Momentum trading is a strategy in which traders buy or sell assets according to the strength of recent price trends. Price momentum is similar to momentum in physics were mass multiplied by velocity determines the persistence with which an object will follow its current path, like a heavy train on a track. In financial markets, however, momentum is determined by other factors like trading volume and rate of price changes. Momentum traders bet than an asset price that is moving strongly in a given direction will continue to move in that direction until this trend loses strength or reverses. In this session, we will discuss how to identify momentum as a feature of a trading strategy. We will then look at the use of metrics such as moving averages and moving average ribbons to determine trade entries and exits in a momentum strategy. Last, we will evaluate the effectiveness of these metrics in generating trading profits. Creating features or feature engineering is one of the most important steps in using machine learning to develop a quantitative strategy. Before you create features you need to know what features you expect to use. Since we are dealing with momentum strategies here, let's understand a bit more about momentum. Our first step is to establish that a tradable momentum trend exists for an asset. You've probably heard before that a stock has momentum, but what do we mean by momentum? Momentum is a phase in which an asset appears to be moving based on past changes in prices rather than due to any stock specific fundamental or news. When prices move higher in reaction to higher prices is known as a Bull phase, and when prices move lower just because they'd been going lower it's known as a Bear phase. A momentum run is clearly seen with the candlestick charts where green represent up days and red represents down days. On the chart you can see that up appears to be in a bowl momentum phase for a few months in 2014 and is hugging what is known as a trend line. A trend line is a straight line that connects two or more points which can be temporary highs or lows, since stock prices tend to be trendy. Trend lines that connect the highest or lows in a stock's price history, can help identify the current trend and predict what the stock price might do in the future. Volume is critical to confirming momentum trends. How do you detect a trend? Most discretionary traders say they know a trend when they see it. Quantitative traders need a measure that is more objective but still versatile. They use moving averages to clearly see stock price trends that may not be apparent in a forest of price bars. The slope of a moving average's line can show you whether a stock is an upward or downward trend and can be used to generate trading signals. You'll see how to construct moving averages next. Momentum traders use moving or rolling averages to identify trends and specified trade entry and exit parameters. You saw earlier that a trend line is a good way to look at whether a stock is in a sustain momentum phase, either a Bull or Bear phase. The key word here is sustained. Trends don't tell us whether the trend will continue or will break abruptly. This is where moving averages and crossovers come in. Let's add some moving averages and look at the 2014-15 period when the stock was in a Bull momentum phase. We'll demonstrate this on the same chart you saw earlier, but with some added lines. The slope of the moving average line shows whether the stock is in an upward or downward trend. A simple moving average is just the average of the close price over a specified period. A 50-day simple moving average is the sum of all the closing prices in the 50-day period divided by 50. You can see this on the plot where the green line begins 50 days after the first price data point, and exponentially weighted moving average works the same as a simple moving average except the most recent prices are given more weight in the average than the older prices. This gives you an indicator that more closely reflects current market conditions while still including earlier data. A very common way to obtain a buy or sell signal is to look for moving average crossovers in stock charts. This means computing two moving averages of different lengths and waiting for one to cross over the other. The direction of the cross will indicate the direction of the momentum. At least that's what technical traders believe. Volume is once again a critical component in determining whether the crossover is a real change in trend or temporary. A Bear cross is when the shorter moving average moves below the longer moving average, and a Bull cross is when the shorter moving average moves above a longer moving average. Traders take a long position after a Bull cross and a short position in a stock after a Bear cross. They hold that position while the cross is in place. They switch positions when the bowl cross moves into a Bear cross or vice versa. We can see here that there are four points where the averages cross each other. Assume you are currently long the stock, the first two crossings come close to each other and indicate a quick sell and then buy back phase, but the second two crossings predict a longer up face. This can be one way to make profits using moving average crossovers. However, since most traders are watching the same signals, there is no guarantee these will work in the real world. For that you will need to develop more complex trading strategies based on more unconventional trading signals. Next let's look at how you choose the size of your moving average window or length. Choosing moving average length is very complicated. There are many choices of lengths such as 20,30, 50, 65, 200 et cetera. In fact, any integer is a possible choice. So the tendency is to look at the chart and find the two moving averages that seem to fit the given chart nicely, and then use that as a trading strategy for all stocks or even for the same stock in a future time period. This is known as over-fitting and you need to be aware of the danger of over-fitting. What works in one time period may not work in another time period, even for the same stock. The reason is the choice of lengths will strongly affect the signal you receive from your moving average crossover strategy. There may be better windows and you can attempt to find them with robust optimization techniques. However, it's incredibly easy to over-fit your moving window lengths. One approach is to use moving average crossover ribbons, withdraw many moving averages at the same time, an attempt to extract statistics from the shape of the ribbon rather than any two moving averages. You can see how ribbons look here. You can combine quantitative measures of ribbon shape and so generate a trading signal. Last we will look at two methods for evaluating or scoring the strength of a combination of moving average signals with different window lengths. The first is a distance metric and the second is correlation. One way of scoring is to use a distance metric. You can use a distance metric to see how far away from some given ranking our ribbon is. Here we combine ribbons to come up with a one to 10 ranking which we normalized to a score of zero to one. A perfectly increasing order of ribbons results in a score of zero. This can be a signal to buy or go long. A perfectly decreasing order of ribbons results in a score of one, this can be a signal to sell or go short. After ranking the rolling means and normalizing the scores, we plot them on the price chart. We see a number of strong cell signals followed by a weak buy signal another strong sell signal and then a weak buy signal. Next we will look at what happens after the final buy signal. When we look at what happens after the buy signal from the previous chart. We see that the stock price rises for bit and then drops by about 20-30 percent over the next few months, not good. So you have to constantly be on the lookout for better signals. Another slightly better way maybe to use correlation to rank ribbons. You can use a correlation metric, a perfectly increasing order of ribbons results in a score of one, this can be a signal to buy or go long. A perfectly decreasing order of ribbons results in a score of negative one, this can be a signal to sell or go short. First, we rank the ribbons using a Spearman's correlation and normalize the score once again. A score close to one means a buy signal and a score close to negative one is a sell signal. This is somewhat better since it triggered three sell signals pretty close to the short-term top of the stock. This may be worth exploring some more.
