Definition Of Algo-Trading (Alg0rithmic Trading)

On this webpage, we will be discussing the Definition Of Algo-Trading (Algorithmic Trading). The algorithmic trading form of trading also known as an automated trading system refers to an automated trading system in which buying and selling orders are placed. The Basic Concepts Of Algorithmic Trading And Examples, Example of Algorithmic Trading, The main algorithmic trading strategies

Algo-trading, also known as algorithmic trading, is an automated trading system according to the rules of a computer program or algorithm. The algorithm may be configured to consider price, but may also look at other factors like timing and volume. As soon as the condition of the market fulfills the criteria of the algorithm, accordingly, the algo-trading software will place a buy or sell order. A few examples could be:

  • Buying 10 BTC when the ten-day moving average exceeds the 30-day moving average
  • Selling 10 BTC when the ten-day moving average falls below the 30-day moving average.

Definition Of Algo-Trading (Algorithmic Trading)

In reality, algo-trading involves many more complex rules and conditions in building a formula for profitable trading. There are many reasons for traders using algo-trading for instance it offers the opportunity for faster and more frequent trading across an entire portfolio that couldn’t be possible using manual orders because orders are instant, algo-trading also secures the best prices and reduces the risk of slippage. Algorithmic trading reduces the risk of mistakes or emotional reactions to the market conditions by taking the human element out of the equation. 
Algo trading on a macro level, creates more liquid markets all thanks to a higher-order frequency, making the markets more predictable because algorithms are programmed to respond to emerging conditions. 

Definition Of Algo-Trading (Algorithmic Trading)

Although the use of algo-trading across many markets offers even more benefits in the 24/7 cryptocurrency markets, where traders risk missing opportunities or facing loss risks while asleep. Therefore, Algo trading can even be used by those who prefer manual trading as a failsafe when they’re away from their screens. Algo-trading is suitable for a wide range of trading strategies. An arbitrageur who depends on incremental price differences could use an algorithm to ensure order efficiency. 
Traders that are short-term aiming to capture profits from smaller market movements use algo-trading to ensure execution at a high enough frequency to be very profitable, and eliminate the risk of chasing losses. Likewise, market makers also use algo-trading to ensure that there’s sufficient depth of the market liquidity.

Definition Of Algo-Trading (Algorithmic Trading)


For backtesting a particular strategy, traders also use algo-trading in order to check if it can be able to return a consistent profit. Algo trading is incorporated with some risks, particularly around issues such as system downtime or network outages. Their algorithm is also programmed by humans, which makes them subjected to human errors showing that backtesting is critical in ensuring the algorithm behaves as expected. Finally, an algorithm will always be expected to do exactly what it’s programmed for and cannot account for unanticipated “black swan” events that may call for more human intervention and mitigating actions.

The Basic Concepts Of Algorithmic Trading And Examples 

Algorithmic trading is also known as automated trading, black-box trading, or algotrading makes use of a computer program that follows a defined set of instructions to place a trade that can generate profits at a speed and frequency that is impossible for a human trader. These defined sets of instructions are based on quantity, timing, price, or any mathematical model. Apart from profit opportunities for the trader, algo-trading also renders the markets more liquid and trading systematically by ruling out the impact of human emotions on trading activities. 

Therefore Algorithmic Trading in Practice  will Suppose a trader that follows these below simple trade criteria:

  • Buying a shares 50 of stock when its 50-day moving average goes above the 200-day moving average. (A moving average is defined as an average of past data points that smooths out day-to-day price fluctuations and thereby identifying the trends.) 
  • Selling shares of the stock when its 50-day moving average goes below the 200-day moving average.

A computer program using these two simple instructions will automatically monitor the stock price (the moving average indicators) placing the buy and sell orders when the defined conditions are met.

Advantages Of  Algotherimic Trading


The following benefits are provided by Algo-trading:

  • Trades are been executed at the best possible prices.
  • Trade ordering placement is instant and accurate
  • Trades are been timed correctly and instantly to avoid significant price changes.
  •  it reduced the cost of transactions.
  •  Automated checks on multiple market conditions are carried out simultaneously
  • Reduces the risk of manual errors when placing trades.
  • It Reduces the possibility of making mistakes by human traders based on emotional and psychological factors.


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


Many forms of trading use Algo-trading for trading and investment activities such as:

  • Mid- to long-term investors or buy-side firms, pension funds, mutual funds, and insurance companies make use of algo-trading when they do not want to influence stock prices with discrete, large-volume investments to purchase stocks in large quantities.
  • Short-term traders and sell-side market participants makers such as brokerage houses, speculators, and arbitrageurs benefit from automated trade execution, also algo-trading helps in the creation of sufficient liquidity for sellers in the market.


Systematic traders trend followers, hedge funds, or pairs traders which is a neutral market trading strategy that matches a long position with a short position into a pair of highly correlated instruments like two stocks, exchange-traded funds (ETFs), or currencies), finding it much more efficient in programming their trading rules and letting the program trade automatically.

Trading Strategies Of Algorithm


An algorithmic trading strategy 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:

Following trend Strategies

This is the most common algorithmic trading strategy which follows trends in moving averages, channel breakouts, price level movements, and related technical indicators. They are the simplest and easiest strategies to implement through algorithmic trading because they do not involve making any predictions or price forecasts. 

Trades that are initiated based on the occurrence of desirable trends are easy and straightforward to implement through algorithms without getting into the complexity of predictive analysis. Using 50- and 200-day moving averages are a popular trend-following strategy.

Opportunities of Arbitrage


This involves Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offering the price differential as a risk-free profit or arbitrage. The same operation can be replicated for stocks or futures instruments as the price differentials do exist from time to time. To identify such price differentials an algorithm is implemented and placing the orders efficiently allows profitable opportunities.

Rebalancing index fund

This has defined periods of rebalancing to bring their holdings to a point with their respective benchmark indices, creating profitable opportunities for algorithmic traders, who capitalize on expected trades which offer 20 to 80 basis points profits depending on the number of stocks in the index fund just before the index fund rebalancing. These types of grades are initiated through algorithmic trading systems for timely execution and the best prices.

Strategies based on a mathematical model


Just like the Delta neutral trading strategy, proven mathematical models, 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 which is a ratio of comparing the change in the price of an asset.

Mean Reversion ( trading range)

This is based on the concept that the high and low prices of an asset are a temporary phenomenon that reverses back periodically to their mean value (average value). Identification of a price range and implementing an algorithm based on it, allows trades to be placed automatically when the price of an asset breaks in and out of its defined range.
Volume-weighted Average Price (VWAP)

This strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market with the use of stock-specific historical volume profiles. Its aim is to execute the order close to the volume-weighted average price (VWAP).

Time Weighted Average Price (TWAP)


This form of strategy breaks up a large order and releases dynamically determined smaller chunks of the order. To the market by the use of evenly distributed time slots. Between a start and end time. The aim is to execute an order that is close to the average price. Between the start and end times thereby minimizing market impact.

Percentage of Volume (POV)

This algorithm continues to send partial orders until the trade order is fully dead.  According to the participation ratio and volume traded in the market, the related “steps strategy”. Sends orders at a user-defined percentage of the market volumes and increases or decreases its participation rate. When the stock price reaches user-defined levels.

Implementation Shortfall

This form of strategy focus on minimizing the execution cost of an order by trading off the real-time market. Thereby saving on the cost of the order and also benefiting from the opportunity cost of delayed execution. When the stock price moves favorably and decreases when the stock price moves adversely. This strategy increases the targeted participation rate. 

Trading Algorithms beyond the usual

To attempt to identify “happenings” on the other side, a few special classes of algorithms are considered. For example, these “sniffing algorithms” are used. In a sell-side market, makers have built-in intelligence. To identify the existence of any algorithms on the buy-side of a large order. Through algorithms, such detection will help the market maker identify large order opportunities. And enable them to benefit by filling the orders at a higher price. 

Algorithmic Trading technical requirement


The final component of algorithmic trading is the implementation of the algorithm using a computer program. Accompanied by backtesting The challenge is to transform the identified strategy into an integrated computerized process. That has access to a trading account for the placement of orders. Below are following are the requirements for algorithmic trading:

  •  Hired programmers or pre-made trading software with Computer-programming knowledge to program the required trading strategy.
  • Network connectivity and access to trading platforms to place orders.
  • The access to market data feeds that will be monitored by the algorithm for opportunities to place orders.
  • The ability and infrastructure to backtest the system once it is built before it goes live on real markets.
  •  Historical data is available for backtesting depending on the complexity of rules that are implemented in the algorithm.

Example of an Algorithmic Trading


On the Amsterdam Stock Exchange (AEX) and London Stock Exchange (LSE), Royal Dutch Shell(RDS) is mentioned. First, we start by building an algorithm to identify arbitrage opportunities. Here are a few observations that are interesting

  •  LSE trades in British pound sterling while AEX trades in euros
  •  AEX opens an hour earlier than LSE due to an hour difference followed by simultaneously trading. Both exchanges for the next few hours and then trading only in LSE during the last hour when closes.
  • exploring the possibility of arbitrage trading on the Royal Dutch Shell stock. Listed on these two markets in two different currencies requirements are:
  • A computer program that can read current market prices.
  • Price feeds from both LSE and AEX.
  • A forex (foreign exchange) rate feed for GBP-EUR.
  • Placing order capability that can route the order to the correct exchange.
  •  Carry out backtesting capability on historical price feeds.


The computer program should be able to perform the following:


Read the incoming price feed of RDS stock from both exchanges. Algorithmic strategies
Using the available foreign exchange rates, convert the price of one currency to the other.

  • The program should place the buy order on the lower-priced exchange and sell the order on the higher-priced exchange
  •  If there is a large enough price discrepancy (discounting the brokerage costs) leading to a profitable opportunity.

The main algorithmic trading strategies


The three main algorithmic strategies are: 

  • A price action strategy
  •  A technical analysis strategy
  • A combination strategy

Price action strategy


This takes a look at the previous open and close or session high and low prices. Thereby triggering a buy or sell order if similar levels are achieved in the future. For example, you could create an algorithm to enter buy or sell orders if the price moves above point X. Or if the price falls below point Y. This is a popular algorithm with scalpers who want to make a series of quick but. Small profits throughout the day on highly volatile markets which is a process known as high-frequency trading (HFT).

Definition Of Algo-Trading (Alogrithmic Trading). The algorithmic trading form of trading also known as an automated trading system refers to an automated trading system in which buying and selling orders are placed. The Basic Concepts Of Algorithmic Trading And Examples, Example of Algorithmic Trading, The main algorithmic trading strategies

Technical analysis strategy

This type of strategy relies on technical indicators such as Bollinger bands, stochastic oscillators, and MACD. The relative strength index, and many more. With this, you can create an algorithm to act on the parameters of these indicators. Such as closing a position when volatility levels spike. In creating a technical analysis strategy, you need to research and be comfortable using 
different technical indicators. You can create algorithms based on Bollinger bands for example to open or close trades during highly volatile times. To open or close depends on One’sattitude to risk. And whether one has a long or short position in a rising or falling market. With the aid of a technical analysis strategy. One is less focused on price and more interested in using indicators. Or a combination of indicators to trigger your buy and sell orders.


Combination strategy


This uses both price action and technical analysis in confirming potential price movements. Algorithms can then enter buy or sell orders based on this information. To create a combination trading strategy One needs to carry out an analysis of historical price action on an underlying market. Having an understanding of different technical indicators and what they tell you about an asset’s previous price movements. In a combination strategy, one needs to establish whether they want to go long or short. And when they want the algorithm to trade during the day. One can configure a combination strategy according to the market. The time frame, the size of the trade, and the different indicators that the algorithm is designed to use.

The Difference Between Automated Trading And Algorithmic Trading

The key between automated trading and algorithmic trading depends on open interpretation. Because the two-term is used by some people interchangeably. Automated trading often refers to the automation of manual trading through stops and limits, which automatically closes out. Your positions when they reach a certain level. Regardless of whether you are at your trading platform or not. On the other hand, Algorithmic trading often refers to the process through. Which a trader will build and refine their own codes and formulas. To scan the markets and enter or exit trades depending on current market conditions.


Conclusion

There are many trading strategies to choose from. Most traders choose a price action strategy or a technical analysis strategy, whereas some combine the two. A price action strategy applies price data from a market previous. Open or close and high or low levels to place trades in the future. When those price points are achieved again. A technical analysis strategy relies on technical indicators to analyze charts and algorithms. Whose reaction depends on what the indicators show, such as high or low volatility. 

Definition Of Algo-Trading (Alogrithmic Trading). The algorithmic trading form of trading also known as an automated trading system refers to an automated trading system in which buying and selling orders are placed. The Basic Concepts Of Algorithmic Trading And Examples, Example of Algorithmic Trading, The main algorithmic trading strategies

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  5. Crypto
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