Trend-Following Systems
Overview
Trend-following systems identify and ride sustained directional moves using rules-based approaches — moving average crossovers, breakout channels (Donchian, Keltner), and momentum filters. The strategy accepts many small losses for occasional outsized winners, relying on fat-tailed distribution of returns in financial markets.
Key Concepts
Moving average crossover systems (dual, triple). Channel breakouts (Donchian 20/55-day). Volatility-adjusted position sizing (ATR-based). Turtle Trading rules. Time-series momentum (absolute momentum). Cross-sectional momentum (relative strength). Trend filter (200-day MA, ADX) for trade qualification.
Entry Signals
Price closes above Donchian channel high (breakout entry). Fast MA crosses above slow MA with ADX > 20. Price above 200 EMA and RSI above 50 (momentum confirmation). New 52-week high with expanding volume.
Exit Signals
Price breaks below trailing stop (chandelier exit or ATR-based). Opposite channel breakout. Moving averages cross back. Time-based exit if trade hasn't moved in defined period. Trailing stop: 2-3 ATR from highest close.
Best Timeframes
Daily, Weekly — trend-following historically performs best on daily and above. 4H can work for more active management.
Pro Tips
Trend-following has long drawdown periods (30-50% of the time). Diversify across many uncorrelated markets to smooth the equity curve. Never second-guess the system — the edge comes from consistency over hundreds of trades, not from any single trade.
More Topics in This Category
Algorithmic Trading Strategies
Algorithmic trading uses computer programs to execute trades based on predefined rules — from simple moving average crossovers to complex machine learning models. The key advantage is removing emotion, achieving consistent execution speed, and backtesting strategies across decades of data before risking real capital.
Statistical Arbitrage
Statistical arbitrage (stat arb) exploits temporary mispricings between correlated assets using quantitative models. Pairs trading — the simplest form — goes long the underperformer and short the outperformer when their price ratio deviates beyond historical norms, profiting as the spread reverts to the mean.
Grid Trading
Grid trading places buy and sell limit orders at regular price intervals above and below the current market price, creating a 'grid' that profits from range-bound oscillations. Each buy order has a corresponding sell order at a higher grid level, generating profit from each completed round trip. It works best in sideways markets and can be automated easily.
Backtesting & Monte Carlo Simulation
Backtesting applies a trading strategy to historical data to evaluate how it would have performed, while Monte Carlo simulation randomises the order of those historical trades across thousands of iterations to understand the range of possible outcomes. Together, these techniques provide both an estimate of expected performance and a probabilistic view of risk, drawdown, and return distributions.