Swing trading is a speculative trading strategy in financial markets where a tradable asset is held for one or more days in an effort to profit from price changes or 'swings'. A swing trading position is typically held longer than a day trading position, but shorter than buy and hold investment strategies that can be held for months or years. Profits can be sought by either buying an asset or short selling. Momentum signals (e.g., 52-week high/low) have been shown to be used by financial analysts in their buy and sell recommendations that can be applied in swing trading.
Using a set of mathematically-based objective rules for buying and selling is a common method for swing traders to eliminate the subjectivity, emotional aspects, and labor-intensive analysis of swing trading. The trading rules can be used to create a trading algorithm or "trading system" using technical analysis or fundamental analysis to give buy-and-sell signals.
Simpler rule-based trading 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 aligned in an upward direction, and only traded Short when the three averages are moving downward. Trading algorithms/systems may lose their profit potential when they 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 when to enter and when to exit a trade is the primary challenge for all swing trading strategies. However, swing traders do not need perfect timing—to buy at the very bottom and sell at the very top of price oscillations—to make a profit. Small consistent earnings that involve strict money management rules can compound returns over time.
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Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.
Day trading is a form of speculation in securities in which a trader buys and sells a financial instrument within the same trading day, so that all positions are closed before the market closes for the trading day to avoid unmanageable risks and negative price gaps between one day's close and the next day's price at the open. Traders who trade in this capacity are generally classified as speculators. Day trading contrasts with the long-term trades underlying buy-and-hold and value investing strategies.
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