Common Algo Trading Strategies and Examples

Algorithmic trading strategies are simply rule based systems that convert a trading idea into measurable, testable, and repeatable logic. Retail traders in India often approach automation by first picking a platform, but strategy selection comes earlier. Before you automate anything, you need to understand what kind of logic you are deploying, how it behaves, and what assumptions drive it. Most strategies fall into a few well defined categories used globally by quant teams, brokers, and institutional desks.

This guide outlines the most common algo trading approaches, how they work, and what their practical implications are when deployed in Indian markets. The goal is to help traders understand the structural behaviour of each strategy so they can test and modify it rather than rely on templates blindly.

Trend Following Strategies

Trend following systems try to capture directional moves. They assume that strong trends persist longer than expected and that price momentum can be quantified. These strategies are popular among positional index traders because they simplify market noise into a clear directional framework.

Trend following systems typically use moving averages, breakout levels, or volatility filters to define trend strength. For example, a simple two-MA crossover system triggers a long position when the fast MA crosses above the slow MA. More advanced systems use indicators like ADX or Donchian channels to avoid weak trends.

The strength of trend following is its simplicity and robustness across instruments. The drawback is the long periods of sideways churn where whipsaws are frequent. In Indian indices like Nifty and Bank Nifty, trend followers often require volatility filters to avoid false signals during high IV but low directional intent.

Mean Reversion Strategies

Mean reversion assumes that extreme price moves revert back toward an average. These systems work best in range-bound environments, short-duration timeframes, and liquid assets. In India, mean reversion is widely used in intraday index trading because Bank Nifty and Finnifty frequently oscillate around intraday VWAP bands.

A typical mean-reversion rule might buy when price dips two standard deviations below a moving average and exit when it reverts back to the mean. Examples include RSI-based reversals, Bollinger band pullbacks, and VWAP deviations.

The advantage of mean reversion is its high win rate and quick trade cycles. The risk is the occasional trend day where price never returns, causing large losses. Such systems require strict stop losses and volatility filters to prevent runaway trades.

Breakout Strategies

Breakout strategies look for price escaping a defined range. These systems assume that consolidation often precedes expansion, and that once a level breaks, momentum typically continues. In India, breakouts are common around opening ranges, previous day highs/lows, and round number zones.

A breakout system might enter when price closes above the previous day high with confirmation from volume or ATR expansion. Some traders combine breakout rules with trend filters to reduce false signals.

Breakouts benefit from asymmetric payoff structures, where a few strong moves cover many small losses. The main challenge is noise during low liquidity periods, especially in stock derivatives where spreads can widen unexpectedly.

Momentum Strategies

Momentum systems identify strong directional acceleration and aim to participate early. Unlike simple trend following, momentum focuses on speed and magnitude of moves rather than directional bias alone.

Indicators used include ROC (rate of change), momentum oscillators, or multi-timeframe confirmation. Momentum is especially relevant in Indian stocks where sector rotation causes sudden rallies in midcaps or thematic baskets.

These systems require tight execution because momentum fades quickly. Slippage, latency, and spread impact become meaningful, especially in intraday systems.

Volatility Based Strategies

Volatility driven strategies focus on price expansion, contraction, or volatility skews. They are commonly used in options trading, where volatility defines both opportunity and risk.

Examples include:

• Straddle/strangle entries when IV is low relative to realised volatility
• Iron condors during compression phases
• Directional option buying after volatility expansion

Volatility strategies require an understanding of how Indian options behave around events like RBI policy, weekly expiry, and gap-open tendencies.

Statistical Arbitrage Strategies

Stat arb strategies attempt to exploit pricing inefficiencies between correlated assets. They require clean data and strong modelling but remain popular for traders using pairs or basket trades.

A common example is trading the spread between two historically correlated stocks. If the spread widens beyond a statistically significant deviation, the system enters mean-reversion positions.

While effective in stable structures, stat arb can break when correlations shift due to fundamental changes. Indian markets see frequent regime changes, so these systems require constant recalibration.

Example Strategy Structures

A few simplified examples to show how the logic translates into rules:

• Nifty Trend Breakout: Long above 20-period high only if ATR > threshold and RSI > 50.
• Bank Nifty Mean Reversion: Buy at lower Bollinger band, exit at mid-band, avoid trades when IV percentile > 80.
• Momentum Stock Filter: Buy stocks with ROC > 5 percent over 5 days and volume > 20-day average.
• Options Volatility Compression: Sell straddles when realised volatility remains below implied volatility for three consecutive sessions.

These are not plug-and-play systems. They illustrate how rules create structure.

Where Stratzy Fits in Strategy Development

With Stratzy’s organised models on Stratzy traders gain clarity on how strategies behave in different conditions. That clarity makes algo trading smoother and more disciplined.