AIZEN AI Price Prediction Model Tech Brief

Executive Overview

AIZEN represents a breakthrough in cryptocurrency trading technology, developed by two PhD researchers with NASDAQ high-frequency trading experience. Key differentiators include:

Infrastructure

• Google Cloud (GCP) powered infrastructure

• Daily retraining on NVIDIA GPUs

• Continuous model updates to prevent drift

Cognitive Processing

• Simultaneous analysis of multiple variables

• Surpasses human cognitive limitations

• Integrated technical indicator analysis

Model Architecture

• Built on transformer-type deep learning

• Purpose-built for market price prediction

• Not adapted from generic algorithms

Understanding Temporal Fusion Transformers

Explanation

A Temporal Fusion Transformer (TFT) analyzes cryptocurrency price movements across different time intervals simultaneously. Unlike traditional trading systems that analyze each time period separately. Also, TFT identifies complex relationships across different timeframes and feature types (various technical indicators). Specifically, TFT looks at patterns across 1-minute, 15-minute, 60-minute and 240-minute intervals together. This enables it to detect complex relationships that simpler systems miss. This is no off the shelf algorithm, it’s trained using NVIDIA GPUs on an extensive 1 minute market data from Binance and it learns more every single day!

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Traditional technical algo vs. TFT Approach

Traditional systems rely on:

• Fixed moving averages

• RSI thresholds

• Volatility measures

• Volume analysis

TFT advantages:

• Multi-timeframe analysis (1, 15, 60, 240 minutes)

• Return-based predictions instead of direct price

• Handles non-stationary behavior

• Standardized measure across cryptocurrencies

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TFT vs Large Language Models (e.g., ChatGPT)

Purpose

• TFT: Specifically designed for time series prediction, focusing on numerical patterns across different timeframes

• LLM: Designed for natural language processing and generation

Data Structure

• TFT: Processes structured numerical data with fixed dimensions and time intervals

• LLM: Processes unstructured text data of variable length

Prediction vs. Generation

• TFT: Makes specific numerical predictions (e.g., Return of Close Price) based on historical patterns

• LLM: Generates contextual responses based on probability distributions of language patterns

Time Understanding

• TFT: Explicit understanding of time-series relationships and intervals

• LLM: No inherent understanding of time series or numerical patterns

Application

• TFT: Quantitative forecasting for specific variables

• LLM: General-purpose language tasks and reasoning

Stefan Ojanen