What are the main categories of systematic trading strategies (e.g. momentum, mean reversion), as might be considered by an index or fund-of-fund analyst?
Are there any common sub-strategies?
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There are other strategy types not covered by mean-reversion/trend following:
arbitrage - keep correlated assets close in price (SPX index versus the 500 stocks contained in it, or Gold trading in London versus Gold trading in New York)
market making - buy on bid, sell on ask, gain the spread
liquidity rebate - some venus pay you for putting limit orders in the book. Put in a limit order to buy, when it's hit try to sell at the same price that you bought at (or better) and gain the rebate. Works best on high volume, low price assets.
predatory trading - seek big hidden liquidity in the market and front-run it
behavioral trading - quantify market sentiment and trade on it (analyze tweets, determine global/regional mood and use known psychological theories to predict the effect on market behavior)
event trading - analyze news (electronic, paper, blogs, twits) and predict market impact of new relevant facts (litigation, new products, new management, ...)
There is no official taxonomy of quant trading models. After all, "valuations" are inherently subjective, no matter how much math we put behind them. But there are some industry-standard terms that might be helpful.
Inside the Black Box has the following break-down:
It's also possible to break-down by implementation:
And these don't even get into portfolio construction, position limits, risk monitoring, etc.
As for what works, keep this maxim in mind:
Bulls make money, bears make money, but pigs get slaughtered.
And lastly, comparing chartists to quants is like comparing astrologists to astronomers.
The categories for systematic trading strategies are
The description of these categories is below.
Chart Patterns: Different patterns are used to identify the trading signals such as head and shoulders, trend lines and support and resistance levels
Technical Indicators: These strategies mainly use the technical indicators such as RSI, MACD to determine the trading signals
Quant Trading Strategies: Advanced tools such as statistics are used to generate the trading signals. Example, statistical arbitrage using cointegration
Machine Learning Strategies: Different machine learning algorithms are employed such as very basic linear regression to more advanced LSTM (neural network)
With each of these main categories, there are different styles based on the frequency of trades such as low-frequency trading (LFT), medium frequency trading (MFT) and high-frequency trading (HFT)
There can be further subcategories categories based on the data used such as