Swing trading is commonly defined as a stock, index, or commodities trading practice whereby the instrument is bought or sold at or near the end of an up or down price swing caused by daily or weekly price volatility. A swing trade position is typically open longer than a day, but shorter than trend following trades or buy and hold investment strategies. Swing traders engage in prospecting changes in an instrument's price caused by oscillations between its price being bid up by optimism and alternatively being sold down by pessimism over a period of a few days, weeks, or months. Profits can be sought by engaging in either Long or Short trading.
Swing trading methods
A Predictive market trading algorithm or Trading System is defined as a calculable set of trading rules that uses either technical analysis and/or fundamental analysis and results in entry, exit and stop loss trade price points. Trading algorithms are not exclusive to swing trading and are also used for daytrading and long term trading. Investment in researching trading algorithms/systems has skyrocketed, particularly by investment banking firms like Goldman Sachs which spends tens of millions on trading algorithm research, and which staffs its trading algorithm team more heavily than even its trading desk. The goal of trading algorithms is to remove the subjective decision making from swing trading and can be as esoteric as extrapolated biology theories like neural networks applied to derivatives trading by Rutgers University's Gang Nathan Dong. Simple approaches include Alexander Elder's strategy which measures the behavior of an instrument's price trend using three different moving averages of closing prices. The instrument is only traded Long when the three averages are up, and only traded Short when the three averages are down. Trading algorithms/systems may lose their profit potential when their strategies obtain enough of a mass following to curtail their effectiveness: "Now it's an arms race. Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits," observes Andrew Lo, the Director of the Laboratory For Financial Engineering, for the Massachusetts Institute of Technology.
Identifying whether a market is currently trending higher or lower, or trading sideways and when this will change is a challenge for many swing trading and long-term trend following trading strategies. Swing traders do not need perfect timing - to buy at the bottom, and sell at the top of price oscillations. Small consistent earnings that involve strict money management rules can compound returns significantly. Most important is to understand that there is no foolproof mathematical model or algorithm that will always work so only use them as research tools not decision making engines.
Risk of loss in swing trading typically increases in a trading range or sideways price movement, than in a bull market or bear market that is clearly moving in a specific direction because of the increased potential for whipsaws or false positives. In trending markets (either a bear market or a bull market), momentum may carry the traded instrument's price for a much longer than usual time in one direction only, making swing trading strategies that do not incorporate this trending, less profitable than trend following strategies.
As with all financial instruments risk of loss in trading is considerable, and only mitigated by the trading strategy that is back tested on any particular equity, index, or commodity, and continues to prove its worth with successful trades.