Source: https://www.babypips.com/forexpedia/slippage

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Slippage generally occurs when the bid/ask spread (the difference between the highest price a buyer is willing to pay and the lowest price a seller is willing to accept) changes between the time an order is requested and the time that the order is executed. These changes in asset price can be self-induced when placing orders of significant magnitude, as your own alteration to the supply and demand cycle can alter the price before the trade executes due to keen-eyed traders and algorithms observing your order’s placement. Slippage can also occur when there is insufficient volume at the given price point to sustain the current bid/ask spread, i.e. when there are not enough buyers and/or sellers at the given levels.

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Slippage can be positive in scenarios where the expected and actual prices of an asset benefit the trader in question, yielding a profit in cases such as the bid price increasing in short trades. However, slippage is more often negative, incurring a loss in the events such as the ask price increasing in long trades. The following hypothetical example taken from Investopedia may help in understanding the mechanics of negative slippage:

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• A given bid/ask price is $183.50/$183.57 on a broker interface

• A market order for 100 shares is placed, intending to be filled at $183.53

• In the time between placing the order and the order’s execution, the bid/ask spread lifts to $183.54/183.57

• This incurs a $0.04 per share or $4 total negative slippage for the 100-share trade

For a more tangible and crypto-centric example of slippage, see section 2.1 of our article on DEX aggregators.

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SOR primarily aims to avoid incurring slippage losses by identifying liquidity and volatility factors across a number of sources and may also include differing order types such as limit or market orders, and more complex timed strategies such as trailing stop orders. In doing so, a SOR algorithm carves out a ‘route’ between buyer and seller, a route that may span multiple venues in order to take advantage of those venues’ liquidity depth and volatility metrics.

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Scarce Liquidity

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Cryptocurrency market venues often suffer from liquidity scarcity, rendering large transactions either impossible or infeasible due to the various losses incurred. This is largely due to the issue explored in the next section but is also a symptom of the relatively small market cap of cryptocurrencies relative to traditional markets.

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Fragmented Liquidity

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As previously mentioned, SORs both take advantage of and aim to mend the problem of fragmented liquidity, an issue facing the various cryptocurrency spaces as the number of trading venues continues to increase. Liquidity within venues is generally somewhat endemic to that ecosystem, meaning that the liquidity of one asset in that venue is separated from the liquidity of that asset in other venues. This can dramatically reduce the readily available liquidity of any given asset, unless tools are used to bridge these venues in an intelligent manner, such as via the implementation of SOR.

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Market Volatility and Price Inconsistencies

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Cryptocurrencies are not generally known for their stability (barring non-algorithmic stablecoin examples). Volatile assets are extremely prone to slippage, combined with inconsistent asset pricing across venues, SOR offers a lot of value in the way of both loss mitigation and in the potential for arbitrage opportunities.

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Nuances of Smart Order Routing

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SORs come in many forms specialized to their own ecosystems and objectives. Generally speaking, the goal is to find the best route for an order; but this can mean different things in different use cases. SORs may break orders up, spreading orders across venues, they may favor fill speed rate over fill price or vice versa. Additionally, more advanced techniques such as dark pools (private exchanges on which transactions are not made public, useful in the aforementioned cases in which your own large order would noticeably affect the market) may be leveraged in cases where extremely large orders are being placed, with the intent of minimizing immediate market reaction; or they can be configured to optimize dynamic ratio indicators such as volume-weighted average price (VWAP) or time-weighted average price (TWAP).

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What is a Route in the Stock Market?

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In traditional markets, an order placed through a broker will be passed on to market makers or order flow agreement partners for execution. The broker is the middleman in this arrangement and facilitates the routes through which your order can be connected with its counterpart offer/s, often through order flow arrangements with third-party liquidity providers known as market makers.

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Orders placed through electronic communication networks (ECN) are able to cut out the middleman broker, routing trades directly. ECNs use order books that automatically match buyers to sellers via algorithms that are generally centered on best price execution.

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What is a Route in Crypto?

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In crypto, a SOR generally searches for the most efficient route to swap tokens for another token type. SOR for centralized exchanges (CEX) is comparable to the SOR techniques used in ECN, as CEXs similarly use order books, and routing consists of matching buyers to sellers across the order book in accordance with some algorithmically optimized goal.

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However, in the case of decentralized exchanges (DEX), SOR deviates somewhat in functionality from its traditional market counterpart. A more comprehensive definition of DEXs can be found in this article, but they are essentially exchanges that do away with third-party authority, enabling users to exchange assets with one another without the need to forfeit custody of their assets. This is made possible by liquidity pool smart contracts, pools of (generally two types of) tokens that users can swap for one another which use conservation functions such as the XYK model in order to algorithmically price assets — these functions are often referred to as automated market makers (AMM), as they effectively fill the same role as traditional market makers by buying and selling assets to facilitate market swaps, only they do so algorithmically. In its simplest form, a user wishing to swap token A for token B will deposit token A into an A:B liquidity pool, receiving the appropriate amount of token B in return.

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DEXs are however by no means impervious to the aforementioned issues of liquidity scarcity, liquidity fragmentation, and market volatility, and as such, are susceptible to slippage all the same. In fact, in many cases these factors are amplified within the current state of DEXs, as liquidity pools are many in number, fragmenting liquidity and leaving only small amounts for each pool; furthermore, liquidity issues tend to cause price fluctuations on account of the pool’s conservation functions acting to quickly rebalance the pool’s asset ratio.

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Due to the nature of AMMs in DEXs, slippage due to placing large orders is often even more of an issue than in other types of venues, as the liquidity concentration of DEX liquidity pools is generally on the smaller side relative to other sources of liquidity. When swapping tokens in a DEX, you are essentially adding one asset to the liquidity pool while simultaneously removing another, the AMM conservation function then automatically rebalances the ratio of these two tokens. This means that trades in pools with small liquidity, conservation functions can overcompensate and cause dramatic price swings as the proportional ratio will be more affected — conducive to major slippage. This can be thought of analogously as the fluid displacement of a boat (an order) in a body of water (a liquidity pool); a dingy in a river will virtually have no impact on the water level, but a yacht in a pool certainly will.

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With all of these factors in play, it is generally infeasible to execute large trades in DEXs without incurring large losses to slippage. To combat these effects, DEX SORs can scour across various liquidity pools in order to find swaps with the most optimal price for the user. Additionally, they may also take advantage of the liquidity depth of several pools rather than simply relying on the available liquidity of a single pool, potentially even making use of any profitable arbitrage opportunities along the way.