Algorithm vs Liquidity In Determining PriceBased on my research into IPDA and algorithms, central banks, trading firms/hedge funds, and smaller banks use execution algos (EAs) for trading with different objectives. Small banks use EAs to split large parent orders into smaller child orders generally in one direction, buy or sell. These orders are executed separately over a period of time to either open or close positions.
Trading firms and hedge funds use opportunistic EAs to buy and sell to turn a profit.
Central banks use market making EAs to buy and sell in order to bring liquidity providers net positions back to or close as possible to neutral. (This sounds like equilibrium). Central banks use EAs cautiously and only during their main trading hours and always under the supervision of people.
A key reason for using EAs is to access multiple liquidity pools in order to reduce market impact or footprint.
This is similar to a parent child relationship between Central Bank algos and other smart money players, where smart money (including central banks) accumulate orders in consolidation before expanding price, then the central bank algo pulls them back to equilibrium like a parent calling their child that has strayed too far away. Then they rinse and repeat.
I am of the opinion that with the function of central bank algos to facilitate the provision of liquidity with minimal market impact, that liquidity itself is the determining factor in price delivery.
Algos used by smart money break up large orders in to smaller chunks and funnel them to multiple liquidity providers (market makers) for fulfillment since forex is decentralized. If there is enough liquidity (buyers and sellers) to open/close positions at a certain price then it is done at that price. When liquidity is low or there aren't enough buyers and sellers at the current price, the market maker's algo has to fill these received orders where there is enough liquidity based on available buyers and sellers. The algos move very quickly which can deplete available buy or sell orders rapidly leaving unfilled counter party orders in its wake which defines liquidity voids (imbalance).
Algo adjustments to meet buyers and sellers at their price is perceived as a stop hunt but it's just economics.
Example: If I must sell something and I want to sell it for $100 but no one is willing to pay $100, I would have to look for buyers willing to pay $95.
If I must buy something and I only want to pay $100 but the seller is charging HKEX:105 , then I have to pay $105.
Either the buyer crosses the spread to meet the seller or the seller crosses the spread to meet the buyer. When there are limit and stop orders the buyer or seller isn't moving so the liquidity provider has to move to meet these buyers/sellers at their limit or stop order prices (including orders left behind in liquidity voids).
When the orders trigger and price reverses it takes out both buyers and sellers so people call it a hunt, but I'm sure it is intended for actual institutional trading entities because retail traders such as ourselves can not provide the liquidity to be on the other side of every order placed by institutions.
We are simply collateral damage in the battle between financial titans seeking to provide and tap into liquidity.
