Trading System And Algorithmic Trading Strategies

Sarbani Maiti September 13 2017

What is a Trading System?

A “trading system” creates a set of trading strategies which are applied to the given input data to generate entry and exit signals (buy/sell) in a trading platform.The traders/professionals who create the trading strategies to maximize the profit are called “Quants”. They use exhaustive quantitative research & analysis to build such efficient strategies by applying advanced statistical and mathematical models.

Algorithmic trading – Algorithmic trading uses various algorithms to create a trading strategy from trading ideas. The algorithms are backtested with historical data and then used with real market data to give the best return. The execution can be done manually or automated.

Quantitative trading – Advanced mathematical and statistical models are used in Quantitative trading creation and execution of trading strategies.

Automated trading – Automated trading involves automated order generation, submission, and the order execution process. However, they are not fully automated. Manual interventions are also required

HFT (high-frequency) trading – Trading strategies can be classified into low-frequency, medium-frequency and high-frequency strategies as per the holding time of the trades. High-Frequency Trading strategy holds the trading position for a fraction of a second time and executes the trading strategy automatically. Millions of trades are an executed per day in this model.

The most of the algo-trading is high-frequency trading (HFT), which attempts to capitalize on placing a large number of orders at very fast speeds across multiple markets and multiple decision parameters, based on pre-programmed instructions.

The other name of Algo Trading is black box trading.

The profit opportunities are higher in algo trading and it makes markets more liquid and makes trading more systematic by ruling out emotional human impacts on trading activities via sentiment analysis.

Algorithmic Trading Strategies

  • Momentum/Trend Following:
    Calculate 50 days SMA (Simple Moving Average)
    Calculate 200 days SMA
    Take a long position when the 50 days SMA is larger than or equal to 200 days SMA
    Take a short position when the 50 days SMA is smaller than 200 day SMA. This is one of the most common algorithmic trading strategies. This follows trends in moving averages, channel breakouts, price level movements and related technical indicators. Algo Trader assumes there is a trend in the market and use the statistics to determine if the trend will continue. It does not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends. The above-mentioned example of 50 and a 200-day moving average is a popular trend following strategy.
  • Arbitrage Opportunities:
    Buying a dual listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks versus futures instruments, as price differentials do exists from time to time. Implementing an algorithm to identify such price differentials and placing the orders allows profitable opportunities in an efficient manner. Also, trading can be triggered by the acquisition of the issuer company. This is called corporate event. Such event driven strategy is applied when the trader is planning to invest based on the pricing inefficiencies that may happen during a corporate event (before or after). Bankruptcy, acquisition, merger, spin-offs etc could be the event that drives such kind of an investment strategy. These strategies can be market neutral and used by hedge fund and proprietary traders widely. Index Fund Rebalancing: Index fund has defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders, who capitalize on expected trades that offer 20-80 basis points profits depending upon the number of stocks in the index fund, just prior to index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and best prices.
  • Machine Learning based
    The major aspect of ML is learning from past data and predict the outcome of an unseen or new situation. The human learns in the same fashion however machine can process a huge volume of data much faster than human and predict the outcome. This is the way trading system works. Traders handle a large volume of historical data, analyze them and predict the stock price to establish a various trading strategy. Hence machine learning has become one of the key elements in Algo Trading system. There are many types of ML techniques depending on the nature of target prediction: Regression, Classification, Clustering, Association. The other set of categorization is Supervised (Target prediction is known to the model) vs Un-Supervised (Target prediction is unknown to the model) techniques. Python is a powerful language which supports statistical computations and can work with ML algorithms easily. R is another powerful language for statistical analysis.
  • Mathematical Model Based Strategies:( source Investopedia)
    A lot of proven mathematical models, like the delta-neutral trading strategy, which allows trading on a combination of options and its underlying security, where trades are placed to offset positive and negative deltas so that the portfolio delta is maintained at zero.
    • Trading Range (Mean Reversion):
      Mean reversion strategy is based on the idea that the high and low prices of an asset are a temporary phenomenon that reverts to their mean value periodically. Identifying and defining a price range and implementing an algorithm based on that allows trades to be placed automatically when the price of asset breaks in and out of its defined range.
    • Volume-Weighted Average Price (VWAP):
      The volume weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock specific historical volume profiles. The aim is to execute the order close to the Volume Weighted Average Price (VWAP), thereby benefiting on average price.
    • Time Weighted Average Price (TWAP):
      Time-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times, thereby minimizing market impact.
    • Percentage of Volume (POV):
      Until the trade order is fully filled, this algorithm continues sending partial orders, according to the defined participation ratio and according to the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-defined levels.
    • Implementation Shortfall:
      The implementation shortfall strategy aims at minimizing the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when the stock price moves adversely.

Benefits of Algorithmic Trading

  • Trades are executed timely and instantly to get benefit from best possible price change
  • Reduced risk of manual errors in placing the trades order and achieved higher performance
  • Reduced transaction costs
  • Take the benefit of multiple market conditions
  • Backtest the algorithm, based on available historical and real-time data
  • Reduced possibility of human error based on emotional and psychological factors of traders

Algo-trading is used in many forms of trading and investment activities, including:

  • Mid to long term investors or buy side firms (pension funds, mutual funds, insurance companies) who purchase in stocks in large quantities but do not want to influence stocks prices with discrete, large-volume investments.
  • Short term traders and sell side participants (market makers, speculators, and arbitrageurs) benefit from automated trade execution; in addition, algo-trading aids in creating sufficient liquidity for sellers in the market.
  • Systematic traders (trend followers, pairs traders, hedge funds, etc.) find it much more efficient to program their trading rules and let the program trade automatically.
  • Algorithmic trading provides a more systematic approach to active trading than methods based on a human trader’s intuition or instinct.

Sarbani Maiti

Head of Production Support


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