What is low latency algorithmic trading?
Low latency algorithmic trading is a process for carrying out orders using automated and pre-programmed trading directions to account for different prices, timing, and volume. Faster execution is achieved through low latency, which delivers data in under a millisecond to make faster decisions.
Institutional investors and big brokerage firms mostly use algorithmic trading to reduce the expenses associated with trading.
What are the benefits of low latency algorithmic trading?
Markets change and generate new information far faster and at higher volumes than any trader, or even an entire trading floor, can process or react to. An algorithm can get this information and make trading decisions much faster. This has a lot of advantages:
Better bidding parameters: Having more information and acting on a holistic view of the market allows for more optimized bidding, which can keep costs low (buy-side platforms) or prices high (sell-side platforms).
More market opportunities: Reacting to changing conditions faster than other market participants allows algorithmic traders to take advantage of short-lived opportunities. This is especially useful for arbitrage/high frequency trading (HFT) strategies that seek to capture inefficiencies at little or no risk. For example, being able to capture the spread between bid and ask prices when one appears.
More agile: A properly developed low latency trading algorithm can spot impending market risk and adjust to a more conservative strategy. Or, it can identify a trend as it starts and shift to more aggressive decisions — all before human traders are even aware that a change has occurred.
Is low latency trading the same thing as high frequency trading?
While the two concepts are closely related, they aren't the same. High frequency traders use low latency algorithmic trading to capture tiny differences between bid and ask prices, called the spread. This spread is typically small, sometimes fractions of pennies, but can be profitable at high enough volumes.
Example: Trader A is trying to sell a share of $STOCK at $1.00 (the ask price) while Trader B wants to buy a share at $1.01 (the bid price). A high frequency trade would submit a bid to buy the share at $1.005 from Trader A, getting their bid in before the higher bid cleared, and then immediately sell it to Trader B for $1.01, pocketing the $0.005 difference.
What impacts low latencies?
Many factors can impact the low latency of algorithmic trading, such as the distance between the exchange and the trading system or the efficiency of the trading system architecture. This architecture might include network adaptors, the operating system choice, code efficiency, and programming language.
Signal speed
Digital signals can travel fast — up to 80% to 90% of the speed of light — but not instantaneously. Latency increases with distance, is further increased by networking hardware between the origin and destination, and can be further affected by the quality of the cables. Wireless networks tend to have more latency than wired ones. Placing the signal source as close to the exchange as possible reduces latency, making co-location a popular option.
Any processing of the signal adds latency. This latency starts at the host machine where the signal is generated. It's added to by any networking equipment along the way, like routers and switches. Finally, it's added again at the exchange server where the signal is read and acted on. Faster hardware or less hardware between source and destination can decrease latency.
Decision delays
Low latency algorithms need a lot of data to work effectively, and moving and loading this data can introduce delays. Accessing the data necessary for good decision-making adds a second layer of network, hardware, and software latencies on top of signal latency. Updating the data from the market does the same. Faster databases with faster transactions on servers closer to the trading hardware can reduce data access delays.
And not every algorithm is created equal: choices about specific functions, logic, APIs, programming language(s) used, and runtime environment can all change the speed at which data is turned into action. This is also true on the market side, as inefficient exchanges can introduce processing delays while the order is read and executed.
How can traders reduce latencies?
Reducing latencies in low latency algorithmic trading requires an end-to-end approach that optimizes every step in the process for speed. This includes:
Place servers as physically close together as possible. Co-locating the trading server with the exchange server is ideal.
Upgrade networking equipment and remove any unnecessary middleman hardware. What's best is a direct fiber connection between trader and exchange.
Upgrade processing hardware. Faster CPUs are good; FGPAs dedicated to specific tasks are even better.
Use a faster database deployed closer to the processing server. Low latency and high-throughput databases are essential for getting market data into trading algorithms quickly. In-memory data storage and caching can increase speeds, though at a higher cost than traditional approaches.
Tune and optimize the trading algorithm by moving it as close to bare metal as possible. A fully hardware or assembly solution will run faster than one that requires several layers of abstraction.
Low latency databases support low latency algorithmic trading
Low latency trading algorithms were a market-shaking change when they first hit the trading scene. Today, they're an established part of the financial ecosystem, and the principles developed for trading stocks are being applied to other auction-style markets like programmatic advertising exchanges and cryptocurrency.
As the principles continue to be refined, users will need to start looking at ultra-low latency algorithms to find and keep their edge, and one of the keys to speedy trading decision-making will be the ability to pull in data at hyper speed and hyper scale. Aerospike's Database 8 can help ultra-low latency traders ingest, clean, and process the firehose of raw data to enable faster decisions and better trades, making it a vital part of the low latency algorithmic trading stack today, and especially tomorrow.
What is low latency algorithmic trading?
Low latency algorithmic trading is a process for carrying out orders using automated and pre-programmed trading directions to account for different prices, timing, and volume. Faster execution is achieved through low latency, which delivers data in under a millisecond to make faster decisions.
Institutional investors and big brokerage firms mostly use algorithmic trading to reduce the expenses associated with trading.
What are the benefits of low latency algorithmic trading?
Markets change and generate new information far faster and at higher volumes than any trader, or even an entire trading floor, can process or react to. An algorithm can get this information and make trading decisions much faster. This has a lot of advantages:
Better bidding parameters: Having more information and acting on a holistic view of the market allows for more optimized bidding, which can keep costs low (buy-side platforms) or prices high (sell-side platforms).
More market opportunities: Reacting to changing conditions faster than other market participants allows algorithmic traders to take advantage of short-lived opportunities. This is especially useful for arbitrage/high frequency trading (HFT) strategies that seek to capture inefficiencies at little or no risk. For example, being able to capture the spread between bid and ask prices when one appears.
More agile: A properly developed low latency trading algorithm can spot impending market risk and adjust to a more conservative strategy. Or, it can identify a trend as it starts and shift to more aggressive decisions — all before human traders are even aware that a change has occurred.
Is low latency trading the same thing as high frequency trading?
While the two concepts are closely related, they aren't the same. High frequency traders use low latency algorithmic trading to capture tiny differences between bid and ask prices, called the spread. This spread is typically small, sometimes fractions of pennies, but can be profitable at high enough volumes.
Example: Trader A is trying to sell a share of $STOCK at $1.00 (the ask price) while Trader B wants to buy a share at $1.01 (the bid price). A high frequency trade would submit a bid to buy the share at $1.005 from Trader A, getting their bid in before the higher bid cleared, and then immediately sell it to Trader B for $1.01, pocketing the $0.005 difference.
What impacts low latencies?
Many factors can impact the low latency of algorithmic trading, such as the distance between the exchange and the trading system or the efficiency of the trading system architecture. This architecture might include network adaptors, the operating system choice, code efficiency, and programming language.
Signal speed
Digital signals can travel fast — up to 80% to 90% of the speed of light — but not instantaneously. Latency increases with distance, is further increased by networking hardware between the origin and destination, and can be further affected by the quality of the cables. Wireless networks tend to have more latency than wired ones. Placing the signal source as close to the exchange as possible reduces latency, making co-location a popular option.
Any processing of the signal adds latency. This latency starts at the host machine where the signal is generated. It's added to by any networking equipment along the way, like routers and switches. Finally, it's added again at the exchange server where the signal is read and acted on. Faster hardware or less hardware between source and destination can decrease latency.
Decision delays
Low latency algorithms need a lot of data to work effectively, and moving and loading this data can introduce delays. Accessing the data necessary for good decision-making adds a second layer of network, hardware, and software latencies on top of signal latency. Updating the data from the market does the same. Faster databases with faster transactions on servers closer to the trading hardware can reduce data access delays.
And not every algorithm is created equal: choices about specific functions, logic, APIs, programming language(s) used, and runtime environment can all change the speed at which data is turned into action. This is also true on the market side, as inefficient exchanges can introduce processing delays while the order is read and executed.
How can traders reduce latencies?
Reducing latencies in low latency algorithmic trading requires an end-to-end approach that optimizes every step in the process for speed. This includes:
Place servers as physically close together as possible. Co-locating the trading server with the exchange server is ideal.
Upgrade networking equipment and remove any unnecessary middleman hardware. What's best is a direct fiber connection between trader and exchange.
Upgrade processing hardware. Faster CPUs are good; FGPAs dedicated to specific tasks are even better.
Use a faster database deployed closer to the processing server. Low latency and high-throughput databases are essential for getting market data into trading algorithms quickly. In-memory data storage and caching can increase speeds, though at a higher cost than traditional approaches.
Tune and optimize the trading algorithm by moving it as close to bare metal as possible. A fully hardware or assembly solution will run faster than one that requires several layers of abstraction.
Low latency databases support low latency algorithmic trading
Low latency trading algorithms were a market-shaking change when they first hit the trading scene. Today, they're an established part of the financial ecosystem, and the principles developed for trading stocks are being applied to other auction-style markets like programmatic advertising exchanges and cryptocurrency.
As the principles continue to be refined, users will need to start looking at ultra-low latency algorithms to find and keep their edge, and one of the keys to speedy trading decision-making will be the ability to pull in data at hyper speed and hyper scale. Aerospike's Database 8 can help ultra-low latency traders ingest, clean, and process the firehose of raw data to enable faster decisions and better trades, making it a vital part of the low latency algorithmic trading stack today, and especially tomorrow.