Trading Strategy Definitions

Automated Trading uses computer algorithms to execute trades based on predefined rules and conditions. It eliminates emotional decision-making, increases efficiency, and allows traders to implement complex strategies across multiple markets with minimal oversight.

Breakout Trading is a strategy that involves entering trades when the price moves outside a defined support or resistance level. Traders anticipate continued momentum in the breakout direction, seeking quick profits from strong price movements.

Day Trading involves buying and selling financial instruments within the same trading day. The goal is to profit from short-term price fluctuations. Day traders often use leverage and focus on technical analysis to identify entry and exit points.

Discretionary Trading relies on the trader’s judgment and experience to make decisions rather than following strict rules or algorithms. It allows flexibility but can be prone to emotional biases and inconsistent results.

High-Frequency Trading (HFT) is an automated trading strategy that uses powerful computers and algorithms to execute a large number of orders at extremely high speeds. HFT aims to capitalize on small price discrepancies across markets.

Mechanical Trading Systems are rule-based strategies designed to eliminate emotional interference from trading decisions. These systems follow strict criteria for entry, exit, position sizing, and risk management to ensure consistency and objectivity.

Mean Reversion is a strategy based on the assumption that prices will eventually return to their historical average or equilibrium level. Traders profit by buying undervalued assets and selling overvalued ones, anticipating price normalization.

Momentum Trading involves buying assets that are trending upward and selling those trending downward. The strategy is based on the idea that strong price trends are likely to continue in the same direction for a period of time.

Pair Trading is a market-neutral strategy that involves buying one asset and simultaneously selling a related asset to profit from the price difference. It seeks to exploit relative value discrepancies while minimizing exposure to broader market movements.

Position Trading is a long-term strategy where traders hold assets for weeks, months, or even years. It relies on fundamental analysis, trend-following systems, and macroeconomic factors to identify profitable opportunities.

Quantitative Trading uses mathematical models, statistics, and algorithms to identify and capitalize on trading opportunities. It emphasizes data analysis, backtesting, and rigorous testing to ensure consistency and profitability.

Scalping is a short-term trading strategy focused on making small, frequent profits by executing numerous trades within minutes or seconds. Scalpers rely on tight spreads, high liquidity, and rapid execution to minimize risk.

Statistical Arbitrage involves identifying and exploiting statistical inefficiencies between related financial instruments. It uses quantitative models to detect temporary mispricings and profit from price corrections over short time frames.

Swing Trading aims to capture price movements over days or weeks by taking advantage of market “swings.” Traders seek to profit from upward or downward trends before they reverse, using technical and fundamental analysis.

Systematic Trading relies on pre-established rules and algorithms to make trading decisions. It eliminates emotional biases, increases consistency, and allows for scalable and repeatable trading processes across various markets.

Trend Following is a strategy that seeks to profit from sustained market trends. Traders enter positions when a trend is established and hold them until signs of a trend reversal appear. It is commonly used in stocks, forex, commodities, and futures markets.