11 basic terms to know about algorithmic trading

Algorithmic trading

11 basic terms to know about algorithmic trading


Algorithmic trading is characterized by ultra-high-velocity trading action, capitalizing on prompt action and market inefficiencies. Algo-trading denotes rules based trading i.e. algorithm-trading or black-box trading denotes a benchmark rules or gatekeeper rules, about what-to-trade and when-to-trade. The algorithm monitors every trade price, on which stock is traded on trading floor in capital markets. An extensive check about liquidity scenarios in markets is implemented to redirect trading decisions on a particular trading price.

Here is list of 11 basic terms you need to know about algorithmic trading:


1). Volatility

Volatility represents how fast and how far, a security moves, in price terms and on real-time trading volume basis, within a designated period of time. Thus liquidity or money flow in capital markets forms the basis of estimation of volatility for a stock or index or assert class. Different algorithms are programmed to check volatility in stock market, during trading hours, to determine volume of large trades in a stock; or larger repetitive series of low profitability trades in a series of securities.


2). Average True Value

ATV is a volume based technical analysis indicator that determines how far a security trades from high, to low over a designated period of time. Average True Value is considered as fair designator of market volatility in a trading session.


3). VAR (Value-At –Risk)

Value At Risk (VAR) represents the total proportion of market value that is at risk or faces risk due to speculation, due to uncertainty in market caused by presence of an external or un-systemic risk or internal risk parameter or systemic risk. Algo trading is able to capitalize on VAR estimation, to capitalize on risk estimation, and bring down value of traded security in the market.


4). High frequency trading (HFT)

HFT, a specialized form of algorithmic trading is characterized by high turnover and high order-to-trade ratios. HFT is implemented by algorithm on meeting an acceptance or decision criteria, where a pre–decided programming price parameter, “price higher than last day closing price”,  “ registering a day high-price” is met by a market security price, during real-time trading hours. A larger repetitive series, of low profitability trades, in a series of securities or select security is followed on meeting an acceptance or decision criteria, in HFT.


5). Pairs trading

Pairs trading is a long-short market neutral strategy enabling traders to profit from differences in relative value of close substitute. The strategy is limited by a decline in volatility, persistent and large divergences, as well as risk, which can result in unprofitability for the stock. The stocks that are not exact substitute cannot be considered for implementing long short in pair-trading, since law of one price can in-fact result divergence or scattering away of stocks prices instead of expected result on convergence of prices.


6). Delta neutral strategies

Delta neutral strategies refer to a portfolio of related financial securities, where portfolio value remains unchanged due to small changes in the value of the underlying security. As such the underlying securities such that positive and negative delta components of options offset, making portfolio’s value being relatively insensitive to changes in the value of the underlying security.


7). Arbitrage

Arbitrage refers practice of registering monetary gains from difference of stock or options or currency or futures prices, between two or more markets.  Arbitrage refers to generating a profitable deal, based on combination of matching deals, capitalizing upon the imbalance in price in different markets. Thus, arbitrage is possibility of a risk-free profit, at zero cost; achieved through null negative cash flow, at any probabilistic or temporal state, and a positive cash flow in at least one state. Different algo trading are programmed to explore arbitrage in asset price based on price difference due to difference in time zone or location of stock exchange. Example of arbitrage is future to future arbitrage (meaning buying futures in one month and selling in another) or between exchanges such as buying in one stock exchange and selling in another stock exchange.


8). Mean Reversion

Mean reversion is a mathematical methodology based upon estimation on stock’s high, low prices and average price. Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques. When the current market price is above the average price, the market price is expected to fall. In other words, deviations from the average price are expected to revert to the average. Thus, the standard deviation, of the most recent prices is often used as a buy or sell indicator. Different algo trading methods use concept of mean reversion and standard deviation, to realize profitable price during trading hours in stock markets.


9). Scalping

Scalping is a liquidity provision by non-traditional market makers, whereby traders attempt to earn (or make) the bid-ask spread. Scalping is profitable till the price moves are less than this spread. This is normally ensured by establishing and liquidating a position usually within minutes or less. This is achieved with use of sophisticated trading systems and technology.  A fair trading is ensured by obligation by exchange rules on registered market makers to stipulate their minimum quote obligations. NASDAQ exchange requires each market maker to post at least one bid and one ask at some price level, to maintain a two sided market for each stock represented.


10). Parameters covered in algorithm trading:

Different algorithms are programmed, to detect profitable scenarios, based upon analysis of

Arrive price – which is defined as midpoint of the bid-offer spread at order-receipt time and denotes the speed of the execution.

Implementation shortfall – which is parametric calculation achieved on the basis of a model, which weighs the urgency of executing a trade, against the risk of moving the stock.

Market-On-Close (MOC) – measures the last price obtained by a trader, at the end of the day, against the last price reported by the exchange.

Time weighted aver­age price (TWAP) – is a weighted average of stock price over a measurable duration of time.

Volume Weighted Average Price (VWAP) – is mathematical value that is calculated by adding the dollars traded, for every transaction in terms of price and multiplying that by shares traded, and then dividing that by the total shares traded for the day.


11). Categorization of Algorithm components based upon trade cycle

Different trading components can be classified as pre-trade analytics, execution stage, and post-trade analytics.

Pre-trade analytics analyze historical data and current price and volume data to reach decision on algorithm trade— whether to implement algorithm trading, where and when to send orders. The trading decisions help optimizing level of aggressiveness – volume and time interval, between trades, implemented using algorithmic trading, for trading various stocks. Algorithms compare the spread between bid and ask prices, in real time market conditions, against the volatility of a given stock, to forecast create a range of potential outcomes.

In the Execution analytics, traders prescreen lists of stocks for algo trading and put value for the start time and the end time, for implementation of algo trading. The use of market and trade filters basket, exchange, market cap, percent of volume, profit and loss per share and sector proving flexibility to the trader, in algo-trading.

Post-trade analytics are used to track commissions and assist in uncovering the costs involved from the time a trade is done, all the way to execution. This strategy is used to improve execution quality and facilitate the making of investment decisions.


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