QHASH AI
Forging The Native AI Model
Step 1
Raw Data Creation
The process starts with creating raw data, which involves gathering comprehensive and varied information from different cryptocurrency markets. Price swings, trade volumes, market sentiment, and other pertinent variables are all included in this raw data. Main data sources involve Taapi.io, Kraken, CMC, etc.
Step 2
Data Analysis
After analyzing the data, QHASH AI identifies the most relevant patterns and correlations that significantly impact cryptocurrency forecasts. By carefully selecting the most important variables, the model's predictive power is enhanced during training.
Step 3
Crucial Data Selection
After identifying the important data points, an organized dataset is created, serving as the basis for training the AI model and allowing it to absorb knowledge from relevant, high-quality data.
Step 4
Training & Testing
Training and testing scripts are powering the automated assessment of model performance and streamlining the process.
Step 5
Performance Evaluation
Evaluating and analyzing each machine learning model to select those with the highest prediction accuracy.
Step 6
Maximizing Accuracy
Following the identification of the top prospects, a rigorous testing and training program is initiated, repeating the process multiple times to maximize results.
Step 7
Real-Time Testing
The trained model is tested in real-time scenarios, generating forecasts using up-to-date market data. During this phase, the AI adapts to current market trends and provides valuable insights essential for making confident transaction decisions.
Step 8
Autonomous AI Learning
The AI model system has an automatic live dataset updating mechanism enabled. The AI’s ability to evolve autonomously allows it to fine-tune its forecasts and adapt to changing market conditions, significantly enhancing its accuracy in predicting future cryptocurrency movements and market trends.