4. The World's First Trustworthy AI Prediction Model
The Trustworthy AI Prediction Model is the flagship product trained and powered by the manadia AI-Native Compute Coordination Network. It demonstrates how the manadia network integrates distributed compute resources, real-time data, AI Agents, strategy systems, and trustworthy execution into a unified framework capable of delivering verifiable predictions and strategy execution.
4.1 Product Positioning
The manadia AI Prediction Model is positioned as the world's first Trustworthy AI Prediction Model. Here, "trustworthy" does not imply that the model is always correct or that any level of financial return can be guaranteed. Rather, it means that the model's inputs, versions, execution environment, strategy logic, result records, and audit trails are built upon a technical foundation that is traceable, verifiable, and reproducible.
The model is trained and operated by the manadia AI-Native Compute Coordination Network. The compute network provides data processing, feature computation, model training, real-time inference, strategy backtesting, and Agent execution capabilities. The prediction model serves as the network's flagship product, demonstrating how the underlying compute coordination infrastructure can be applied to high-value real-world scenarios.
4.2 Model Architecture
The Trustworthy AI Prediction Model consists of seven core modules:
Data Layer
-> Feature Engine
-> Prediction Model
-> Strategy Engine
-> Risk Engine
-> Execution Agent
-> Trust & Audit Layer
The Data Layer is responsible for ingesting multi-source data. The Feature Engine transforms raw data into features that can be understood by the model. The Prediction Model identifies market conditions and estimates future probabilities. The Strategy Engine selects the optimal strategy portfolio. The Risk Engine manages position sizing, leverage, and drawdown constraints. The Execution Agent executes actions and adjusts strategy parameters. The Trust & Audit Layer records model versions, data sources, execution environments, and execution results.
4.3 Multi-Source Data Inputs
The quality of an AI prediction model depends heavily on its data. Relying solely on price data can easily lead to overfitting or incorrect predictions. Therefore, manadia adopts a multi-source data architecture that includes:
CEX market data
CEX order book depth
Trading volume and aggressive buy/sell flow
Perpetual funding rates
DEX on-chain liquidity
AMM pool states
On-chain address behavior
Prediction market probabilities
News events
Social sentiment
Macroeconomic indicators
Project fundamental data
Oracle data
Among these, CEX data provides a high-frequency liquidity perspective, DEX data provides insights into on-chain capital flows, prediction market data reflects collective market expectations, news and sentiment data capture event-driven signals, while on-chain data reveals capital movement and behavioral patterns.
4.4 Data Pipeline and Real-Time Caching
The Trustworthy AI Prediction Model requires a low-latency, multi-market, continuously updated data pipeline. A typical data flow is structured as follows:
CEX WebSocket
DEX WebSocket
Prediction Market WebSocket
Oracle Feeds
News & Sentiment Feeds
-> Normalization
-> Redis / Time-series Storage
-> Feature Engine
-> Prediction & Strategy Engine
WebSockets are responsible for real-time data collection. The Normalization layer standardizes incoming data, while Redis or a time-series database provides low-latency caching and short-term state management. The Feature Engine retrieves data from the real-time cache to generate model features.
This architecture enables the model to simultaneously process high-frequency market prices, order book changes, prediction market probability shifts, and on-chain events, while preserving structured historical data for future replay and auditing.
4.5 Feature Engineering
The Feature Engine supports hybrid computation of both real-time and offline features, covering hundreds of factors including momentum, volatility, funding rates, order book imbalance, on-chain capital flows, sentiment intensity, and prediction market probability deviations. By leveraging time-series databases and distributed caching, the system enables low-latency feature retrieval, ensuring millisecond-level inference even during periods of extreme market volatility.
Rather than directly reading raw prices and predicting market direction, the AI Prediction Model constructs a comprehensive representation of market conditions through feature engineering.
Common features include:
Price momentum features
Volatility features
Trading volume change features
Order book depth features
Buy/sell pressure features
Funding rate features
Basis spread features
On-chain capital flow features
Address behavior features
Prediction market probability change features
News event intensity features
Social sentiment features
Cross-market spread features
Liquidity shock features
Through these multi-factor features, the model can recognize different market conditions. For example, trending markets, ranging markets, event-driven markets, liquidity droughts, extreme funding rate environments, and converging prediction market probabilities all require different trading strategies.
Example:
Raw Data:
{
timestamp: 1234567890,
bid_price: 45230.5,
ask_price: 45231.2,
bid_volume: 5.2,
ask_volume: 4.8,
last_trade_price: 45230.8,
last_trade_volume: 0.5
}
Computed Features:
# Market Microstructure Features
spread = ask_price - bid_price = 0.7
mid_price = (bid_price + ask_price) / 2 = 45230.85
imbalance = (bid_volume - ask_volume) / (bid_volume + ask_volume) = 0.04
vwap_5min = ... (5-minute Volume Weighted Average Price)
realized_vol_1h = ... (1-hour realized volatility)
# Trend Features
momentum_5min = price_now - price_5min_ago
momentum_1h = price_now - price_1h_ago
trend_strength = ... (trend strength indicator)
# On-chain Features
whale_activity_24h = Number of large-value transfers
liquidity_score_dex = Current DEX liquidity vs. historical average
large_transfer_alert = Presence of abnormal large transfers
# Sentiment Features
social_sentiment_score = ... (-1 to 1)
news_sentiment = ... (-1 to 1)
twitter_volume_zscore = ... (number of standard deviations)
These features are then fed into the model, allowing it to learn that certain combinations of features typically indicate upward price movement, while others tend to signal downward price movement.
4.6 Model Training and Evaluation
The training process of the manadia AI Prediction Model may include:
Historical data cleansing
Feature generation
Sample labeling
Training and test set splitting
Rolling-window training
Out-of-sample testing
Multi-strategy backtesting
Parameter stability testing
Stress testing under extreme market conditions
Live trading simulation
Evaluation metrics should not focus solely on returns, but should also include:
Monthly performance
Maximum drawdown
Sharpe Ratio
Calmar Ratio
Win rate
Profit-loss ratio
Volatility
Slippage impact
Transaction fee impact
Capital utilization
Strategy capacity
Out-of-sample performance
Performance under extreme market conditions
During testing under specific market cycles and strategy combinations, the manadia AI Prediction Model achieved strategy performance exceeding 50% monthly returns. This metric should be interpreted as a demonstration of the model's ability to capture Alpha under specific conditions, rather than as a guarantee of fixed returns. This technical white paper consistently distinguishes between historical strategy performance, model capability validation, and future performance guarantees.
4.7 Prediction and Strategy Inference
The output of the Trustworthy AI Prediction Model is not simply a bullish or bearish prediction, but a set of structured decision signals, including:
Market state
Trend direction
Volatility expectations
Risk level
Signal confidence
Strategy recommendations
Position recommendations
Stop-loss conditions
Market-making parameters
Hedging parameters
Execution priority
The model dynamically selects strategy combinations based on different market conditions. For example, it may employ grid strategies during ranging markets, funding rate strategies during periods of extreme funding rates, volatility strategies during event-driven markets, and arbitrage or hedging strategies when cross-market spreads widen.
Model Inference and Decision Flow:
4.8 Strategy Execution System
The strategy framework of the manadia AI Prediction Model may include:
Grid strategies
Funding rate strategies
Hedging strategies
Market-making strategies
Cross-market arbitrage strategies
Volatility strategies
Trend-following strategies
Event-driven strategies
End-of-session strategies
AI Agent dynamic portfolio rebalancing strategies
The strategy execution system must be tightly integrated with the risk management system. No strategy can operate independently of position limits, maximum drawdown constraints, leverage limits, liquidity constraints, or abnormal market suspension conditions.
A simplified execution workflow can be expressed as:
observe market_state
generate prediction_signal
select strategy_set
check risk_constraints
calculate position_size
execute action
record trace
evaluate result
update strategy weights
4.9 Risk Management System
AI prediction models must explicitly acknowledge market risk. The professionalism of Trustworthy AI lies not in avoiding risk, but in structuring, parameterizing, and making risk fully auditable.
The risk management system should include:
Per-strategy position limits
Total position limits
Single-market exposure limits
Cross-market exposure limits
Maximum leverage limits
Maximum drawdown limits
Daily loss limits
Volatility circuit breakers
Data anomaly circuit breakers
Suspension under insufficient liquidity
Slippage protection
Strategy degradation mechanisms
These risk parameters should be associated with specific model versions and strategy configurations, making them available for review within the auditing system.
4.10 TEE Trusted Execution
Trusted Execution Environment (TEE) is a critical component of the Trustworthy AI Prediction Model. It provides hardware-level isolated execution environments that protect model weights, strategy parameters, core logic, and sensitive data.
TEE is responsible for generating Execution Proofs, anchoring key information—including model hashes, data digests, execution timestamps, and output fingerprints—onto the blockchain. Auditors can verify execution authenticity without accessing the model in plaintext, thereby establishing a trustworthy execution framework in which code cannot be tampered with, execution cannot be forged, and results can be independently verified.
Within the system, TEE serves three primary purposes.
First, it protects model developers. The parameters, strategy logic, and training outcomes of prediction models are valuable intellectual property. TEE reduces the risk of unauthorized copying or theft during execution.
Second, it protects data providers. Certain high-value data sources may contain proprietary information or sensitive factors. TEE minimizes the exposure of such data in plaintext during execution.
Third, it enhances execution trustworthiness. By recording model versions, input digests, execution environments, and outputs, the system establishes a reliable foundation for subsequent auditing and replay.
TEE does not guarantee prediction accuracy, nor does it eliminate market risk. Its role is to ensure a trustworthy execution environment, protect models and data, and provide verifiable execution records.
4.11 Three Pillars of Trust
Why Trace Execution?
Suppose an AI Prediction Model makes the following prediction:
"BTC will increase by 3% within the next hour."
The prediction is correct, and users profit → The result is accepted.
The prediction is incorrect, and users incur losses → Questions arise: Was the model incorrect? Was the data flawed? Were the parameters wrong? Or did the implementation fail?
Without execution tracing, these questions cannot be answered.
With execution tracing, we can:
Replay all input data used at the time to verify whether the model inputs were correct.
Determine which model version was used and review its historical performance.
Inspect the constraints enforced by the risk management module.
Verify whether execution suffered from latency or slippage.
Ultimately determine whether the issue originated from the model itself or from the implementation.
What Is Traced?
Component
Tracked Information
Data Pipeline
Timestamp, value, and version of every sampled data source
Feature Engineering
Computation time and source data used for every feature
Model Inference
Model version, parameters, inputs, outputs, and confidence score
Risk Management
Current positions, VaR, drawdown, and triggered constraints
Strategy Selection
Why a strategy was selected and which alternatives were evaluated
Execution
Invoked tools, input parameters, and returned results
Results
Execution price, slippage, and final P&L
manadia structures the trustworthiness of its AI Prediction Model into three layers.
Verifiable Model Performance. Model releases, version updates, backtesting results, risk metrics, and parameter configurations are fully recorded. Users and auditors can review the evolution and historical performance of the model rather than relying solely on published results.
Verifiable Real-World Data. Every data source used by the model includes provenance records, timestamps, signatures, or smart contract addresses where applicable. Critical data sources are further supported by reputation scoring and falsification mechanisms.
Reproducible Execution Results. Given a specific model version, input data, and strategy configuration, key execution processes can be replayed and independently verified. Users are not required to access proprietary strategy details, but they can verify that the execution process has not been arbitrarily altered.
4.12 Model Visualization and User Interface
A Trustworthy AI Prediction Model must be understandable through its user interface. The manadia AI User interface may present the following information:
AI Agent list
Strategy type
Runtime status
Signal confidence
30-day PNL
Asset growth
Latency
Risk mode
Model hash
Configuration contract address
Model version
Data sources
Real-time background activities
PNL history
Factors and data sources
Rather than simply displaying returns, this interface is designed to show how the model operates, which data it depends on, what risk state it is currently in, and whether its execution process is fully traceable.
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