With the availability of massive data feeds combined with improved technology, algorithmic trading has stirred the financial markets. It has empowered firms to rely on machines for auto-execution and explore theinvesting.online for new investment decision-making potentials.
The concept allows you to transform a trading idea into an algorithmic trading strategy through a well-defined algorithm. Backtesting the approach with historical data can confirm whether it will give good returns in real market scenarios. If you find the test result not up to your expectations, you can always ideate and pursue new strategies with proven excellent past performances.
If you wish to develop your algo, you need to know the various automated trading stages. You will find algorithmic trading platforms like QuantTerminal quite useful for the complete cycle, from strategy development to live trading.
Trading Pattern Development
Developing a trading strategy is the heart of any algorithm and identifying an idea capable of generating profit becomes a priority.
You need to know the best trading logic to help you achieve your goal, trading frequency, market domain, backtesting period and required automation tools.
Talking to experienced traders and watching daily market movements can help you find money-making patterns and strategies. You can use sophisticated trading solutions to test multiple indicators, involve one or more symbols, and see how well they work.
In the first stage, you would have decided the segment your trading logic will work most effectively; it could be forex, equity, commodity or crypto.
When you perform high-level planning for your algorithm, the trading pattern will work best in some specific conditions and scripts, based on your segment choice.
You can select the required scripts before or during the trading hours, depending on your core trading logic. The latest trading platforms allow you to design up to 18 different codes, including those for position sizing and commission.
High-Level Trading Pattern Verification
Algorithmic trading needs meticulous planning, involving a lot of time and effort. So, market experts suggest verifying the core pattern at a high level using visualization tools excel before moving on to the subsequent stages.
You can make the entire development advanced by using C# and Python to write the codes and verify the logic. Some institutional-grade research and execution platforms, like QuantTerminal, enable you to use watchlists, drawing and trading tools on multiple monitors, making verification more straightforward.
It is the most critical stage in trading model development. It is where you can test your strategy using historical market data repeatedly till the time it gets optimized to its full potential.
You need to import the necessary libraries, fetch the historical data for an instrument, write the ideated logic code, generate trading signals and review the output. While checking the returns, you can understand the algorithm optimization scope.
Backtesting on high-tech trading tools helps you optimize your pattern with various performance parameters, such as position sizing and transactions.
As you get the backtest results, identify the relevant parameters that significantly impact your strategy performance and optimize them for improved results.
Though regularly optimizing your algo parameters is a good practice, avoid overfitting of parameters. The reason being, when you go overboard, you will achieve a pattern that works well for specific situations but might go wrong if the circumstances change.
Trading Strategy Analysis in Simulation Mode
Once you are confident with your strategy and the relevant backtest results, running the scheme on a simulation model is highly important.
This stage allows your algorithm to track markets in the real essence and perform virtual trades without investing actual money. You can thus analyze if the real-time performance is on the same lines as the backtest results.
Moreover, you can ensure timely trade signal generation, removal or execution-pitfalls and minimized slippages concerning signals.
Deployment in Live Trading Environment
Deploying in a real trading environment involves several aspects, usually absent in backtesting, such as order management and assets diversification.
On a technical level, you will have to establish a connection with the broker API, pass trading orders and connect with the data API. Once done, you can access historical and real-time data using the data API connection.
The latest trading platforms allow you to deploy your strategies with any cloud provider that supports Windows Server, making this stage less complicated.
When you decide to pursue the different algorithmic trading stages, it is best to engage a highly secure trading platform. Not only do you get access to a reliable, fast and modern solution, but also 10+ terabytes of downloadable historical market data for backtesting and research.
With the future of generating alpha in fully automated trading based on quantitative analysis, empower yourself with a sophisticated algo trading tool like QuantTerminal. Rest assured, you will achieve huge assets under management, generating and executing millions of orders in years to come.