Price action tools and utilities to help you interpret price have many benefits, as looking at naked charts can, for the untrained eye, can sometimes be overwhelming and cause confusion. Back in 2010 when I started programming software that I could use to help me understand price action, I based much of my focus on what I had observed whilst chatting with the institutional traders I got to know due to my work performing security assessments for banks. After many conversations with many traders, similar concepts continue to emerge, the details of which became my area of focus for the next many years.
What is price action?
Price action is simply the study of the movement of an asset’s price. Price doesn’t simply move up, down and sideways, as it is constantly moving towards willing buyers and sellers. When using price action to make trading decisions, the trader’s attention is typically focused on historical price data, as this is where the price action trader will attempt to make sense of what has happened in the past and study price patterns in order to identify where the market is most likely move in the future. Just remember that price doesn’t repeat itself, it rhymes.
Price action can be a very challenging area of focus, as for most people, the movement of price simply looks like random noise. Is some circumstances this can be true – but when trading patterns, which emerge as a result of price seeking liquidity, we need to remind ourselves that price is always looking for a better price and that volatility is a function of this.
Price action and the distribution of liquidity
Many of the casual talks I had with institutional traders, even before I was interested in trading, had themes, which always seemed to revolved around the following:
- Liquidity distribution
- The strength of an economy
- Time (cycles and trends)
Initially, this was all very interesting yet overwhelming, so I began my initial studies, looking at how liquidity was distributed, viewed from the sell side, as this is where my customers were positioned, being very large banks. Having access to this information was easy for me, as I was fortunate enough to have become as acquaintances of the director of IT and security on the trading floor, for one of the biggest banks in the world.
This was a very exciting time, as I was allowed to peek into the (in this case) big black box, which shed light on how the bank was able to establish a market based on the liquidity they had available. The retail market rarely had access to this information, as it was typically sold on to an intermediary, which established markets for us to engage in. That said, there were situations when professional clients were granted Direct Market Access (DMA) to via their bespoke applications using various protocols such as the Financial Information eXchange (FIX) protocol, which is an electronic communications protocol initiated in 1992 for international real-time exchange of information related to securities transactions and markets. This was also subject to scrutiny, during a handful of security assessments.
The interbank’s role in the retail investment space
Obviously, organisations are not willing to establish retail market places for investors and traders to participate for free, which is where fees such as commissions and spreads come in.
Most retail investors are unable to access the pricing available on the Forex interbank market (discussed earlier) unless they have a professional agreement, as interbank customers tend to be large financial institutions, including mutual funds, hedge funds and large multinational corporations who are simply engaging in the foreign exchange market whilst conducting business. It is important for retail investors to understand how the interbank market works, as it affects spreads and also plays a key role regarding the pricing of assets and trading expenses.
The foreign exchange interbank market
Initially, my studies introduced me to the concept liquidity distribution and support and resistance, as these seemed to be very key concepts that the banks were paying attention to, and here’s why:
Say a bank receives a big order from a client that needs to be put into the market place. The customer is eager to buy dollars when they are available at a certain price or better. An order of that size is generally settled interbank and sometimes, if the period of holding is short and the risk small, in-house. The entire order is generally sliced up into small orders, which are filled once price reaches the agreed price window. When this happens, the customer takes on risk in exchange for their order being filled. In other words, the trade is on, either partially or in full. Depending on the other market participants, price will either quickly move away from the entry level once the order is filled, or, the market will attempt to find a better price, and begin moving the aforementioned position into negative territory. In most cases, the bank will place the sub-orders above key levels to ensure that some are filled, and then below the key level to ensure a better average price for the entire position, which reminds me of the following.
During my breaks I would often head to the trading floor to have coffee and to ask questions, and my contact there was always welcoming and very friendly. On one specific day, I headed to IT department on the trading floor and we started asking questions regarding the concept of the necessity of sub orders when initial orders were very large. I was lead over to a small team of guys and one of the engineers continued to tell me how this particular system worked.
This system is called Libra. When we receive a client order over the desk or electronically via one of our APIs that is 2 million Euros or less, we don’t even look at it. Libra grabs it, does its magic, and then begins slicing it up into smaller sub-orders that placed throughout the agreed price window.
When the blocks are filled the client commits. Typically the market will continue to look for better asset prices and price will continue to move lower (or higher if selling) until the liquidity flips to one side. Then the market will begin moving away from the price window.
My questions started to flow from this point. What happens if only portions of the parent order are not filled?
Sometimes they are left in for a period of time or until they are set to expire.
Which brings us to another important point, which plays a key role for us as traders.
Locating institutional buy and sell zones
These orders that are left in the market and haven’t expired are important to us a retail traders. If we can find them before they expire then essentially we’d be riding on the backs on giants.
This is the essence of supply and demand trading. Price enters a price window, which is considered fair for a given asset – and once sufficient positions are filled to drive price away from fair value, a shift occurs, which the engineer I spoke with referred to as the flip. As traders, it is our job to identify fair value for the assets we trade and acquire them at these prices or less, not more, at price levels with a high probability of a flip occurring. The flip is simply when demand exceeds supply or when supply exceeds demand.
This is why I began to develop the supply and demand indicator for the MT4 platform (MT5 is currently under development), as I wanted a tool that would be able to visually show me where these flip zones are likely to be located on a price chart. In order words, I wanted to find price windows where buyers and sellers are willing to do business but where one side of the deal out weighed the other. This is what the result of the analysis will look like you your price chart:
The indicator colour- codes everything so you know what time frame a given area of supply no matter the chart time frame. You can see e.g. four hour, daily or weekly on any chart time frame you like, depending on your configuration. Once you’ve viewed a chart through our glasses, you’ll see things completely differently.
The algorithm we employ is elegant and simple, yet powerful. The price action expressing fair high/low/average values is presented to you using visual objects, on your chart, in a way that enables you to easily open positions and place stops and targets, hours, days or weeks before price gets there.
During the development of the algorithm, we created an algorithmic trader that we could use in order to identify optimal combinations of parameters and values, which has since then evolved into an automated trading utility called POOLS. The project continues to run and is currently being used by both retail and professional clients in varying configurations.
In this article we had a closer look at the concept of price action and we also touched upon liquidity, which is a concept I was introduced to via my discussion with banking and trading professionals. I hope the material presented above was of interest – and if you have any questions or comments I’d thrilled to hear from you.