Algorithmic Trading Vs Discretionary Trading

Algorithmic Trading Vs Discretionary Trading

By Nitin Thapar

Introduction

If you are a discretionary trader, you might have asked these questions before

In order to answer these questions, we first need to know what makes these practices stand apart from each other.

In this post, we will make an attempt to decode all the questions related to algorithmic trading vs discretionary trading.

Discretionary Trading

A discretionary trader has a set of rules that they tend to follow throughout their trading practice, these rules are modified or replaced based on their experience and what works best for them. Some follow these rules rigorously, while others tend to experiment till the time they feel they have cracked the code and continue to make required modifications in their strategy.

A discretionary trader studies the signals and charts, and then makes a decision on whether to buy or sell the asset. The trader calls the shots in discretionary trading i.e when to enter or exit positions.

In discretionary trading, maximum risk originates from decisions taken under the influence of uncontrolled emotions of the trader. In most cases, these emotions can lead to trades which cannot be logically defended. Hence in order to make a profit, it becomes extremely important to not just have a profitable strategy, but also have a check on one’s emotions.

Algorithmic Trading

Systematic traders use algorithms to make trading related decisions or predict their best chance of making a profit out of the investments that they make. The algorithms are changed based on the market conditions, the type of shares, markets etc.

A systematic trader cannot stand the degree of uncertainty by relying on studying the charts manually and reading the signals. He/she prefers to make predictions based on historical data, build an algo strategy that suits the market conditions, code it and turn it on. Their role becomes that of a spectator who monitors the algorithms performance based on the logic that has been built and makes the required changes once the algo has dropped in performance or has stopped working.

Key Differentiating Factors

Trading strategy:

The trading strategy of discretionary traders is derived from the information gathered by learning charts, market conditions, understanding indicative signals and other relating factors which help them to draft a certain set of rules to follow before placing an order or deciding when to exit.

An algorithmic trader, on the other hand, finds it risky to depend merely on the findings gathered by examining charts. The decision of placing an order or making an exit depends on the algorithm(s). The algorithms are designed based on:

  • Knowledge of Derivatives
  • Programming Skills
  • Statistics & Probability
  • Risk Management Skills
  • Study of Historical Data
  • Forecasting

This is done by algo professionals with the required skill set. The system studies the market and makes decisions based on the logic set for the algorithms.

Influence of human emotions:

Discretionary traders are prone to be influenced by emotional factors at the time of decision making. Traders often tend to defend their emotional bias at the time of projecting the outcome which may lead to significant losses.

The risk of getting influenced by factors related to emotions is almost nil in algo trading. The mathematical models are purely based on the set of instructions and eliminate the intervention of any kind of emotions be it greed, fear, false intuitions etc.

Automation:

The practice of discretionary trading restricts the use of automated systems that call the shots for you. It is managed manually by the trader and the system has little or no say in what you want to do next.

There is no need for an algorithmic trader to monitor markets and read charts, as trades are done automatically. The information fed into the system is processed by the black box and suggestions are made for the best possible outcome. Once the trader is convinced of the outcome they can switch the algos on and just screen the progress and make changes accordingly.

Pre-defined rules:

There are no pre-defined rules for a discretionary trader. The purchase or exit is made based on the experience and the study done by the trader which may result in multiple trading rules for each execution.

The rules in algorithmic trading are pre-defined and backtested. The backtesting of historical data increases the probability of a successful outcome. The trades are placed at pre-defined levels which are governed by algorithms.

Analyzing current market conditions:

An impulsive behaviour of a discretionary trader due to a sudden change in market conditions may result in a loss. This may be due to the lack of understanding or failure to read the volatility of the market.

Techniques like sentiment analysis help algos perform better in such scenarios and are able to read the fluctuations in markets based on external factors.

Indicators observed by a Discretionary Trader

MRF price chart

A typical set of observations made by a discretionary trader on the price chart mentioned above can be listed as:

  • The overall trend is up
  • Where should I put my stop and limit?
  • Current news that can affect the upward moving trend
  • The moving average is going up as well
  • The current indicator signals a reversal

Indicators observed by an Algorithmic trader

algo trading_1

Algo trading_2

The observations and conclusions made by an algorithmic trader can be listed as:

  • What is the success of the algorithm and the probability of it making a profit for me?
  • What does the historical data indicate?
  • The future estimates of the stock based on current and historical trend
  • What does the time series of a stock indicate
  • What is the margin of error in the strategy that I have designed?

Conclusion

Technology is a part of evolution and us humans have generated technologies that will define this century. Adapting to new and better means of trading is akin to moving to better results and one cannot run away from it. Algorithms reduce the margin of error and remove the ‘human factors’ like emotions, manual trading based errors, stale trading strategies etc

Like a super computer algorithmic trading follows strict discipline and logic.

To quote Albert Hibbs: “Even though I didn’t make it to the moon, my machines did”

So let your machines make the money for you.

Next Step

If you want to learn various aspects of Algorithmic trading then check out the Executive Programme in Algorithmic Trading (EPAT™). The course covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading. EPAT™ equips you with the required skill sets to be a successful trader. Enroll now!