What is Algo Trading?
Algo trading is a type of trading with automatic trade execution using predefined set of rules as a trading strategy. Trading strategies can be defined by specifying certain rules that involves variables such as price, volume, indicators and price levels. Strategies can be coded using programming language such as C++, Python, Java or Excel formulas or using Charting Softwares such as Amibroker.
Types of Algo Trading
Algo trading can be categorized as High frequency trading (HFT), Low frequency trading (LFT), trend following, Mean reversion, Scalping, Arbitrage and market making. Big institutions use Algo trading to cut the large order sizes into set of small orders to reduce price variations.
High frequency trading mainly used by institutions with execution of tens to hundreds of trades per second. As High frequency trading requires high speed computers and faster access to price changes, it requires high capital to set up. Retail traders uses low frequency trading with few trades per day that include mainly trend following or mean reversion strategies.
Advantages of Algo trading
Main advantage of Algo trading is the speed. Computer program can analyze the price patterns, take buy, sell decisions and executes the orders within a fraction of second that is not possible by humans. Algo trading reduce the slippages by executing orders with specified price. For retail traders, Algo trading with back tested strategies is beneficial as it removes the emotion involved in human decision making and improves profitability by proper execution of trades.
Steps to setup Algo trading
1. Setup sample data set: This is one important step as the strategy creation depends on type of data available. For example if trader wants to create an intraday strategy to trade set of stocks, intraday data has to be available for those stocks with reasonable time period say 3 to 5 years of historical data.
2. Create a trading strategy: Trading strategy can be created by specifying Buy, Sell, Stop loss and Profit conditions. Each conditions can be defined with one or more rules based on technical indicators, price levels, candlesticks patterns and volume. Strategy has to be set with intraday time frame such as 5, 10, 15 minutes. Start and end time for trade execution, position size and brokerage charges to be defined.
3. Back test the strategy: Strategy has to be tested for profitability with above said sample data set. While doing back testing, strategy parameter values can be changed manually or by running Optimization process to get the expected profit with limited Draw-down.
4. Validate the strategy: Once the strategy produced good results with sample data, it has to be validated with current data or Out sample data. This step is important because the strategy with optimization may be curve fitted to sample data and produce different result with current data. This has to be corrected by going back to step 3 process with limiting the optimization process to get similar results for both data sets.
5. Live execution: This is the step to make the whole process automated with live data, signal generation and API integration to execute the orders. Strategy has to run continuously by taking live data, generate signals based on conditions and send order details to broker using API.
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