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Algorithmic trading (also known as automated trading, black-box trading, or Algo-trading) involves placing a deal using a computer programme that follows a set of instructions (an algorithm). In theory, the deal can make profits at a pace and frequency that would be hard for a human trader to achieve. 


Timing, price, quantity, or any mathematical model are used to define the sets of instructions. Apart from providing profit opportunities for traders, Algo-trading makes markets more liquid and trading more methodical by removing the influence of human emotions on trading. 


In-Practice Algorithmic Trading:

Assume a trader follows the following simple trading criteria


When a stock's 50-day moving average crosses above its 200-day moving average, buy 50 shares. (A moving average smooths out day-to-day price changes and so finds trends by taking an average of previous data points.) 
When the stock's 50-day moving average falls below the 200-day moving average, it's time to sell. 
A computer programme will automatically watch the stock price (and the moving average indicators) and make buy and sell orders when the preset conditions are met using these two simple commands. The trader no longer needs to manually enter orders or check live prices and graphs. This is done automatically by the algorithmic trading system accurately detecting the trading opportunity. 

Algorithmic Trading's Advantages 

The following are some of the advantages of algorithmic trading: 

The best potential pricing is used to conduct trades. 
The placement of trade orders is quick and precise (there is a high chance of execution at the desired levels). 
To avoid substantial price swings, traders are timed precisely and promptly. 
Transaction costs are lower. 

Automated checks on multiple market conditions at the same time. 
When placing transactions, there's a lower chance of making a mistake. 
To see if Algo-trading is a feasible trading method, it can be backtested using historical and real-time data. 
Reduced the risk of human traders making mistakes due to emotional and psychological variables. 
Fast-frequency trading (HFT) is the most common type of algo trading today, which tries to profit from making a large number of orders at high speeds across numerous markets and decision parameters using preprogrammed instructions. 


Many types of trading and financial operations use Algo-trading, including: 


When mid-to long-term investors or buy-side firms—pension funds, mutual funds, and insurance companies—do not aim to affect stock prices with discrete, large-volume investments, they use Algo-trading. 
Automated trade execution benefits short-term traders and sell-side participants—market makers (such as brokerage houses), speculators, and arbitrageurs; in addition, algo-trading aids in creating sufficient liquidity for market sellers. 
Trend followers, hedge funds, and 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 that programming their trading rules and letting the programme trade automatically is much more efficient. 
Compared to strategies relying on trader intuition or instinct, algorithmic trading offers a more methodical approach to active trading. 


Trading Strategies Using Algorithms 

Any algorithmic trading strategy necessitates the identification of a favourable opportunity in terms of increased revenues or cost reduction. The following are some of the most common algo-trading strategies: 


Trend-following Techniques 
Moving averages, channel breakouts, price level fluctuations, and other technical indicators are used in the most common algorithmic trading techniques. Because these methods do not require any predictions or price forecasts, they are the easiest and simplest to implement using algorithmic trading. Without entering into the complexities of predictive analysis, trades are made based on the occurrence of favourable patterns, which are simple and basic to apply through algorithms. A popular trend-following method is to use 50- and 200-day moving averages. 


Opportunities for Arbitrage 
Buying a dual-listed stock at a lower price in one market and selling it at a higher price in another market provides a risk-free profit or arbitrage opportunity. Because price differentials actually happen from time to time, the identical technique can be performed for stocks vs. futures products. Profitable chances can be found by using an algorithm to discover such price differentials and placing orders quickly. 


Rebalancing Index Funds 
To bring their holdings up to par with their respective benchmark indexes, index funds have set rebalancing periods. This generates attractive opportunities for algorithmic traders, who earn from projected trades that yield 20 to 80 basis points profits right before index fund rebalancing, depending on the number of stocks in the index fund. For timely execution and the best prices, such deals are initiated using algorithmic trading algorithms. 


Model-based Mathematical Strategies 
Trading on a mix of options and the underlying security is possible thanks to mathematical models like the delta-neutral trading technique. (A portfolio strategy known as delta neutral consists of multiple positions with offsetting positive and negative deltas—a ratio comparing the change in the price of an asset, usually marketable security, to the corresponding change in the price of its derivative—so that the overall delta of the assets in question is zero.) 


Range of Trading (Mean Reversion) 
The notion behind a mean reversion approach is that an asset's high and low values are a transient occurrence that reverts to its mean value (average value) on a regular basis. Identifying and defining a price range, as well as designing an algorithm based on it, allows transactions to be executed automatically when an asset's price moves inside or outside of its stated range. 

The volume-weighted average pricing (VWAP) approach divides a large order into smaller portions that are dynamically determined and released to the market using stock-specific historical volume patterns. The goal is to fill the order as near to the volume-weighted average pricing as possible (VWAP). 


TWAP (Time Weighted Average Price) A time-weighted average price method divides a large order into smaller portions and releases them to the market in evenly divided time intervals between a start and end time. The goal is to execute the order as close to the average price between the start and end timings as possible in order to minimise market impact. 

Volume as a percentage (POV) 
This algorithm continues sending partial orders until the trade order is entirely filled, based on the defined participation ratio and the volume transacted in the marketplaces. When the stock price reaches user-defined levels, the corresponding "steps strategy" sends orders at a user-defined percentage of market volumes and raises or decreases this participation rate. 


Implementation Defects 
The implementation shortfall technique tries to reduce an order's execution cost by trading off the real-time market, saving money on the order and taking advantage of the opportunity cost of delayed execution. When the stock price moves in a positive direction, the approach will increase the desired participation rate and decrease it when the stock price moves in a negative direction. 


Trading Algorithms That Aren't Typical 
There are a few different types of algorithms that try to find “happenings” on the other side. These "sniffing algorithms," which may be deployed by a sell-side market maker, have the intelligence to detect any algorithms on the buy-side of a huge order. Such algorithmic identification will assist market makers in identifying huge order possibilities and allowing them to profit by filling the orders at a higher price. This is referred to as "high-tech front-running." Front-running is generally deemed unlawful, depending on the circumstances, and is strictly controlled by FINRA (Financial Industry Regulatory Authority). 


Algorithmic Trading Technical Requirements 
The final step in algorithmic trading is to implement the algorithm using a computer programme, which is followed by backtesting (trying out the algorithm on historical periods of past stock-market performance to see if using it would have been profitable). The task is to turn the selected approach into an integrated automated procedure with access to a trading account where orders may be placed. For algorithmic trading, the following requirements must be met: 

Computer programming knowledge, professional programmers, or pre-made trading software are all options for creating the appropriate trading strategy. 
Access to trading platforms and network connectivity is required to place orders. 
Access to market data sources that the algorithm will monitor for order placement chances. 
The ability and infrastructure to backtest the system once it has been constructed before it is put into production on real markets. 
Depending on the intricacy of the rules employed in the algorithm, historical data is available for backtesting. 
An Algorithmic Trading Example 
The Amsterdam Stock Exchange (AEX) and the London Stock Exchange (LSE) both list Royal Dutch Shell (RDS) (LSE). 

To find arbitrage possibilities, we first create an algorithm. Here are a few important points to consider:


The AEX trades in euros, whereas the LSE trades in British pounds. 1 Due to the one-hour time difference, AEX begins an hour before LSE, with both exchanges trading together for the next few hours until trading exclusively on LSE for the last hour when AEX closes. 
Can we look at the prospect of arbitrage trading on the Royal Dutch Shell stock, which is listed in two different currencies on these two markets? 



A programme that can read current market prices on a computer. 
Both the LSE and the AEX provide price feeds. 
A GBP-EUR forex (foreign exchange) rate stream. 
The capacity to place orders and route them to the appropriate exchange. 
Historical price feeds can be used for backtesting. 
The computer application should be able to complete the following tasks: 


Take a look at the RDS stock price feeds from both exchanges. 
Convert the price of one currency to another using the available international exchange rates. 
If there is a significant enough price difference (after accounting for brokerage expenses) that results in a profitable opportunity, the programme should buy on the lower-priced exchange and sell on the higher-priced exchange. 
The arbitrage benefit will follow if the orders are executed correctly. 

Simple and straightforward! The practice of algorithmic trading, on the other hand, is not easy to manage and implement. Keep in mind that if one investor can execute an Algo-generated trade, so can the rest of the market. As a result, prices swing in milliseconds, if not microseconds. What happens if a buy trade is performed but a sell trade is not because the sell prices have changed by the time the order reaches the market in the example above? The arbitrage approach will be rendered useless because the trader will be left with an open position. 


System failure risks, network connectivity issues, time gaps between trading orders and execution, and, most importantly, flawed algorithms are all risks and obstacles. The more sophisticated an algorithm is, the more rigorous backtesting is required before it can be implemented.



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