Trading Bot Development Guide: Costs Strategy, Team, and Steps
A fascinating niche in the automation field is trading. The art of trading stocks and crypto assets has always been associated with human craft and mastery, a keen eye, and an advanced ability to anticipate price movements using a combination of analytical instruments.
Yet, recent AI developments have shown that most strategies can be digitized and automated. Thus, coding for trading has acquired immense popularity among retail and institutional investors.
It’s unsurprising that the global bot services market is rising at a CAGR of 33%+ every year and is expected to exceed $6.7 billion by 2027.
There are tons of paid and free trading bots in the modern market, but most of them come with pre-made settings and offer little room for customization.
This article shares in-depth insights into the mechanics and operations of trading bots. It shows how to make a trading bot based on your trading strategy to reap the benefits of automation and smart technology.
What is a trading bot?
A trading bot is an automated tool that allows traders to open and close deals on the exchange based on a pre-set combination of parameters. The bot is linked to the user’s trading account and can execute transactions. The trades are done if they detect a favorable market situation meeting the set parameters.
Trading bots can work in a variety of markets and asset classes, such as cryptocurrencies, stocks, forex, commodities, futures, and options. Overall, trading bots may be used in any market where trading is done electronically and where there is adequate liquidity to execute deals efficiently.
Today’s bots are highly customizable and increasingly advanced, giving users various customization options. For example, you can program your bot by specifying:
- The deal’s entry rules: e.g., if the price reaches a specific price target or rises/falls for a specific percentage.
- Exit rules: what percent of profit the deal should yield to be automatically closed.
- Position sizing settings: whether the bot can reduce or increase the position depending on the price movement.
The infographics below illustrate all key components of algorithmic trading.
What data do trading bots use?
Trading bots don’t operate in a vacuum. They use a variety of data sources to analyze deal entry and exit points. The most popular sources are as follows.
Historical Price Data
Analysis of trends, patterns, resistance, and support levels, and the rest of the technical evaluation is done based on the history of the asset’s price movements. Bots use the big datasets of the asset’s price dynamics to identify the best entry and exit points for their trading operations.
Social Media and News
Published news and social media posts create the sentiment around a specific asset. Even if the technical setup is favorable for opening a deal, an unexpected news release may ruin it pretty quickly.
Many traders link their bots to news feeds and social media RSS to monitor the situation and anticipate an abrupt change in sentiment.
There are many auxiliary indicators for technical analysis and decision support in the trading niche. Some examples are moving averages, MACD, RSI, Bollinger bands, and Fibonacci correction levels. Tons of others are available in standard trading platforms’ packages.
You may customize your bot to react to specific indicators and open or close deals based on signals derived from a combination of technical indicator setups.
Order Book Data
The order book is a litmus test for the relative strength of buyers and sellers at any given moment. A smart bot can track the number of sell and buy orders in the order book and make trading decisions based on fluctuations in the market liquidity.
Broader economic dynamics are also vital aspects of assets’ price movements. Experienced traders always keep their fingers on the pulse of the broader market and analyze data on GDP, unemployment rates, and inflation to understand where indices (e.g., S&P 500, Russell, Dow Jones) will move next. Modern bots can also consider this data and adjust their trading settings based on macroeconomic updates.
It’s not necessary to limit your bot to one or a couple of data sources from the list we’ve just discussed. Combining several indicators into a well-designed and polished strategy can improve your trades’ profitability by removing the market noise and weeding out weak signals.
How Do Regulators Affect the Trading Bots’ Behavior?
Regulators have a pronounced influence on bots’ operations because a bot should be coded in line with regulatory compliance rules of a certain market, be it cryptocurrencies, stocks, or any other type of asset market.
Amendments to these rules necessarily presuppose the bot settings’ changes, as a failure to follow the law comes with severe fines and litigation risks. It’s vital to program a bot flexibly so that new rules and updates can be integrated without extensive re-coding and bot redesign.
Another aspect of regulatory impact on algo-trading is a set of precautions against market manipulation and insider trading. Bots can’t operate using confidential data; if such violations are detected, the owners and operators of such bots are sure to face legal action.
Distinctions between a Simple Trading Bot and an Advanced ML-Based Trading System
There’s much variety in the market of trading, and users can pick among tools with different levels of sophistication and complexity. Here are the main differences between a simple trading bot and an advanced trading system using Machine Learning algorithms for decision-making.
|Simple Bot||Advanced System|
|Operation||Pre-programmed rules and indicators for deal entry and exit. |
Straightforward operations based on the user's input.
|Machine Learning algorithms for continuous analysis of Big Data from various sources.
Identification of complex patterns and trends.
Adaptation to changing market conditions to embrace emerging market opportunities.
|Risk Management||Simple risk management strategies lacking flexibility.||Advanced risk management strategies (e.g., dynamic portfolio optimization and position resizing).|
|Overall Efficiency||Effective in predictable, stable market conditions.||Greater flexibility and adaptivity to changing market conditions.|
As one can see, simple trading bots are cheaper, easier to master, and quick to configure. They work pretty well in specific market conditions but may fail if the market landscape changes abruptly.
Advanced systems, in turn, are more expensive and require advanced skills for efficient navigation. However, they are more adaptive to market changes and can help a trader identify opportunities that other systems won’t detect early enough.
Six Main Tips for Successfully Running a Trading System
Running a trading bot is not a walk in the park. Most bots that can deliver impressive trading results require technical proficiency and discipline in setup.
The trader using a bot should comply with the following rules to maximize the bot’s performance and preserve their deposit.
- Stick to a strategy. No bot can work without a consistent set of parameters for entering and closing a trade. To equip the bot with these settings, you need to have a clear strategy in mind. It’s better to use backtested strategies that perform well in different situations and offer a reasonable risk-reward ratio.
- Choose data sources. Link your bot to trusted data sources that supply information on time. This way, your bot can make informed, data-backed decisions without flaws.
- Manage risks. Equip your bot with a risk management system that specifies the percentage of stop loss orders, conditions for position resizing, the percentage of deposit allowed for one position, etc.
- Exercise correct money management. You should have a sufficient deposit to support your strategy’s healthy execution. Some strategies presuppose minimal deposits to work well, and ignoring these preconditions can cause unexpected losses or margin calls.
- Be disciplined. Traders are often prone to emotional decisions and steps on the market, which bots don’t do. It’s better to equip the bot with all trading settings to let an automated system do the job more consistently.
- Don’t stop. Thinking that a bot should be configured once and for all is a huge mistake. You should monitor the bot’s performance and analyze profitable and failing deals to see how to improve and adjust the settings.
Though these rules seem self-obvious, following them is not that simple for many traders. However, only discipline and continuous improvement can make algo-trading profitable in the long run.
Trading Bot Platforms vs. Custom Trading Bot Development
There are a ton of ready-made trading bots online. Some require a one-time payment, while others are available at a monthly subscription costing $15-700, depending on the service package. So, why bother with the lengthy and expensive custom software development?
Here is an illustrative comparison of ready-made and customized trading systems to help you make a more informed decision.
|Custom Trading Bots||Advanced customization, the ability to add all required features, indicators, and settings;|
Robust security protection.
Commitment to lengthy collaboration with the development team to explain specifications and fine-tune the system to your expectations.
|Trading Bot Platforms||Straightforward and cheap; |
Quick to set up
A large variety of ready-made products on the market;
User-friendly interfaces and settings;
Suitable for people with limited technical knowledge.
Only a set list of parameters is available;
No ability to experiment with individual trading strategies;
Monthly payments for subscriptions;
Server or hosting problems, security issues.
This comparison illustrates that every option has pros and cons, meeting the needs of specific trader categories.
Those who start and lack advanced technical skills can try out a pre-made trading bot and see how it goes.
Professional traders with enough budget for software development can go for the second option and create a secure, stable, fully customized system that works as they want and adapts to tiny market changes.
Pros and Cons of Using a Trading Bot
Trading is a very demanding industry in terms of emotional balance and self-discipline. It’s pretty hard to keep calm when your money is at stake. Those who fail to cope with their emotions and make stupid mistakes because of psychological pressure should consider using a bot instead.
Trading bot development shouldn’t be considered a panacea to all trader’s troubles. It obviously comes with many advantages:
- Automated trading. All people are subject to typical human flaws, such as emotional tension, fatigue, or a lack of concentration. Using a bot helps address those issues and avoid panic deals, FOMO, and emotional disorientation. A bot is not subject to the flaws of human emotions and psychological traps. It will close the failing deal without remorse and hesitations.
- Speed and efficiency. Manual opening and closing of trades takes time and requires experience. Bots can perform these operations much faster, saving you from errors and critical delays. In addition, the bot can send you notifications based on your settings, thus removing the need to stare at the screen for hours.
- More time for yourself. Successful trading means always being “into the market”, tracking the news and following the key assets’ price dynamics. But it’s hard to be online 24/7, and bots can do some essential work instead of you. For instance, it may complete deals in line with your trading strategy when you’re out or having a good night’s sleep.
Even with a simple Python trading bot, you can amplify your market outreach and improve your trading performance. However, using a trading bot has some cons you should also consider:
- Need for monitoring. There’s no ideal bot setup that will guarantee you 100% profitability in deals. The majority of well-configured bots can yield you moderate profit, but it’s vital to monitor their performance and avoid falling into the technical trap and losing all your money.
- Technical proficiency is needed. Bots work well if they are wisely configured. To achieve that, you should be proficient with basic coding and settings to make the bot function exactly how you expect. That’s usually a task for technical specialists that not every layperson can handle.
Consider these points to make a balanced choice between manual and automated trading. Here is a visual presentation of these issues – see the infographics below.
How Difficult Is It to Build a Trading Bot?
Many traders are scared off by the seeming complexity of trading bot creation. Indeed, people without technical skills and coding knowledge can hardly create a well-functioning bot. Such a project requires an inside-out understanding of the financial market’s basics.
It is vital to consider the following parameters before starting a bot creation project:
- The programming language you want to use in the bot;
- The trading strategy’s complexity;
- The number of features and parameters in the bot;
- External data availability.
Open-source libraries are plentiful online today, and if you’re motivated enough, you can develop a bot on your own. Still, a functional trading bot is a product of deep technical expertise and high-quality financial market insights.
How to Build a Trading Bot: Key Steps
Building a custom-tailored trading bot takes time and requires proper organization. Here are the main steps the development team will go through before presenting the final product.
#1 Strategy Development
Strategy is the basic component of any trading bot, as it gives your algorithm a roadmap for analyzing data, identifying meaningful items, and producing trades based on those findings. After understanding what exactly you will look into and how you will act upon that data, you may give your bot the needed instructions for entry, exit, and position sizing.
You will need to develop a set of “if-then” statements to make the strategy more sophisticated and accurate. Don’t over-complicate the strategy. Too many filters or conditions can prevent your bot from fulfilling its purpose. Please focus on the ultimate outcome of your strategy and write down very detailed specifications for its implementation in the bot’s logic.
#2 Technology Selection
The underlying technology you will use in the bot’s creation is crucial for its proper functioning. The most optimal tech stack for this task is Python, Java, or C++.
#3 Exchange Selection
Now that you’re done with the strategy and have a tech stack for the bot, it’s time to choose where you will trade.
If you want to deal with crypto stocks, crypto exchanges like Binance or Coinbase will be more suitable. If your target is commodities and regular stocks, you need to work with a regular broker, like Robinhood or Charles Schwab. Those working with currency pairs may need to register an account at FOREX.
Check the following before registering and depositing money:
- Does your jurisdiction allow trading these assets?
- Does the exchange offer a public API for the bot’s use?
- Is the exchange well-known, reputable, and reliable?
- What daily trading volumes does the exchange have?
Checking off these points from your checklist guarantees that you work with a reliable resource, enjoy low risks of price slippage, and don’t break the law.
#4 Server Choice
A server is needed to allow your bot to send requests to the exchange’s API. It’s impossible to complete trades without a stable and robust bot-exchange connection. An easier option is to use your computer as a server, but it’s not sustainable. A better way to go is to choose:
- A cloud provider (AWS, Digital Ocean, Azure, and many more provide full server capacity for traders)
- Try a Raspberry Pi as your server (e.g., Cooler service).
#5 Bot’s Development
Once you’ve polished the strategy and picked the programming tools for your bot, it’s time to build it from scratch. Your bot’s code should cover its operational basics, such as the mechanics of trading signal generation, trade execution, and stock/external data analysis.
Backtesting is vital to your bot’s comprehensive testing before its deployment. It involves the code’s audit to ensure that it works exactly as you expect it to. Backtesting is also needed to evaluate the strategy’s performance with different timeframes, asset types, and market conditions.
Now that you have a functional trading bot, you need to maximize its efficiency while reducing its inadvertent bias during adjustments. Efficiency maximization is possible by choosing a popular productivity KPI (e.g., the Sharpe ratio) that offers a reasonable risk-reward ratio and concurrence.
Bias happens when your bot follows historical data too closely, thus creating an illusion of high productivity. While the future is always unpredictable and different from the past, such a bot can fail in real market conditions.
The bias may be minimized by training your bot with big data, weeding out irrelevant input data, and simplifying the strategic model.
#7 Integration with the trading platform
All bots operate on the stock exchanges, so they should be linked to the user’s brokerage account via an API to execute trades on behalf of the user.
See the process of system integration with the crypto trading bot on the infographics below.
#8 Deployment and Use
Once everything is done and your bot is linked to a brokerage account via an API, it’s ready for use. It’s highly advisable to try the bot on the demo account first, allowing it to show how it works on real-time market data without real money (the approach is referred to as a “dry run”).
At this stage, you should check whether the bot’s productivity is on par with the one it showed during backtesting. Besides, you need to monitor your bot’s operations continuously to ensure that the market conditions it was designed for are still in place.
The Team You Need to Create a Trading Bot
You need a development team with a diverse tech stack to build a trading bot. Here’s a list of specialists you will need to hire to get that job done:
- Software engineers. These experts are responsible for the factual creation of your trading bot, so you should hire one or several developers knowing the programming languages you need (e.g., C+, Java, or Python).
- QA specialists. Trading strategy should be linked with the bot’s functionality, and that’s what a quantitative expert will do. These team members should be proficient in math and statistics, know the financial market, and understand how to translate a trading strategy into code.
- Data scientists. These experts will help you tie the bot’s functionality with external data sources by developing predictive models for all input data and determining how a bot acts using those insights.
- Cybersecurity specialists. Trading bots deal with money and link to real banking accounts, so they require bulletproof protection from hackers.
- UI/UX designers. The bot’s interface is a vital parameter of its usability, especially if it’s meant for laypersons. You should hire a UI/UX designer for an intuitive, minimalist interface design.
- BA specialists. If you build a bot for commercial use, it’s better to study the market and understand what features and functionality users will pay for.
- Project manager. This person will tie the whole team’s operations together, tracking their compliance with deadlines and reporting to you on essential progress points.
Your team may be smaller or larger than the presented list, as some experts can combine two or more functions, and some jobs may require two or more experts. The main thing to remember is that the whole team should be on the same proficiency level to work productively in synergy without failing or delaying your end product.
How Long Does the Trading Bot Development Take?
The timing matters when it comes to trading bot development. Some companies don’t want to wait for months to start trading on the market and abandon this idea before they think the process of custom bot development is too lengthy.
However, the timelines differ depending on the strategy’s complexity and the need to integrate multiple data sources. The timing is affected by the technology stack you want to use and the needed size of the team.
As a rule, custom bot creation using advanced AI and ML tools takes from a couple of months to one year. The process of bot creation involves the following steps:
- Market research and roadmap creation
- Bot’s design
- Back-end and front-end development
- The bot’s testing
- Maintenance and updates
As you can see, the process is lengthy and complex, taking much time and involving many resources. But if you’re patient and committed enough, you’re sure to receive a functional, resilient, and adaptive solution customizable to all market conditions.
How Much Does Making a Trading Bot Cost?
The budget needed for bot development is one of the most important questions for a company or individual interested in algo-trading. But things are not that simple with cost estimation, as there’s no one-size-fits-all solution for trading bots.
A trading bot’s cost depends on the project’s complexity, the size of the team you need for its creation, and the needed tech expertise.
When determining the budget for this project, think of the following elements that play a role in price formation:
- The number and complexity of the bot’s features; its expected functionality.
- The cost and availability of data sources and APIs for the bot’s real-time decision-making.
- Tech stack of the team that will work on your project.
- Team size and expertise in the trading niche.
- The time you have for the bot’s development process.
All these parameters are unique for each project, so the cost of your bot may start from $10,000+ and climb up to $100,000+ and even more. You should ensure that the project’s scope is well-understood and precisely set before you start development. Otherwise, you may incur additional costs and drop the project in the middle.
How to Create a Trading Bot with RNDpoint
RNDpoint can become your dedicated trading bot development partner, as we specialize in stock trading and investment app development and possess in-depth expertise in the trading niche.
Why choose RNDpoint for trading bot development?
- Expertise in the trading tech niche. RNDpoint has been serving clients in the trading sector since 2014 and has a portfolio of 120+ FinTech projects.
- Qualified development staff. We have 250+ experts on board with diverse trading software and algo-trading tech stack.
- Quick software deployment. Our well-organized work processes allow MVP development within 2–4 months.
- Proprietary software. RNDpoint has a set of helpful white-label solutions that save you time and costs: ProcessMIX low-code back-end platform, ABLE platform for lending businesses, and an eWallet white-label solution.
Here are the recent trading software projects we’ve finalized and are ready to share with you:
- Brokerage: Stock Trading and Investment App. A B2C stock trading and investment app extending a European commercial bank’s service coverage for its existing clients and offering brokerage services to newcomers.
- Breaking Equity: Algo-trading solution. A customizable, fully automated algo-trading solution for a U.S. FinTech startup. It comes with rich functionality and a user-friendly interface to simplify retail users’ access to stock trading opportunities.
Building a trading bot is a worthy, sustainable investment with a good ROI in the long run. As soon as you learn what it takes to create a sophisticated trading bot, you get valuable experience and a clear understanding of market operations and rules.
This way, you save time by automating your trading strategy and get extra time to study additional trading assets or have a proper rest.
After setting up your custom trading bot, all you need to do is optimize the existing trading strategies. The best in this is that you grow more proficient in the technical analysis due to the optimization of trading operations and iteration of effective steps.
Explore your business and technical capabilities with RNDpoint