Algorithmic trading strategies list: trend following, arbitrage, and market making
An algorithmic trading strategies list includes directional systems such as trend following, relative-value systems such as arbitrage, liquidity strategies such as market making, and execution algorithms such as VWAP or TWAP. These categories need different data, order access, latency, capital, and risk controls.
Key Takeaways
- Trend, breakout, and mean-reversion rules are usually easier to express on chart data.
- Arbitrage needs synchronized prices, hedging, fees, and execution across related products or venues.
- Market making needs two-sided quoting, inventory controls, order-book data, and fast order updates.
- A backtest models orders under assumptions and does not prove that live execution will match.
Directional strategies
Trend following, momentum, and breakout systems take a directional position when a stated condition occurs. Common inputs include price, moving averages, ranges, volatility, and volume. These strategies can often be written as bar-based rules, which makes them suitable for Pine Script strategies, MQL5 Expert Advisors, or cTrader cBots.
- Trend following: stay with an identified direction until an exit rule changes
- Momentum: trade continued strength or weakness over a defined horizon
- Breakout: enter when price leaves a specified range or level
Mean reversion and relative value
Mean-reversion systems expect a measured deviation to move back toward a reference. Pairs and statistical-arbitrage systems compare related instruments rather than one chart alone. The rules need a defensible relationship, synchronized data, hedge sizing, and a response when the historical relationship changes.
Arbitrage strategies
Arbitrage seeks pricing discrepancies between related products or markets. The SEC report on algorithmic trading describes examples involving an ETF and its underlying basket or futures and related ETFs. A real implementation must model both legs, fees, available size, fill timing, and the risk that one leg executes without the other.
Market-making strategies
A market maker places resting buy and sell orders and manages the inventory created by fills. The same SEC report notes that passive market makers need fast market data and technology that can update orders as conditions change. Chart-based strategy code does not provide that venue connectivity or queue management by itself.
Execution algorithms
VWAP, TWAP, participation, and implementation-shortfall algorithms focus on how to execute an existing order. Their objective differs from a signal strategy that decides whether to buy or sell. A useful test needs order size, market volume, spread, partial fills, and an execution benchmark.
Match the idea to the required infrastructure
Start with the data and order behavior the strategy needs. Bar-based directional rules can be simulated in TradingView, while MQL5 Expert Advisors can use MetaTrader trading events and functions. Arbitrage and market making usually need synchronized multi-venue data, direct order management, and latency controls beyond a chart script.
Pineify next step
Code the strategy types that match a supported platform
Pine Script AI Coding Agent can implement bar-based TradingView indicators and strategies. MQL5 AI Coding Agent can generate MetaTrader 5 Expert Advisors and indicators. Pineify does not provide exchange connectivity or market-making infrastructure, so arbitrage and liquidity systems need additional execution engineering.
This page is educational and does not provide investment advice. Algorithmic trading can lose money because of model error, execution costs, data problems, market changes, and operational failures. Simulated results do not guarantee live performance.