Algorithmic Trading Guide 2022 For Beginner

algorithmic trading

Automated trading has woven its inevitable way into the fabric of modern Industry. Automation is the vital watchword if your business seeks to stay ahead. Automated trading is treated in two parts: Algorithmic Trading and High-Frequency Trading (HFT). The former is referred to most of the time in public discourse about Forex trading. 

What is Algorithmic Trading?

As they pursue a rule-based system to select trading instruments, identify trading opportunities, manage risk, and optimize position size and capital use, algorithmic trading strategies are always on the ascent. Algorithms handle the entries and exits as well, systems being automated. Besides being (mistakenly) conflated with the term ‘automated trading’, algorithmic trading comes with a set of monikers and appellations. Electronic trading, systematic trading, mechanical trading, quantitative trading, and black box trading refer to algorithmic trading.

Trading systems all follow a set of rules, never wavering from it. When a trading system purchases an asset as its 20-day moving average crosses above its 50 -day moving average and then sells the asset on the 20- day moving average crossing below the 50 -day moving average, we have an algorithmic trading system. 

While any trading class is amenable to treatment by algorithmic trading systems, the latter is most purposeful in trades involving liquid assets on exchanges or interbank markets. Algo trading serves little purpose in illiquid bond markets or small/micro-cap stocks. Algo systems’ versatility permits their use in any time frame, from sb-microseconds to weekly/monthly time frames. 

The Mechanics of Automated Trading 

An algorithmic trading system needs a stock exchange live price feed. One requirement is incoming price feed reading software that can run trading programs -apart from submitting orders. Hardware that runs the software is a prerequisite. Market sentiment/fundamental data must be had through supplemental feeds, as well. 

Moreover, a rules-based trading strategy has to be coded. When the strategies are on the software, the algorithm will survey the market, looking for compatibility with its own specified preconditions. Auto-generated orders are subsequently submitted to the exchange in question. A message is relayed to the platform, updating position and order management tools, instant upon trade execution. 

Apart from these chores, automated trading algorithms manage live trades, manage risk, and exiting trades soon as targets are achieved or stop-loss levels. Any such system has to see exposure management and the deletion of obsolete orders.

The Market For algorithmic trading Systems

Algorithmic trading is often co-mingled in discourse with HFT (High-Frequency Trading). HFT algorithms, blazing-fast, make good use of the price differences between exchanges. Programs have, indeed, become an integral part of financial markets. Trade and investment companies are replete with algorithmic trading systems. New technologies have necessitated the evolution of newer trade and money management models. 

Trend following funds created the first automated trading systems. These were predicated upon a limited set of parameters – price and EOD Data. Involved early mainframe computers were generating trade signals. That was indeed a long time ago! Research, stock selection, trade execution, and risk management – algorithmic trading systems run the whole show for fund investment processes. 

Quantitative investing funds look for relationships between securities and optimize strategies through deep tech. Deep tech brings together computing power with statistical-mathematical models with the objective of minimizing risk-adjusted returns. Subsequently, deep tech identifies and undertakes swift trade execution. 

Hedge funds have become progressively automated trading dependent. Data Intelligence funds employ news and social media platform data as real-time sentiment scores. 

Institutional brokers and banks prefer stock trading algorithms that execute large orders. Algos keeps risks low, maximizes profits, optimizes price, and therefore finds great favor with market makers. Algorithms are a darling of options traders, too -the latter dynamically hedging positions and managing risk, even as prices move. 

Algo trading has already gained wide acceptance in the day trading community. Retail traders and investors can now readily access automated trading platforms and algorithmic trading software.  Deep tech has also evolved to the extent that highly sophisticated systems are actually easy to operate. MetaTrader and NinjaTrader are among the new generation of trading platforms that permit traders without a programming background to set up automated trading systems. 

Leading Algorithmic Strategies 

As discussed elsewhere here, the most basic algorithmic trading systems involve just a couple of indicators. Antithetically, ground-breaking funds utilize information from company financial statements, big data, and AI, seeking to identify opportunities. all useful strategies can be transformed into rule-based algorithms.  We get a brief overview of leading algorithmic strategies below : 

  • Trend following strategies ;
  •  Mean reversion strategies ;
  •  Arbitrage trading strategies ; 
  • Statistical arbitrage ; 
  • Index arbitrage ; 
  • VWAPand TWAP algorithms ; 
  • Quantitative investing strategies ;
  •  Quant trading strategies ;
  •  Index changes. 

Trend Following Strategies

Ensure the fund’s position in the current trend by selling weakness and buying strength. Historical high and low-based trend channels and moving averages are what these systems find most useful. The aim is to minimize losses during consolidation periods and capture long-term trends. 

Mean reversion strategies

Monitor prices that revert to their average, immediately profiting from that fact. range-restricted price periods are when this is most true. These strategies are predicated upon oscillators, volatility bands, and moving averages. The identification of extremes through market sentiment parameters is one course of action preferred under these strategies. 

Arbitrage trading strategies

Look for profit from temporary mispricing, opening long and short positions simultaneously. When the same security trades on different exchanges at dissimilar prices, these strategies are put into play. Varied share classes and convertible bonds may also be used in conjunction with said strategies. 

Statistical Arbitrage

Opens long and short positions in similar stocks, based upon an admixture of fundamental and price data. An algorithm might, for instance, open a short position in Shell and a long position in BP based on relative valuations. Without having exposure to the market or the oil price, such strategies place a bet on the change in relative valuations. 

Index arbitrage

seeks profits from mispricing between futures markets and equity. Traders lock in risk-free profits through the opening of long and short positions in underlying stocks and futures contracts. This happens when an index futures contract and the index it is based upon, move apart. In this scenario, an algorithm buys/sells all the stocks that comprise the index – thereby executing the trade in the most consummate fashion. 

Large institutions deploy VWAP and TWAP algorithms

To execute large orders. An algorithm may purchase a given number of shares at the day’s VWAP (volume-weighted average price). The algorithm will keep on purchasing shares throughout the livelong day, thus maintaining synchronicity between the average price and the market’s average price. TWAP(Time Weighted Average Price) employs the market price periodically to calculate the average price. These algorithms may also be so manipulated as to enable their trading of a certain percentage of the total market volume. Algos such as these restrict large orders’ market impact. 

Quantitative investing strategies

Select securities to buy and sell, employing varied factors including value, growth, momentum, or dividend yield. 

Quant trading strategies

use any combination of price and fundamental data. 

Index changes

catalyze is also used. Frequently, indices undergo re-balancing. Algos may calculate the possible orders that may emerge from demand and supply changes. 

Algorithmic Trading – How Does It Work?  

Algorithmic trading is already highly evolved and offers a rewarding career. Mathematically modeling trading concepts, followed by the coding up of the same to aid monetization is an exciting and challenging work line. Algorithmic trading could symbolize the broader economy in a nutshell. Algo trading is blindingly fast, with no quarter given to laggards. 

Algorithmic trading is a coder with Python programming knowledge under his belt. Also, he has a natural aptitude for applied math and statistics. But he’s no nerd or recluse. He’s a people person. He’s in contact with people across departments. 

Algo trading facilitates trade. It is a low-cost, highly efficient manner of trading, and it covers a lot of ground. From high-frequency sub-millisecond forex market-making systems to multi-second sophisticated derivative systems – algorithmic trading has a deep reach. For large volume trading orders executed by multi-hour trading systems, algorithmic trading speeds up trading.

A software engineer could automate an elementary trading system. The trading platform’s role is to render connections to a forex broker. The broker gives real-time data and executes buy/sell orders. The data feed follows the outline below. 

Automated Trading Platforms

What MetaTrader4 does, is access all data with internal functions. The latter is accessible in different kinds of time frames. These range from every minute (M 1) to every hour (H 1). There are other time frames, standing for the 4th hour, the day (D 1), the month (MN). InvestBY has the latest trading platforms to ensure safe trading that’s precise plus profitable. 

A tick gives the current price movement. A tick may be understood as the change in a currency pair ask/bid price. There are many tricks for every second on a good trading day. On dull days, there might not be ticks at all for minutes. The tick may be understood as a currency market robot’s heartbeat. Placing an order through such a platform, a trader buys/sells a certain currency in a predetermined amount. The most number of pips ( price variations) that a trader can afford to lose before calling it a day is the stop-loss limit. The amount of pips a trader collects before finally exiting trading is called the take profit limit. 

Formulating Trading Decisions

Our hypothetical trader customer could make the following demands on our coder/engineer. The engineer could be asked to create trading robots featured with two indicators. Indicators are based on past data. A good instance of the latter would be the last day’s highest price value. Indicators come in handy, defining market conditions, and formulating trading decisions. 

Our hypothetical trader client could ask the engineer he would like to trade just when the indicators cross at an ascertained angle. 

Some functions execute a trade for each tick. 

Backtesting provision 

Once the algorithmic trading system is ready, it can be tested to see: if it is working as expected; if the forex trading strategy it has deployed is useful at all. The testing takes place with reference to past events. 

MT4 has functionality-adept application tools. In order to begin, the trader sets up his time frames and runs the program under-stimulation. Knowing that unit, the tool ought to open at a certain point, close at a given point, and reach given highs and lows. 

Program actions can be compared against historical prices. In order to know if the program is working, such a comparison is essential. 

It is possible that the client trader can choose the wrong indicator set, as also a faulty decision logic. The engineer may tweak the client’s trading robot a bit – however, there cannot be radical changes without a complete overhaul. 

Parameter optimization

 our hypothetical software engineer (and forex trading enthusiast) could now take note of major differences in the risk-reward ratio. 

The first parameter will, in all possibilities, over-predict future results. Any shift, or uncertainty, will result in far worse performance. 

The future of the market is not knowable. To seek to find future trends on the basis of past results is not going to work. Actually, your forex predictions must have this unpredictability factored in. 

This shows the perils of using optimized parameters in Algo trading. These can give results that exaggerate possible future results. 

Vast Future Prospects for Algo Trading 

For those who develop automated algorithmic trading systems, there are a lot of possibilities. The engineer could develop systems with the purpose of locating ‘big fish’ movements. These would be massive pip variations in very small time units.  Software engineers can exercise their imagination as well as learn to earn on the go. When they build their own forex simulation system, they could foray into hitherto uncharted fields.

 For instance, they could attempt to decipher the probability distribution of price variations as a function of volatility in a given market with any degree of accuracy. Devising a trading plan and concomitant trading strategy set is very easy for such engineers who would like to trade as well. 

Automated Trading Robots: EAs

A programmed trading system that permits automated buying/selling of assets as per predetermined rules is automated trading software or trading robot.  Also known as EAs or Expert Advisors, these systems are quite popular. 

Robots that are ideal traders 

By virtue of their being automated, EAs are disciplined. They are programmed to follow a set of strategies with precision. An emotional response to market events is not an option for traders. These robots come in very handy since they are free of emotions. Not least importantly, they can work throughout the year without a single break. 

Such robots were developed to enable traders to trade different assets across platforms all at the same time. 

The Mechanics of Automated Trading Software

Utilizing advanced mathematical models, automated trading software systems execute faster trades through swift decision making. 

EAs are programmed to do all that a forex trader expects of himself. This is done at a very fast speed and with accuracy. Pre-determined strategies are programmed into EAs, even whole trading plans. The best thing is that EAs are not susceptible to psychological trading mistakes at all. This explains their popularity. 

A broker account can have an EA linked right to it. Also, EAs can be coded right into a specific platform’s proprietary programming language.

The biggest investors in EAs are said to be the big banks. These have systems worth millions of dollars, and they hire the best programmers through companies. The most sophisticated programming languages (think ‘Python’) are part of your repertoire? Then fully expect to be hired by the likes of JP Morgan!

 Advantages of Automated Trading

  • As opposed to discretionary trading based on forecasts and theories, algorithmic trading systems are empirical evidence-based. These can be backtested. Discretionary decision making and forecasts are hard to assess ;
  •  algo trading systems make it possible for traders to research a large number of securities. Humans cannot have such range on their own ; 
  • opportunity sets for sub-microsecond periods are impossible without algorithmic trading systems ; 
  • algo trading systems are error-free and immune to psychological mistakes that become a bane of human traders’ existence. 

Demerits afflicting Automated Trading Systems

  • There are other players in the field, and this can erode your trading system’s competence. If you are operating with small margins, working with EAs could be less profitable, thanks to higher transaction costs; 
  • Algo trading systems have some way to go before perfection. In this day and age, there might still be some advantage for mere humans when differentiating between winning and losing streaks ; 
  • Volatility spikes and flash crashes are another set of problems. Algos have a ways to go before they can sort out causes of volatility by themselves. 


Automated trading is fast becoming the norm for fund managers and short-term traders alike. The employment of algorithmic trading systems ( as also HFT systems ) has made the market undergo changes at the infrastructure level. This will force a new evolution wherein algorithmic trading system management will become a prerequisite for trading. For better or for worse, automation’s here to stay. The opportunities are actually more numerous – albeit so microscopic that humans are unable to detect them unaided. InvestBY helps you profit in its Brave New World with its impeccable automated trading platform!