Algorithmic Trading Strategy: How to Develop Trading Algorithms

Algorithmic trading is a sophisticated method of trading financial markets that leverages technological resources to build curated strategies based on automation and seizing high-standard trading performances and fast executions.

This type of trading procedure also aims to go beyond human limits in terms of data processing, detecting setups, executing entries, and avoiding pitfalls related to psychological and emotional biases.

Overviewing Algorithmic Trading

Human traders can face hard times during losing streaks or experience overconfidence during winning streaks. In any case, such behaviors can be detrimental to trading performance and, subsequently, the trading account.

Since algorithmic trading relies on machines and computer programs, it is not susceptible to hesitations. In this sense, algorithms help traders achieve better results as they can be more consistent with a specific strategy.

We can list the principal benefits as follows:

• Since algorithms are automated trading systems they reduce stress helping traders save traders from biases while executing entries and exits.
• Algorithms are likely to remain strongly consistent as they are systems following pre-defined and specific instructions.
• Algorithms for trading are highly effective in detecting several trading setups that can be missed by humans.
• High-frequency trading is a plus in this matter, as algorithms can be streamlined to execute several trades in short periods, quickly and efficiently.

When it comes to developing algorithms, it is also pertinent to mention potential challenges to implementing these systems:

• The quality of data can damage the performance of the algorithms as they could be obeying wrong instructions. Ensuring the reliability of the information provided can lead to accurate predictions and better setups.
• In terms of high-frequency trading, low latency can delay the data transmission, inducing the algorithms to miss trade opportunities or execute the less effective ones.
• Concerning hardware and software, Algo Trading needs optimized computers to achieve the best results. Maintaining larger algorithms can be costly.

Key components of algorithmic trading

• Data: Historical price data, market news, economic events, and indicators provide valuable insights alongside real-time data such as market depth, order quotes, and order flow and book dynamics.
• Models: These consist of statistical techniques like linear regression. Related to models, machine learning features enable algorithms to learn from data and adapt to changing market conditions rapidly and efficiently.

Developing Trading Algorithms

Role of data in algorithmic trading

To develop accurate algorithm systems, the quality of data is crucial. Market data like past and present prices alongside order dynamics can boost the algorithm's future performance.

In the case of cryptos, fundamental data such as project roadmaps, network improvements or issues, blockchain events, and so on, can determine the market impact of the algorithm development.

Data collection and preparation

To retrieve the best data, developers, and market analysts should aim to collect data from the most reliable sources. This could imply using programming languages to do scraping and assemble large datasets.

Feature engineering

Standard algorithms can be enhanced in the meantime by featuring advanced engineering models like:

• Machine learning.
• Non-linear relationship models.
• Statistical calculations on standard deviations, mean regression, and correlation within data.
• Custom technical indicators based on moving averages, RSI, and MACD.
• Volatility metric measures, etc.
• Features interactions.

Model selection and training

Developers can choose to train specific models like:

• Linear regression for modeling linear relationships between variables.
• Time series models to forecast time-dependent data.
• Machine learning models leveraging neural networks for trading.
• Ensemble models combining multiple models to improve performance.

Backtesting and optimization

Before deploying the algorithmic models, backtesting is a prior step. This can be done with the same data already collected. By backtesting, the algo traders can expect:

• Performance calculations and analytics over metrics such as profit/loss, Sharpe ratio, drawdown, and win rate.
• Analysis of the results to identify strengths, weaknesses, and areas for adjustment.
• Assessing the overall performance of the algorithm in changing conditions by simulating different markets.

Deployment and monitoring

Deployment consists of choosing the right platform to test the algorithm and implement it live. A platform like Altrady can be an excellent trading terminal for building and deploying algorithms. It also emphasizes the analytics features to monitor metrics such as profit/loss, Sharpe ratio, drawdown, and win rate.

To deploy the algorithm it is crucial to attend to the following:

• Risk management policies to maintain robust protection of the initial capital.
• Testing on a demo account before going live.

Also, it is important to consider:

• Current market volatility
• Potential technical issues.

Common Algorithmic Trading Strategies

Trend-following

It consists of identifying a trend and sticking to the prevailing direction in the market. Algorithms could be optimized to perform DCA (Dollar Cost Average) entries for long-term investments in trending assets.

Mean reversion

This strategy is based on the assumption that asset prices tend to reverse to their long-term average following a sustained movement.

Arbitrage

It consists of detecting disparities in the price of the assets and seizing an advantageous opportunity to buy at a price and sell at another across different exchanges.

Statistical arbitrage

Similar to normal arbitrage. In this case, the buying and selling activity is based on statistical models that allow algo traders to anticipate mispriced conditions in the assets.

High-frequency trading (HFT)

This strategy involves executing several trades at high speed allowing algo traders to take rapid advantages in the market. In this sense, it is very suitable for scalping trading styles.

Pairs trading

It seizes opportunities to profit from trading two correlated instruments at the same time expecting a disparity in their prices as a consequence of an impact of one to the other.

Blockchain analysis

Specifically tailored for cryptocurrencies, this strategy will try to establish a correlation between network and wallet activities alongside the volume present in exchanges in an attempt to predict market movements.

For example:

Mt. Gox, the US, and Germany have been transferring BTC units to exchanges recently, and this activity alerts market participants expecting a massive supply induction in the market, influencing the selling pressure.

Conclusion And Technical Considerations

If you are a market analyst or trader, consider familiarizing yourself with programming languages like Python, R, and C++, to understand how they work in terms of algorithmic trading,

If you are a developer could consider familiarizing yourself with data sources like market watchers, economic indicators, and market events.

Algorithmic trading is so far a sophisticated method that can achieve the highest standards for trading executions. It helps traders approach the markets in an automated way. 

You can give a try to algorithmic trading in Altrady by signing up for a free trial account and start testing on paper trading with a wide range of features like the market explorer and watchlists and alerts.