* Trend. All strategies try to anticipate the trend, but the current trend of a price curve itself can be used to predict future prices. Traders tend to follow the mass: when some buy, others start buying too. This causes a sequence of price movements in the same direction that can be detected and exploited. Trend following is one of the most often used strategies, but also one of the most difficult: the crucial point is detecting a beginning trend as early as possible, while at the same time preventing false signals. An example of a trend trading strategy can be found in Workshop 4.

* Mean reversion (counter-trend). Traders often believe in the existence of a 'fair price' of an asset. They buy when the actual price is cheaper than it ought to be in their opinion, or sell when it is more expensive. This causes the price curve after going in a certain direction often to reverse back to the mean. An example of a mean reversion strategy can be found in Workshop 5.

* Cycles. When a trade is profitable, traders often sell after a certain time for taking profits; unprofitable trades are sold after a different time. This has often the effect to synchronize entries and exits among a large number of traders, and causes the price curve to oscillate with a period that is stable over a relatively long time span. Such a cycle can be detected with spectral analysis functions and used for predicting the price trend. (Those cycles in price curves are real and unrelated to the imagined "waves" mentioned below).

* Clusters. Traders often believe in a 'real price' of a certain asset, and sell or buy a position at the moment when the price curve reached that value. This is the reason that prices sometimes cluster at certain levels and produce the famous "support" and "resistance" lines (also called "supply" and "demand").

* Curve patterns. Traders believe that price movements are preceded by certain curve patterns. Most of them - such as the famous "head and shoulders" pattern - are myths. But some patterns, for instance "cups" or "half-cups", can indeed precede an upwards or downwards movement and can be exploited by pattern-detecting methods such as the Fréchet algorithm.

* Price action. Certain configurations of the open, close, high, or low prices of 3, 4, or 5 consecutive candles are said to predict price movements. Problem is that predictive candle patterns can't be found in books - they depend on the market, the broker's liquidity providers, and on the time zone, and thus change all the time. But done the right way, price action trading can indeed generate profit with candle patterns that are found by statistical analysis. An example for detecting profitable candle patterns with a machine learning algorithm can be found in Workshop 7.

* Price cap. Sometimes a governement establishes a floor or ceiling for its currency exchange rate. Interventions prevent that the exchange rate exceeds a certain limit - a famous example is the above mentioned 1.20 ceiling of the EUR/CHF rate. This can be used in strategies to the trader's advantage.

* Seasonality. "Season" does not necessarily mean a season of a year. Monthly, weekly, or daily trading behavior at large stock exchanges, such as the NYSE, often follows a certain pattern that can be exploited by strategies. For instance, the S&P500 index often moves upwards in the first days of a month, and also has often an upwards trend in the early morning hours before the main trading session of the day. You can detect seasonal effects with Zorro's Profile functions.

* Gaps. When an asset is traded during certain market hours only - such as stocks and stock indices - tomorrow's market open price can sometimes be predicted to some degree from the trading behavior at today's market close time.

* Arbitrage. When two assets are known to be strongly correlated, strategies can exploit the fact that their prices tend to regularly end up at the same level or the same ratio, and derive profit from temporary price deviations.

* Price shocks. They often happen on Monday or on Friday morning when companies or organizations publish good or bad news that affect the market. Even without knowing the news, a strategy can detect the first price reactions and quickly jump onto the bandwagon.