Algorithmic strategies are one of the most trending topics in the stock world. Traders and individuals from different markets consider algorithmic trading their primary method of investment. To understand how to develop algorithmic trading software, you can go through the following article and determine strategies based on historical data, backtesting, and financial goals for the algorithmic trading strategies.
To create algo-trading strategies, you need to have programming skills that help you control the technical aspects of the strategy. So, being a programmer or having experience in languages such as C++, Python, Java, and R will assist you in managing data and backtest engines on your own. If you know how to develop a framework for an algo strategy, then it allows you to trade at a higher frequency as you will be in full control of your technology. Through extensive knowledge, you can eliminate bugs and make the essential changes in the software. There are different technical indicators that are used in algo-trading.
Some of the technical indicators are:
- Stochastics
- Moving Averages
- Relative Strength Index
- Relative Vigor Index
- Parabolic SAR
Table of Contents
What Is Algorithmic Trading, and How Do Traders Benefit from It?
A trading algorithm is a series of steps to make a buy or sell order in the stock market. According to current market conditions, your order can be executed or cancelled. With algo-trading you can customise the order and get a human error-free system with constant monitoring and accurate results. Automated trading eliminates the effort of manual monitoring and scanning the market.
This technology helps traders receive instant orders, implement different strategies, and handle complex data. But one thing to remember is that an experienced trading platform development company requires technical as well as financial experts to build a platform that can implement an automated trading strategy.
Key Component to Develop Trading Algorithmic Strategies
To develop algorithmic trading techniques, you need to follow these steps and customise each step according to your requirements.
Step 1: Create a Platform
The first thing you need to do is make or purchase software that can receive data from various data sources and then store it for the backtesting process. The task of testing algorithms is important in order to use a proper algorithm strategy. You can use a pre-existing application programming interface (API) or build your own platform. Today, most people use APIs and an existing platform. This existing platform helps traders to use essential trading tools, customise their trades, and access data and multiple trading strategies.
Step 2: Visualise Your Trading Strategy
Just like you cannot paint a picture without visualising it, for a successful trading algorithm, you need to research and learn different mathematical models and have an in-depth understanding of the financial market. In other words, you need to visualise the strategy in a flowchart.
Step 3: Define the Time Frame and Other Ratios
When using algo strategies to trade, you must define the timeframe and the conditions for placing an order. As per the instructions you entered, the computer will automate your trade. So, you need to be careful when using variables that will be based on the analysis and performance of financial instruments. Another ratio you pay attention to is the frequency of the trades.
Step 4: Test the Algorithm Strategies
When making your online trade, it’s important to test your algorithm before going live. To avoid any errors or malfunctions, you need to test the trading bot. Testing includes running the algorithm with the historical data and then analysing the performance of the algorithm over thousands of trades. If all goes well, you can proceed to the live trading test.
Evaluating Trading Strategies
After you develop algorithmic trading strategies, you need to evaluate the basic principle in the algo strategy. When trading using algo strategies, you need to define the timeframe and the condition of placing an order. To evaluate the strategies:
- Sharpe Ratio Analysis: Evaluate the performance of the algorithmic trading strategy using the Sharpe ratio. The Sharpe ratio measures performance relative to a risk-free asset, considering both reward and risk. Define the period and frequency for measuring returns and volatility to tailor the evaluation.
- Frequency of Trade: Consider the impact of trading frequency on capital requirements. High-frequency trading demands more capital and is characterised by complexity. Thorough backtesting is essential to ensure bug-free results before deploying high-frequency strategies.
- Volatility Considerations: Assess volatility using the Sharpe ratio as a key metric. Higher volatility in underlying asset classes and unhedged assets results in a more volatile equity curve and smaller Sharpe ratios.
- Parameter Management in Machine Learning Strategies: Acknowledge the challenge of managing numerous parameters in machine learning-based strategies. Each parameter introduces a potential vulnerability for optimisation. Focus on strategies with a limited number of parameters for practical implementation.
- Benchmark Utilization: Use benchmarks, such as an index, for measuring strategy performance. Benchmarks provide a reference point to assess the strategy’s success relative to a broad sample of underlying assets.
Conclusion
As we study how to develop algorithm trading software and apply different trading strategies, you need to be sure about the mathematical models and test them by customising different ratios. But if you are not a professional trader or don’t have good knowledge about programming and algo-trading, then you can partner with a broker like Share India. It offers advanced technology along with full-broker service in India. Trading models can often fail or not work in some trading situations. So, trading involves both risk and reward. Other than that, we advise traders to be aware and trade as per their risk appetite, as trading beyond your capital and taking unwanted leverage can harm personal finances. To become a professional trader and use algorithmic trading techniques, you require a significant amount of patience, discipline, and skills. Before making any trade, you need to get an expert’s opinion and then put your funds in the money market.
Frequently Asked Questions (FAQs)
What are the different types of financial assets?
There are equities, bonds, futures and options, commodities and the currency, Sovereign Gold Bonds (SGBs), etc. You can trade in any of these financial assets by opening a demat and trading account with a Securities and Exchange Board of India (SEBI)-registered broker like Share India.
Describe the expertise required for algorithmic trading.
Skills required for algo-trading are as follows:
- Financial market knowledge
- Programming skills
- Mathematical skills
- Analytical skills
- Building strategies for trade
- Risk management
Explain the concept of weighted average price.
The volume-weighted average price (VWAP) strategy is based on time and volume. It assigns all of the small chunks of large-volume holdings based on historical data, taking into account the start and end times. The aim of this strategy is to protect from the impact of the volatility of financial assets by closing the deal as close to the VWAP as possible.
Explain the concept of weighted average price.
The volume-weighted average price (VWAP) strategy is based on time and volume. It assigns all of the small chunks of large-volume holdings based on historical data, taking into account the start and end times. The aim of this strategy is to protect from the impact of the volatility of financial assets by closing the deal as close to the VWAP as possible.
What are the pros of algo-trading?
The pros of algo trading is as follows:
- It offers trade at a low price
- Provides efficiency in trades and better returns
- Offers better risk management
