These systems rely on breakouts or moving-average confirmation.
A practical example is a 50/200 moving average crossover. When the 50-day average crosses above the 200-day, the strategy assumes that momentum has shifted upward and enters a long position. When it crosses back below, it exits. This style is slow, reduces noise, and works best during sustained moves in indices like Nifty and Bank Nifty.
Trend followers accept lower win rates because their profits come from occasional strong trends rather than frequent small wins.
Mean Reversion Strategies
Mean reversion assumes that price temporarily deviates from its average but eventually returns to it. These strategies work well in range-bound markets where prices oscillate between support and resistance.
A simple example is RSI-based mean reversion. When RSI drops below 30, the system buys expecting a rebound. When RSI crosses above 70, it exits. In sideways markets, this can generate steady returns if transaction costs and slippage are kept under control.
Mean reversion typically has high win rates but is vulnerable to sharp trends that break the range.
Breakout Strategies
Breakout strategies react when price moves beyond a well-defined level. Instead of predicting direction, they wait for confirmation.
For instance, a system may buy when Nifty closes above the previous 20-day high with above-average volume. This signals accumulation and potential continuation. Breakout strategies often overlap with trend-following logic but operate at shorter timeframes and use more reactive triggers.
Breakouts require clean execution and proper risk limits because false breakouts are common.
Statistical Arbitrage Strategies
Stat-arb strategies rely on statistical relationships rather than chart patterns. A simple retail-friendly version is pair trading. If two historically correlated stocks suddenly diverge, you go long the underperforming stock and short the outperforming one, expecting the spread to revert.
For example, if HDFC Bank and ICICI Bank usually move together but HDFC drops unusually while ICICI holds steady, the system bets on the spread normalising.
These strategies require clean data and strict risk rules since correlation breakdowns can be costly.
Momentum Strategies
Momentum systems buy assets that are already moving strongly in one direction. The logic is that recent winners tend to continue performing well for some time.
A simple example is ranking Nifty 100 stocks by their 3-month returns and buying the top 5. Many global equity portfolios use similar relative momentum filters.
Momentum strategies are sensitive to regime shifts but can outperform strongly when markets trend broadly.
Volatility-Based Strategies
Volatility strategies use metrics like ATR, IV, or price ranges to detect expansion or contraction. They either trade volatility directly or adjust position sizing dynamically.
One basic version uses ATR breakout filters. When ATR rises sharply, the system anticipates a move and shifts into breakout mode. When ATR compresses, the system scales down or switches to mean-reversion logic.
This category is widely used in options trading because volatility defines premium behaviour.
Event-Driven Strategies
Event-driven algorithms react to known triggers such as earnings, macro announcements, or scheduled expiries. At the retail level, the most common event-driven system involves expiry-day options strategies. For example, a trader may sell an iron condor every Thursday morning with strict stop-loss rules.
Although event-driven systems can be profitable, they require disciplined exposure control because event reactions vary widely across months.
Hybrid Strategies
Many robust algorithms combine more than one behaviour. For example, a hybrid Nifty system might:
- trade breakouts during high volatility phases,
- switch to mean reversion during tight ranges, and
- size positions based on volatility.
This improves consistency across regimes and reduces dependency on a single market condition.
Why Understanding Strategy Categories Matters
Knowing how a strategy behaves: trend, range, volatility, momentum, helps traders choose the right risk limits, testing framework, and complementary strategies. Each style has strengths and weaknesses depending on the regime. A well-structured portfolio usually includes multiple behaviour types rather than relying on one method.
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