Basics of Algorithmic Trading: Concepts and Examples

Algorithmic trading (likewise called computerized trading, black-box trading, or algo-trading) uses a computer program that follows a defined set of directions (an algorithm) to place a profession. The trade, in theory, can create revenues at a rate and also regularity that is impossible for a human investor.

The specified sets of guidelines are based upon timing, cost, amount, or any type of mathematical model. Aside from earnings chances for the trader, algo-trading makes markets a lot more liquid as well as trading more organized by dismissing the impact of human feelings on trading tasks.

Algorithmic Trading In Practice

Mean an investor follows these easy profession criteria:

-Purchase 50 shares of a stock when its 50-day relocating average goes above the 200-day moving average. (A relocating average is an average of previous information points that ravels daily rate changes as well as consequently determines patterns.).
-Offer shares of the stock when its 50-day moving ordinary goes listed below the 200-day moving standard.
Using these 2 simple directions, a computer program will instantly keep an eye on the supply rate (and also the moving average signs) and position the deal orders when the specified conditions are fulfilled. The trader no more requires to check online prices and charts or placed in the orders by hand. The mathematical trading system does this instantly by correctly identifying the trading opportunity.

Basics Of Algorithmic Trading

Benefits of Algorithmic Trading

Algo-trading provides the following benefits:

  • Trades are executed at the best possible prices.
  • Trade order placement is instant and accurate (there is a high chance of execution at the desired levels).
  • Trades are timed correctly and instantly to avoid significant price changes.
  • Reduced transaction costs.
  • Simultaneous automated checks on multiple market conditions.
  • Reduced risk of manual errors when placing trades.
  • Algo-trading can be backtested using available historical and real-time data to see if it is a viable trading strategy.
  • Reduced possibility of mistakes by human traders based on emotional and psychological factors.

Most algo-trading today is high-frequency trading (HFT), which attempts to capitalize on placing a large number of orders at rapid speeds across multiple markets and multiple decision parameters based on preprogrammed instructions.

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—use algo-trading to purchase stocks in large quantities when they do not want to influence stock prices with discrete, large-volume investments.
  • Short-term traders and sell-side participants—market makers (such as brokerage houses), 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, hedge funds, or pairs traders (a market-neutral trading strategy that matches a long position with a short position in a pair of highly correlated instruments such as two stocks, exchange-traded funds (ETFs) or currencies)—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 trader intuition or instinct.

Algorithmic Trading Strategies

Any strategy for algorithmic trading requires an identified opportunity that is profitable in terms of improved earnings or cost reduction. The following are common trading strategies used in algo-trading:

Trend-following Strategies

The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements, and related technical indicators. These are the easiest and simplest strategies to implement through algorithmic trading because these strategies do not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends, which are easy and straightforward to implement through algorithms without getting into the complexity of predictive analysis. Using 50- and 200-day moving averages 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 vs. futures instruments as price differentials do exist from time to time. Implementing an algorithm to identify such price differentials and placing the orders efficiently allows profitable opportunities.

Index Fund Rebalancing

Index funds have 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 to 80 basis points profits depending on the number of stocks in the index fund just before index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and best prices.

Mathematical Model-based Strategies

Proven mathematical models, like the delta-neutral trading strategy, allow trading on a combination of options and the underlying security. (Delta neutral is a portfolio strategy consisting of multiple positions with offsetting positive and negative deltas—a ratio comparing the change in the price of an asset, usually a marketable security, to the corresponding change in the price of its derivative—so that the overall delta of the assets in question totals zero.)

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