If you want to develop an algorithmic trading system you must understand the systematic trading technologies as well as assess every simple component of the market such as data, trading tools and infrastructures. Moreover, it is important to understand the way market works if you want to be well positioned to and optimize your trading technologies.
The first step you need to complete for an algorithmic trading development process is to assess the historical data through the use of quantitative analysis because it is important to minimize your time to market new trading models.
Today algorithmic trading is used by funds and any other traders in order to divide large trades into several smaller and to have the opportunity to manage market impact and risk. This process is crucial because algorithms have no value if the strategies do not perform.
Algorithmic trading system development process includes the following:
- understanding strategies of users by closely interacting with them;
- creating an algorithm based on the inputs;
- presenting the results of back tests to the client;
- create analysis based on tick level data.
When the trading algorithm is available for one or two beta clients they begin to use it on small volumes of live trades. Then the client starts to interact with the vendor and to conduct post trade analysis.
The process’s most important strategies are:
- trend following strategies;
- arbitrage opportunities;
- mathematical model based strategies;
- trading range;
- volume weighted average price (VWAP);
- time weighted average price (TWAP);
- percentage of volume;
- Implementation shortfall.
Trend following strategies are the easiest strategies because they don’t contain not any preconditions or price forecasts. Trades are implemented through the incident of desirable trends for which there is no need of any predictive analysis.
Arbitrage opportunities include the operation of buying a double listed stock at a bottommost price in one and concurrently selling it at higher in different market. In order to have these profitable opportunities we must implement an algorithm able to distinguish such price difference and place the orders.
Mathematical model based strategies place trades to offset negative and positive deltas and maintain the portfolio delta at zero.
Trading range strategy implements algorithms based on identified and defined price range which place the trades when price of asset breaks out and in of its outlined range.
Volume weighted average price (VWAP) uses stock inelastic historical volume profiles to break up a giant order and release it to the market.
Time weighted average price (TWAP) uses equally divided time slots between the end and start time to break up a giant order and release dynamically determined smaller part to the market.
The percentage of volume sends partial orders depending on outlined participation ratio and volume traded in the market until the trade order is completely filled.
Implementation shortfall strategy weighs the urgency of executing a trade against the risk of moving the stock.
In the algorithmic trading system development process algorithms can be used in different stages of the trade cycle and can be classified into pre-trade analytics, execution stage and post-trade analytics.
Pre-trade analytics determine where and when to send orders by using current price, volume data and the analysis of historical data. In the execution stage, traders can choose a particular strategy; they can monitor the algorithms in real time and make the necessary changes to the parameters. Post-trade analytics is used for making investment decisions in the future and improves execution quality.