UNDERSTANDING ALGORITHMIC TRADING
Algorithmic Trading often refers to sell-side and buy-side aspects of trading. Buy-side trading algorithms are "strategic" such as deciding what to buy/sell and when, while sell-side trading algorithms are "tactical" by trying to get the best price for a buy/sell order. The main functions of sell-side trading algorithms are pre- trade and post-trade analytics in predicting and measuring the market impact of a large trade, which is supported by statistical analysis of tick data. The objective is to minimize permanent and temporary market impact on liquidity over time. There are two main approaches in which algorithms are used to break down a large order and slice them into the market over a period of time. The goal is to minimize the impact a large order has in the market and to achieve a benchmarked price which is the VWAP (Volume Weighted Average Price) and Market Participation Algorithms. These algorithms use metrics to determine how to slice a large order; VWAP uses the historic volume distribution for a particular symbol over the course of a day and divides the order into slices, proportioned to this distribution. The typical use of an algorithm is the buy-side sending an order to be executed algorithmically into a broker. This can be done either by phone or in an automated way from a buy-side Execution Management System as a FIX order. The buy-side provides all the information, such as instrument, side, quantity, and the algorithm to use. An instance of the execution algorithms is then instantiated within the broker environment to trade the order. It is also possible to run these algorithms within the buy-side and send only the child orders straight to the market through (DMA) Direct Market Access). To achieve this, an (EMS) Execution Management System would have built their own algorithms using technologies such as (CEP) Complex Event Processing.
While “execution algorithms” are about automating “how to trade” and “how to place orders in the market”, high Frequency trading algorithms addresses the “when to trade” and “what to trade” aspects of electronic trading. Execution algorithms are about minimizing market impact and trying to ensure a fair price, whereas HFT algorithms are about profit making incentives. The “high frequency” refers to being able to keep up with the high frequency streams of data, make decisions based on patterns in the data indicating possible trading opportunities, and automatically place and manage orders in the market to capitalize.
Liquidity Aggregation and Smart Order Routing
As market fragmentation continues, algorithmic techniques have been employed to aggregate liquidity and use smart order routing to send orders to the venues with the best price and liquidity. These techniques can be used to by high frequency algorithms to operate more effectively in a fragmented market on the sell side. In a fragmented market, there are at least 100 trading venues meaning, if you want to sell IBM stock, you can go to 100 different electronic trading networks which are collectively known as Alternative Trading Systems. Among the Alternative Trading Systems is the so-called dark liquidity pool (also known as a crossing network). It is called a dark pool because the order book is hidden from the participants (bid/ask in the pool is unknown); this allows traders to park huge blocks of shares in the order book without having to worry about sending out a signal to market participants. The dark pool matches limit orders and executes them at the mid- point of the bid/ask price quoted in the normal exchange. There are many different dark liquidity pools in the US, and to "source liquidity" (get your order filled) you have to go around "pinging" these dark pools, until you hit a match. There are algorithms which perform this sweeping in an intelligent manner by estimating the probability of hitting a match in a given dark pool. This dark pool sweeping algorithms is integrated into smart order routing systems.The other function of smart order routing is to get the best price in the least amount of time while incurring the least amount of exchange-related costs. Regulation NMS (SEC regulation concerning the National Market System) means brokers are legally required to execute client orders at the best price (a practice known as best execution), which has caused a big push towards smart routing technologies.
Real-Time Pricing of Instruments
Algorithmic techniques have also been used in real time pricing of instruments, such as bonds, options, and foreign exchange. Traditional pricing techniques use slower-moving pricing analytics and fundamentals to price instruments. However, now higher frequency algorithmic techniques can enhance these pricing algorithms based on what is happening in the aggregated market (how can we make money by increasing the spread on liquidity available) and the type of customer the price is being published for. Accordingly, any spread adjustment is based on historical trading that this customer has conducted in the past. High frequency pricing can therefore skew prices and spreads based on the up-to-millisecond view from the market and the tier of the customer.
This involves thousands of permutations & computations of algorithms which run in parallel and fed with real market data for trading in the market. The algorithms to have the most profitable theoretical P&L takes precedence over other algorithms to trade live in the market. Over time, algorithms may become less profitable and can be deactivated. This model of Darwinian trading allows self-evolving systems to discover profitable opportunities through evolutionary processes, with some guidance by trader experts. This technique is still in exploratory mode.
C67 refers to 67 iterations of a million+ lines of code, software program developed by Daniel Sznicer over 6 years to accurately represent trading market sentiment. Utilizing real-time data, C67 software will issue a variety of signals and display those signals in a topographical format for ease of analysis by humans as well as computerized direct trade signals that will direct automated algorithmic execution systems. The C67 conveys market sentiment with 100% accuracy, including no-trade signals where none exists. This allows trading systems to focus on market opportunities, trading intervals, methodologies and market metrics conducive to predictable alpha generating conditions.
Mean Reversing and Volatility Adjusted Order Entry. Conventional trading wisdom says "don't fight the market" yet math suggests that all patterned data regresses back to a mean distribution. Somewhere between these two truths describes market trading behavior ever switching back and forth between extreme trending behavior and consolidating market equilibrium pricing. Long-term price movements reflect the underlying economic metrics of markets, companies, commodities, and indices. Intra-day price movements derive more from short-term exogenous events and the sum total of market supply-demand pressures, order aggregation and computer algorithms. The best prices always reflect those being purchased in the direction of the long-term trend but against the short-term volatility. The danger of accumulating a position in this manner is that trends change, and investors fail to recognize the change in trend before they go bankrupt, e.g., a black swan event.
C67 governs our trading black boxes, which are constantly fed market volatility data to adapt, position sizes and money management to match market trading conditions. Positions are scaled in and out to optimize pricing and keep positions near market prices. Price executions are dictated by our own custom deviations from the mean, and exits are scaled through automated trailing stops moved so quickly that they would be incomparable to manual execution. And most importantly, C67 ensures our positions are always in line with real-time market sentiment.
These types of algorithms have become less reliable in fragmented, fluctuating markets, which currently favor more dynamic strategies that adapt to current market conditions. In response to this natural evolution, brokers are designing less historically dependent algorithms and focusing on strategies to incorporate real-time variables and adjust trading strategies dynamically. However, there are a multitude of algorithms available to buy-side traders in today’s market, often with creative names that don’t make it easy to establish what exactly these algorithms do.
In recent years, customization has become very popular with clients, making it necessary for sell-side firms to offer this service. Client traders adapt sell-side algorithms to mimic their own trading behavior and style and may also use them to evaluate sell-side offerings. By providing each of their brokers with a list of functionality and parameters for customized algorithms and then comparing how each customized broker algorithm performs, clients can compare apples with apples.
Real-time Pre-Trade Risk Firewall
It is possible to continuously re-calculate risk exposures on positions while monitoring trades as they go to market and determine what impact they would have on pre-defined risk limits. In the event of a threshold breach, trades can be blocked from being processed. Another risk is to monitor for erroneous trades, such as “fat finger trades” and block them. This facility is not only useful for the traders, but also for brokers offering sponsored access. This is done to monitor on a client basis using the latest technology platforms, such as CEP (Complex Event Processing). Ultimately, this enables pre-trade verifications to be performed with minimal latency before a trade is executed.
Back Testing & Market Simulation
As introduced above, before executing algorithms on the market, it is highly beneficial to test them with a variety of real historical and pre-determined scenarios to observe how they would perform in real time. This can be accomplished in conjunction with realistic market simulators. .