Not any trading software
Developed using Machine Learning and Deep Learning algorithms
We are a crypto-native software provider
Based on Efficient Market Hypothesis (EMH), prices will quickly adjust to reflect all relevant information.
Some would argue that markets are not always efficient and prices would deviate from their fair value for extended periods of time.
There are gaps in the data that we could process.
Machine Learning and Deep Learning techniques are based on Artificial Neural Networks that can analyze complex and large amounts of data, identify patterns and make predictions based on past trends.
These techniques can continue to improve over time as they are exposed to more data, allowing them to adapt and become more accurate over time.
Data and Data processing are critical components of Machine Learning and Deep Learning algorithms.
The quality and quantity of data can have a significant impact on the performance of the algorithms.
The data needs to be cleaned and split into training and testing sets before feeding into the quant model.
1. Understanding Financial Data and Cryptocurrency Market
2. Reading relevant Machine Learning and Deep Learning academic papers
3. Running trading algorithm analyses
4. Analyzing the results
5. Data Preprocessing
6. Designing the Machine Learning Model
8. Repeat the process where necessary
How does our trading
Using Machine Learning and Deep Learning techniques for efficiency and to eliminate biases.
The trained model looks at as many on-chain and off-chain data as possible and it assigns weights for every data input on its own in order to make predictions or decisions based on new input data.
Prediction models are very specific and currently, our trading software is fine-tuned for cryptocurrencies.
Our trading software will tell you when to enter and exit a trade and how much to trade.
It is designed for building Machine Learning models and it is particularly well-suited for building Deep Learning models which are Neural Networks with multiple layers that can learn complex patterns in data, including support for Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
One of the key features of TensorFlow is its ability to perform computations using dataflow graphs. These graphs allow users to define the computation that a model should perform using nodes and edges, which represent mathematical operations and data, respectively.
This allows TensorFlow to perform efficient and highly parallelized computations, making it well-suited for building large-scale Machine Learning models.
The particular focus of XGBoost is on gradient boosting which involves building an ensemble of weak models and combining them to create a stronger model.
One of the key benefits of XGBoost is its ability to handle large datasets and perform fast training and prediction. It is designed to be efficient and scalable, and it can handle missing data and handle data with a large number of features.
It is typically used for exploring, visualizing statistical relationships in data and visualizing more complex data structures, such as time series and multivariate data.
It has a wide range of tools and algorithms for scientific and technical computing and statistical analysis, such as functions for estimating probability distributions, performing hypothesis tests, and fitting statistical models.
One of the key advantages of Scikit-learn is it allows users to easily swap between different models and algorithms. It has a range of tools for preprocessing and transforming data, making it easy to prepare data for Machine Learning.
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