![]() ![]() ![]() They also include common probability distributions. The math behind HFT strategies generally involve statistical concepts such as normal distribution, standard deviation, or mean. They are not usually related to technical indicators and the models are a very well-kept secret with the institutions or traders that designed the model. HFT strategies use mathematical equations to define price action patterns. Hedge funds, CTAs and financial institutions are most likely to be using this type of strategy. HFTs generally use tick data or at the most one minute periods, to define the next movement in price. High-Frequency Trading (HFT) will use formulas that create many trading opportunities for small changes in price. The objective is to determine when price reaches a level where its next move is a reversal of the latest price action. Formulas can be determined by a set of various technical indicators such as RSI or Stochastic Oscillator. ![]() Mean reversion strategies attempt to determine when the market will reverse its current price direction. When the fast moving average closes below the slow moving average the strategy opens a sell trade. This strategy opens a buy trade when fast moving average closes above the slow moving average. An example of a common trend strategy is the double moving average crossover. ![]() The objective of trend strategies is to try and define the current direction and take positions that are in line with them. Or, a close below the lowest low in 20 periods equals a sell signal. The equations can be as simple as a close above the 50-period average equals a buy signal. Trend following strategies will make use of mathematical formulas that identify a trend. This model does not allow for short selling and will hold cash instead of selling assets short if there is an expectation of price decline. This differs slightly from stocks and bonds which may also include buy and hold strategies. In forex, quant trading is split up into three main categories: Trend Following, Mean Reversion, and High Frequency. This feature creates more discipline in money and risk management. However, in quant trading, they are embedded in the script and will not be subject to change, unless you change the script itself. These parameters are often used by retail traders. Some of the main rules to consider are stop loss, profit target, max number of trades per day, max number of losing trades, max allowable drawdown per day or week, etc. Together with the strategy itself, money and risk management rules need to be defined. Others use statistical evaluation and probability functions. Many make use of a mix of existing technical analysis tools, such as moving averages, MACD, or channel breakout patterns to define their entry and exit conditions. Many quant strategies tend to be quite complex and involve more than one feature. And over time the extra alpha (excess returns) generated by using the model will disappear. If the other competitors in the market know the inner workings of their model, then they will also be able to replicate it and apply it. The reason being that the quantitative trading models developed by the fund presumably give them an edge in trading the market. Most times, not even the investors in the hedge fund are fully aware of what computations the strategies perform exactly. The algorithmic formulas are well protected and guarded with extreme care. These strategies are often used by hedge funds and other financial institutions, sometimes known as black-box trading. The data inputs are used to identify patterns of price behavior over time. Quant strategies make use of mathematical computations using price, volume, and sometimes, time data to determine trading opportunities. We will take a look at the environment for quantitative analysis in trading and discuss what is available to retail traders. ![]()
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