AI Trading with Level2 Ticks:
How to Beat the Market
Backtesting is an essential method for validating the functionality of a new trading strategy—without risking real money in live trading. Even established strategies should be reviewed regularly and adjusted to new market conditions based on the results from market replays.
However, a critical mistake is often made in this process
The tests are conducted on aggregated or incomplete datasets, or over too short a time period. This leads to distorted results and can cause significant losses in real trading.
Precision
Only by backtesting with reliable historical tick data over many years can distortions caused by overfitting, slippage, and spreads be reliably ruled out.
Diligence
Which strategies are tested is up to the user. However, anyone seeking meaningful results should allocate enough time for thorough testing and experiment with various techniques and combinations. Those who do not approach backtesting with the utmost diligence are likely to face difficulties in live trading as well.
The AI Hype – Those Who Don’t Act Now Will Be Left Behind
With the development of increasingly powerful AI models — especially those specialized in time series analysis such as Recurrent Neural Networks (RNNs) — new opportunities are emerging in algorithmic trading. Optimizing a trading strategy through AI promises significantly better results than traditional static methods.
AI enables the processing, analysis, and evaluation of far larger volumes of data than was previously possible. It is therefore only logical to incorporate the untapped potential of Level 2 data into the analysis, gaining a decisive edge over the majority of traders.
Level 2 Tick Data – A Must for Precise Analysis
Tick data from at least the past 5 years is required, including precise timestamps as well as Level 1 and Level 2 information. The data format must be clearly structured (CSV, JSON, XML) to ensure seamless integration with common backtesting software.
Additionally, Level 1 data or candlestick data is also available — ideal for getting started without AI support.
What follows is a brief introduction to the topic
What is backtesting?
Backtesting in stock trading is the process of testing a trading strategy against historical market data to assess its effectiveness and profitability. The purpose of backtesting is to verify how a trading strategy would have worked in the past under real market conditions before applying it in practice. The goal is to optimize the strategy to generate a higher profit.
Overall, backtesting is an important tool in trading to test hypotheses and build confidence in a strategy before investing real capital. In general, the more granular and detailed the data, the better the result!
Static Strategy
Statistical model trading uses methods and algorithms to analyze financial markets and make trading decisions. These models rely on historical price data, market indicators, and statistical techniques to identify patterns and make predictions. Commonly used methods include time series analysis, regression models, and static rules. The goal is to respond to market changes more quickly and efficiently than using pure “gut feeling.” Automating trading decisions can reduce trading costs, shorten response time, and eliminate human error, making trading more efficient and cost-effective.
Improvement through AI models
Machine learning (ML) offers numerous advantages in stock trading. It enables the analysis of large amounts of data and the detection of complex patterns that are often not captured by traditional statistical methods. ML algorithms can continuously improve by learning from new market data and adjusting their predictions, resulting in better performance and accuracy. They also enable the development of flexible and adaptive trading strategies that respond to rapidly changing market conditions.
Training of AI models
Training an artificial intelligence takes place in several steps, depending on the type of model and the method used. In general, the training process can be divided into the following phases:
- Data preparation: First, large amounts of normalized data are needed to train the AI.
- Training: During training, the AI learns to recognize patterns in the data. This is done by applying algorithms that compare the input data with the expected outputs (goals).
- Evaluation: To ensure that the AI works well, the model is tested with a separate dataset that was not used during training (test data).
- Continuous learning: After training, the AI can be deployed in a real-world environment. However, in many cases, the model will continue to be fed with new data to improve its performance and adapt to changes in the data.
