Learn How to Identify Algorithmic Trading Strategies

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Because of the development of cutting-edge technology and the digitalisation of stock markets, traders can now use automated trading platforms. They can implement various trading strategies and approaches. In this article, we will discuss identifying algorithmic trading strategies, methods, their kinds, and justifications for using them. We will also touch upon the resources you might need to ensure effective and profitable trading.

Identify Your Personal Preferences for Trading

Being conscious of one’s personality is one of the most crucial factors in identifying algorithmic trading strategies. Algorithmic trading calls for high levels of self-control, endurance, and emotional strength. Users must decide not to intervene with the trading strategy while it is being conducted since they are letting an algorithm handle their trading for them. This may be quite challenging, especially during protracted drawdowns. Nevertheless, many techniques that have been proven to be quite beneficial in a backtest can be destroyed by a simple intervention. Recognise that if you want to enter the avenue of algorithmic trading, you will go through emotional challenges and that, to succeed, you must overcome these challenges.

Evaluating Trading Strategies

We require a method for objectively and consistently evaluating the effectiveness of initiatives. Here is a list of the criteria I use to evaluate prospective new strategies:

Methodology: Is the approach directional, market-neutral, mean-reverting, or momentum-based? Does the plan rely on difficult-to-understand statistical or machine-learning methods that require a Ph.D. in statistics? Do these methods introduce a significant number of parameters that might cause optimisation bias? Will the plan likely survive a structural change?

The Sharpe Ratio

The reward/risk ratio of the approach is defined by the Sharpe ratio. It measures the amount of return that can be obtained given the degree of volatility that the stock curve has to deal with. Naturally, we must establish the duration and frequency of the measurements used to calculate these returns and volatility (or standard deviation). For example, a higher frequency technique will call for a larger sample rate of standard deviation but a shorter total measurement duration.

The Frequency

The technology stack, the Sharpe ratio, and the total amount of transaction expenses are all directly related to how frequently you use the technique. Considering all other factors, greater frequency techniques are more expensive, complex, and difficult to deploy. But if your backtesting engine is advanced and error-free, it frequently has much higher Sharpe ratios.


Volatility is closely tied to the strategy’s ‘risk’. The Sharpe ratio characterises this. If unhedged, increased underlying asset class volatility frequently results in higher stock curve volatility and poorer Sharpe ratios. Of course, the positive and negative volatility are roughly equal. Some tactics could be more volatile on the downside. You must be conscious of these qualities.

Average Profit/Loss

Win/loss and average profit/loss features of strategies will vary. Even if more transactions are being lost than won, one can still have a successful approach. Momentum methods frequently follow this pattern since they depend on a limited number of ‘big hits’ to be successful. The trades that are ‘losers’ can be very severe, although mean-reversion techniques typically have profiles where more transactions are ‘winners’.

Optimal Drawdown

On the equity curve of the strategy, the maximum drawdown represents the biggest total peak-to-trough percentage decline. It is commonly known that momentum tactics can experience prolonged drawdowns. Even though previous testing has indicated that this is ‘business as usual’ for the technique, many traders will quit during lengthy slump periods. Before you stop trading your method, you must decide what percentage decline (and for how long) you can tolerate. Since this is a personal choice, it must be properly thought out.

Liquidity and Capacity

You won’t need to worry too much about strategy capacity at the retail level unless someone trades an extremely illiquid product (like a small-cap stock). The strategy’s ability to scale up to more money depends on its capacity. Several larger hedge funds experience serious capacity issues as the capital allocation of their strategies grows.

The Parameters

A lot of parameters are needed for some algorithms, notably those used in the machine learning community. A technique becomes increasingly susceptible to optimisation bias (also known as ‘curve-fitting’) with each additional parameter it needs. Aim for techniques with the fewest possible parameters, or make sure a person has a large enough sample size to test your ideas. Evaluate your approach in real time while avoiding having to execute any actual trades. Algorithms should only be applied when the user is very precise.

Obtaining Historical Data

It aids in determining the worth of your investment. According to the widely accepted reversion-to-mean hypothesis, most asset values return to their long-term historical norms. In other words, if a firm’s Price-Earnings (PE) ratio is higher than past stock data would indicate, it may be overpriced. Then, some other assets can be inversely undervalued. Obviously, not all stocks fall into this category. The general idea is to acquire inexpensive stocks for one’s portfolio using historical stock data. You may occasionally develop an investment plan, and historical stock data helps you backtest this technique.

Even better, users may analyse past stock data to identify trends in the businesses that interest them. They can determine whether sales have risen over the past 10 to 15 years. Perhaps the business is cyclical, making a slump a better opportunity to invest in it.


While trading on the stock and forex markets, you may obtain a competitive edge by using algorithmic trading tactics. For traders seeking automated and immediate order executions, algorithmic trading tactics provide significant benefits. It complements many trading philosophies and methods. The automated strategy must first be put in place. Thus, you must either have some programming experience or engage a skilled developer.

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