Algorithmic and Mechanical Forex Strategies | OneStepRemoved

Top 3 Indicators To Use For Short Term Trading

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Short term trading can be an incredibly lucrative process. However, without proper analysis, it can also be a process that leads to big losses. The good news is that there are several tools that make analysis a simple process. Today, we’ll talk about the top 3 indicators for short term trading. So, let’s get right to it…

Short Term Trading Indicator #1: MACD

The MACD is also known as moving average convergence divergence. This is an incredibly popular indicator that is used not only to show whether or not a trend is in process as well as the momentum of the trend associated with any security. This indicator is made of of to EMA’s or exponential moving averages that cover two different time frames, generally these are 12 and 26-period time frames. The MACD, which is the actual indicator is the difference between the two moving averages. The higher the number, the stronger the directional momentum of any given trade. The lower the MACD, the less momentum the trend has. Click to learn more about using the MACD for Short Term Trading Strategies.


The MACD uses a histogram to spot buying and selling opportunities.

The MACD uses a histogram to spot buying and selling opportunities.

Short Term Trading Indicator #2: On-Balance Volume

On-balance volume, also known as OBV is a technical indicator that tracks the positive and negative flow of volume on any security and what that volume flow has to do with the price movement in that security. OBV is a number that shows the average volume on a stock by adding or subtracting each trading session’s value depending on the price movement within that session. Essentially, trading volume is what causes price movement. High volume generally indicates that gains are ahead while low volume generally indicates that declines are coming. Therefore, short term traders watch the OBV on securities to see if the number is going up or down. This gives them an idea of what to expect in the price of the security over a short period of time. Click to learn more about using the On Balance Volume Indicator.

Short Term Trading Indicator #3: Average Directional Index

Essentially, short term traders make money by taking advantage of strong trends in the value of a stock. So, the Average Direction Index or ADX comes in handy. The ADX is an indicator that focuses on trend momentum instead of directional changes. When the ADX on a financial asset is below 20, it means that the current trend is weak, if the asset is even trending at all. However, if the ADX is above 40, it means that the current trend has strong directional strength. So, short term traders tend to look for assets with a high ADX as a high ADX means that momentum will likely keep the trend headed in the same direction, making taking advantage of the trend relatively simple. Click to learn more about using the ADX indicator when trading.

Final Thoughts

Like a contractor, any successful short term trader has a tool box that’s filled with tools that make his or her job easier. Ultimately, without properly analyzing trends, short term trading is nothing more than gambling. However, by taking advantage of indicators that are known to lead to productivity in trading, the ball is put in your court, giving you the ability to somewhat peek into the future. Now, no one has a crystal ball that allows them to see exactly what’s going to happen in the market moving forward. However, using strong indicators like the tools listed above, can give you such a high level of accuracy that you may feel as though you have a crystal ball. So, what are you waiting for? Add these tools to your repertoire, and start short term trading with improved accuracy.

Order book Slope

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I used to make levered bets in the option market during earnings season. Pick a direction, choose an option near the money and close to expiration, then hope. It seemed just as stupid as buying a plane ticket to Las Vegas and betting a black chip on red. By the time all expenses were weighed, I was always charged a premium for entertainment.

I have since learned a new orderbook measure that could provide an edge. It’s an idea that has been around for some time, but has plenty of room for optimization. The math is somewhat cumbersome, but I though I would take a few posts and explain why I might cancel my future trips to Vegas.

Limit Order book Information

When collecting data for an algorithmic trading system, the two most common pieces of information are quoted price, and volume. That said, it would be silly to think that information contained in the limit order book contains no excess details toward the price formation process. Not only do you know the current price, but you also know the current price that others are demanding! Creating a solid model of the order book is the key to understanding aggregate liquidity and trading interests of market participants.

Research in order books has found a significant relationship between volume, volatility, and a value known as order book slope. The ideas are somewhat nontrivial on the surface, so I want to summarize the key points and mathematics for easy implementation.

Order book Slope

If you Google around, the correlations between volume and volatility (typically denoted as the “Volume-Volatility Relation”) are used for several liquidity measures in the market. The Volume-Volatility Relation has empirical findings in stocks, bonds, currencies, and futures, though I will stick to equities in this post for demonstration.

To apply Volume-Volatility to order books requires a notion of slope in a limit order book. Consider the following example.

Orderbook Slope

Two different types of stocks show very different slopes in their order books. The idea of a line connecting the dots intuitively explains the idea of order book slope.

In a market frozen in time, each circle in this example represents a value in the limit order book waiting to be consumed by a market order. In a simple equity market, each tick on the x-axis represents one penny movement in the stock price, while the y-axis represents the total aggregate volume on each side of the market.

The main observation you should be making is the shape of the volume in the market for these two hypothetical securities. The new IT firm seems to be more linear in creating a v-shape, while the blue chip firm has more curvature as it flattens out at the top.

The intuition is there too. Really, take a hard look at Figure 1 and think about it. Blue chip companies like Apple and Google have great volume and perhaps a tighter consensus of price during a typical day of trading. Therefore, it is reasonable to think that just about 100% of the limit order book volume occurs within 5 ticks of the current market price.

On the other hand, think about those penny stock start up companies we have all been enticed to buy at some point of our trading career. Companies with unknown valuation and future potential will have limit order book volume spread ten or more ticks from the current market price. Some people think these companies are doomed to tank as a product of price inflation, while others may own the product produced and realize the potential as early investors. The two diverse opinions will have valuations that come out drastically different and causing the plots above.

Aggregate Orderbook Slope

Now consider what happens over time. If you collect data over time and take an average, the result will appear as in Figure 2.

Aggregate Limit Orderbook Volume

Order book slopes published by Naes and Skjeltorp

Here, about 50% of volume in the blue chip firm has limit prices within 5 ticks (left) while only 10% of volume is within 5 ticks for the young IT firm on the right.

The published research finds a negative relationship between (average) order book slope and the variation of analysts’ earnings forecasts. This means that as the slope becomes more linear (right plot of figure 2) investors disagree more about the value of the firm and thus higher volatility will likely result.

Creating a solid model of the order book is the key to understanding information about aggregate liquidity and trading interests of market participants.

In fact, the order book slope has been shown to have relation to volatility, volume, and the correlations between volume and volatility– all three of which could be profited on.

Surprisingly, in the mid 1990’s, people were studying the shapes of these curves and relating them to the different liquidity providers present in the market. The shapes were shown to find the probability of informed traders completing market orders at any given moment! See the Glosten model for more info.

Making a precise definition of order book slope requires a blog post of explanation of itself. The math looks daunting at first glance, but as I will explain later, has great intuition, elegance and simplicity. Along with that, the formulas are tangible and should leave you with a relatively simple programming problem (assuming you have the data)

Support and Resistance Metric

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Converting back of the envelope ideas to trading algorithms is extremely challenging, but requires significant attention. This could be the single item that makes your system great. In this post, I suggest a way to find more defined support and resistance metric using limit order books.

Creating a Order Book Metric

To start, I want to create a frozen moment in time where two order books appear as in the following picture. For the sake of this example, I assume other signals have detected a $20.00 resistance level.

Support Resistance Metric Limit Order Book Comparison

Figure 1: Comparison of two limit order books during a suspected $20.00 resistance level.

Blue represents the limit bid and orange the limit ask. The order book on the left suggests strong resistance at 20, while the order book on the right suggests weakness . If you’re not convinced, see my post on the support and resistance in the limit order book.

The idea above is great in theory. How can you quantify the above two situations so that it’s actually useful? Otherwise, it’s just another fluff technical analysis piece with no real substance.

A successful method applied to Figure 1 should determined if a short position should be entered at $20.00 (left) or not (right).

The first of two decisions that you must make is how far to look into the order book — a value I will call N. The above two order books have N = 4 because each side of the book contains orders at 4 different price levels.

Of course this will depend on your access to data. However, you might also find through back testing that increasing N provides no useful information.

The next decision you have to make is how to weigh the volume in the order book.

You might be foolish to think that each level of the order book is created equally.

For simplicity, I am going to describe a linear weighting system where ticks closer to the price midpoint are given more weight. Referring to the first order book example, N=4 implies we need four weights on the ask side. My system has weights that satisfy the following conditions:

  • w1 – corresponds to $19.99
  • w2 – corresponds to $20.00
  • w3 – corresponds to $20.01
  • w4 – corresponds to $20.02
  • w1+w2+w3+w4 = 1
  • w1 > w2 > w3 > w4

These weights essentially alter the volume in the order book so that overcoming the level of resistance is a more pronounced event. Here’s how you can choose them for a linear case.

  • w1 = N = 4
  • w2 = (N-1) = 3
  • w3 = (N-2) = 2
  • w4 = (N-3) = 1
  • total = w1+w2 + w3 + w4 = 10

Through a process of normalization, the final weights are found as follows

  • w1 = N/total = 4/10 = .4 = 40%
  • w2 = (N-1)/total = 3/10 = .3 = 30%
  • w3 = (N-2)/total = 2/10 = .2 = 20%
  • w4 = (N-3)/total = 1/10 = .1 = 10%

Notice that the whole system comes by simply defining N, thus is easy to generalize and back test in your trading system.

The next step is to compute the average weighted price that would have to be paid if a market buyers chooses to pass the level of resistance. Again, I will refer to figure 1 on the ask side of the market. This will show the difference between a strong level of $20.00 resistance (left ask book) and a weak level of resistance (right ask book)

Support Resistance Metric Limit Order Book Comparison

Figure 1 repeated

Average price (left ask book) : $19.99*2+$20.00*8+$20.01*4+$20.02*5/19 = 20.0063

Average weighted price (left ask book) : $19.99*2*w1+$20.00*8*w2+$20.01*4*w3+$20.02*5*w4/(19) = 20.0022

Average price (right ask book) : $19.99*2+$20.00*8+$20.01*1+$20.02*1/12 = 20.0008

Average weighted price (right ask book) : $19.99*2*w1+$20.00*8*w2+$20.01*1*w3+$20.02*1*w4/(12) = 19.9989

From the weighting system, you can see that a assumed resistance of $20.00 is passed by only paying an average (weighted) price per share of $19.998 during a time of weak resistance.

The weighted average price shows more defined support and resistance levels

Generating Signals

Finally, to generate a solid metric, I considered the percent movement a stock would have to take to move past a weighted average price. For example:

Left ask book (strong resistance) : 100*(current price – average weighted price)/current price = 0.11%

Right ask book (weak resistance) : 100*(current price – average weighted price)/current price = 0.094%

My rule is set so that if the current price must move over 0.10% weighted average order book price (for a given value of N) then the resistance level is strong. Less that 0.10% makes me think the resistance is weak, and would signal for me to not initiate a short position.