The Procedure for Creating Algorithmic Trading

Making an algorithmic trading function entails the following steps

Developing a Trading Strategy: The first stage is to establish a trading concept that will serve as the foundation of your trading strategy. a pattern that may have market forecasting potential The most effective methods are ones that most other market players have not discovered, and they can only be discovered through thinking in uncommon ways or being innovative.

·Backtesting: Once you've decided on a trading strategy, it's time to start "backtesting." Backtesting is the practice of validating or disproving an idea by testing it using previous data

Curve fitting: When your trading technique is fitted to random market noise rather than genuine market behavior, it is said to be curve fitting. Trading a curve-fit approach is similar to gambling because the technique has no edge. Curve fitting can ruin your trading career if the proper robustness testing procedures are not used

Making a Portfolio: Many traders spend all of their time seeking the ideal trading method and lose sight of the broader picture Because algorithmic is run by a computer, you may trade an almost infinite number of strategies simultaneously as an algorithmic trader The more you diversify across markets and periods, the lower your risk. When you construct your strategy portfolio, you will discover how much of a difference having the appropriate strategies can make to your total earning potential When one technique fails, you want another to succeed so that you may expand your position size and profit in the long run

Monitoring: Several things may go wrong, and even as trading software improves, there will be hitches. The only way to reduce the danger of experiencing catastrophic losses due to hardware or software failure is to periodically monitor order execution, which is a small price to pay to enjoy the benefits of algorithmic trading

Benefits of Algorithimic trading

Speed: Orders are performed in fractions of seconds, which a person cannot do, and the speed is so accurate that the deal may be executed at the exact price

Opportunities: This includes the ability to employ many indications and carry out commands that no person can. Traders also have additional trading possibilities as a result of speedier research and execution

Decision Making: Volatility in the market may necessitate a very rapid and correct response, as well as the elimination of emotions Multiple transactions may be handled by the algorithm in a fraction of a second. Professional trading necessitates a high level of discipline, devotion, and focus A competent trader must maintain his calm and attention at all times. When making decisions, traders must exclude emotional and psychological variables It's simpler stated than done

Costs and Liquidity: Transaction costs are decreased, and time is saved since it can perform a large number of traders in a short time To follow the price, a trader does not need to be glued to their screen during the market hour. Algorithmic trading contributes to market liquidity by allowing you to trade a high volume of shares in a matter of seconds

The Dangers of Algorithmic Trading

·System Failures: Traders must rely on technology to do business. A key downside of Algorithmic trading is the possibility of system failure or a poor internet connection A single flaw in the algorithm might cause you to lose a significant amount of money on a single transaction

·Mechanical failure or error in coding strategies: Because Algorithmic trading is automated, there is no human control. Once the strategy has been written with instructions, a trader cannot exit the deal even if he subsequently recognizes that the approach will not work.

·Risk of being Outsmarted: Traders must check the system to verify that no duplicate or missing orders exist. Traders must always improve their technical abilities to build the algorithmic

Prerequisites for doing algorithimic trading

Analytical abilities: An analytical mindset is a vital trait for any quant trader/developer, given a large data collection, to detect patterns in it. and how you handle any given situation objectively

Mathematical abilities: Because the core of algorithmic trading revolves around algorithmic, data, and programming, algorithmic/HFT trading requires reasonable programming skills as well as a basic understanding of statistics and calculus

Programming abilities: Knowledge of a programming language is advantageous because it allows you to function independently. Traders are interested in learning about the long-term effects and benefits of coding, particularly ‘Python for Trading

Logic and Reasoning: When developing a strategy, it is critical to understand the risks and benefits of that approach to decide whether it has a competitive advantage in the market This is done during a strategy's backtesting. Before going live with the strategy in the markets, the frequency of trading, instruments traded, and leverage must all be considered.

·Financial Markets Understanding: Quantitative trading entails dealing with massive financial datasets and trading in various instruments such as stocks, futures, currencies, and so on. As a result, even if you come from a non-finance technology background, you will need a good grasp of financial markets to work as a developer in a quant business.

·Econometrics: Modern quantitative trading relies heavily on financial econometrics. For predicting objectives, cutting-edge systematic trading algorithmic make considerable use of time-series analytic techniques.

India's Algorithmic Trading Future

SEBI recently recommended a framework for algorithmic trading by individual investors, in response to the increased usage of Application Programming Interface (API) access, which allows trade automation While orders generated by an API can be identified as such, brokers cannot distinguish between algorithmic and non-algorithmic orders Due to the inability of retail investors to identify algorithmic trading via API access, the requirement for algorithmic approval by stock exchanges before deployment is bypassed, resulting in the rise of 'unregulated and unapproved' algorithms, which may be used to entice retail investors by unregistered and unregulated entities by falsely guaranteeing higher returns.

The framework proposed by SEBI has recommended that all orders originating through API access supplied by brokers to ordinary investors be classified as algorithmic orders, which must be identified by a unique ID and validated by the appropriate authorities Furthermore, before deployment or modification, all such algorithms and revisions must be approved by the stock exchange Furthermore, regardless of the developer, all algorithms would be required to run on the brokers' servers, so that the broker retains control of client orders, order confirmations, and other related information The broker will need to implement adequate checks to ensure that the algorithm performs in a controlled manner Furthermore, the responsible broker would be accountable for any algorithmic trading emerging from an API, as well as grievance redressal regarding the pertinent algorithmic trades Algorithmic trading is advanced in many ways; apart from the potential for high gains for the trader, it is more methodical since it eliminates the effect of human emotions and mistakes It also increases market efficiency and liquidity. Algo techniques now account for 80-85% of transactions in established markets, While India has a 50-60% market penetration of algorithmic trading, it is expected that algorithmic trading will continue to increase in Indian markets. The future of algorithmic trading forecasts that as the market expands, the resources for algorithmic trading will evolve and become more organized and efficient.