Using trading signals can help you trade in the market. In fact, following someone else’s signals can save you a lot of time and energy, but it can also teach you new trading techniques and strategies.
The hardest part is finding a reliable signal provider.
What Is a Trade Signal?
A trade signal is a trigger for the action, either to buy or sell a security or other asset, generated by analysis. That analysis can be human-generated using technical indicators, or it can be generated using mathematical algorithms based on market action, possibly in combination with other market factors such as economic indicators.
What if someone else than a human can learn from this data, someone who is much better suited for processing massive and somewhat boring structured data? An algorithm? A “machine”?
The push for AI-based trading signals continues to see traction around the retail industry. AI-based trading signals are a trending offering, with many brokers looking to integrate this within their services suite.
Simultaneously, trading signals are received from machine learning algorithmic models and have a specific form (entry price, stop loss, take profit). These signals follow optimized risk management practices making them ideally useful to help the traders optimize their risk/reward ratio.
AI Trading Signals base their data on:
- Price action
- Currency valuation
- Social Media/News analysis
- Technical Analysis
- Commitments of Traders (sentiment)
- Seasonality
- Structural changes in the futures curve
At Atama.AI, we use more than 160 technical indicators and trained and back-tested millions of times. Atama.ai predicts where the market will go and can take day trading out of your hands.
We use Machine Learning in the next approaches:
1. Strategy optimization
Each indicator has several parameters. You can try to grid-search through all the parameters you might have and use them that work best on historical data or use a random set of parameters. The problem is, the second approach never works, and this approach becomes computationally impossible if you have more than just a few parameters.
2. Indicators based on Machine Learning
Machine learning techniques can be used to:
1. Learn the indicators eventually from the unstructured data, using, e.g., single layer or deep neural networks.
2. Create the indicators using advanced Machine Learning methods, as when you use Natural Language Processing techniques for sentiment analysis of social networks and media.
3. Signals extraction
In trading, technical analysis indicators are popular. There are hundreds of these indicators. They are built on years of R&D in time series processing as well as on years of experience of day traders. You can find them performed in most trading software.
Signals give you the playground. We take care of the data flow, so you will not make stupid mistakes.
Our research is built on the intersection of technical analysis, deep learning, and algorithmic trading. Our goal is to predict changes in the market direction given its current state.
Multi deep learning it’s our approach that allows for training of millions of neural networks each day and evaluating them as trading algorithms. To achieve this we have built plenty of systems in addition to Tensorflow:
- systems to get and prepare data
- system to automatically change settings and run experiments
- trading simulation system
- backtesting system
- experiment analytics system
- stop-loss optimization system and others…
The most significant advantage of using AI trading signals is the time it can save. Suppose you manually analyze the market, identify directions, and recognize trends. In that case, it is a very time-consuming task, and by using AI trading signals, you’re saving a lot of time.
You don’t have to have immense expertise in trading. By following the AI trading signals, you can take the signals and input them into your trading account, providing hands-free trading.
You can use AI trading signals in your daily trading. It’s much easier than you think.
Artificial Intelligence is much better at predicting price trends than a human. By being able to analyze alternative data and using machine learning, it can generate improved trading signals. Information can be collected from various sources such as social media, news, financial news, transactions, and blogs.
