bt_api_risk

Intelligent risk management and compliance monitoring system for 73+ exchanges. Provides real-time risk assessment, ML-driven anomaly detection, and regulatory compliance support.

Features

Core Risk Management

• RiskManager — Central risk management orchestrator for pre-trade, in-trade, and post-trade risk controls
• RiskAssessor — Real-time risk scoring and limit enforcement

Machine Learning Models

• RiskEnsembleModel — Ensemble model combining Random Forest, Neural Network, and XGBoost for risk prediction
• AnomalyDetector — Behavioral pattern recognition and outlier detection for suspicious trading activity

Risk Containers

• RiskEvent — Structured event type for risk-triggered incidents
• RiskEventType — Enum of event types (POSITION_LIMIT, MARGIN_CALL, etc.)
• RiskLevel — Enum of risk levels (LOW, MEDIUM, HIGH, CRITICAL)
• RiskMetrics — Container for aggregated risk metrics

Compliance Standards

• Market Manipulation Detection (spoofing, layering, front-running)
• Anti-Money Laundering (AML) and Know Your Customer (KYC)
• MiFID II and SEC Rule 606 trade reporting
• Position limits and concentration risk limits
• Credit risk and margin management

Installation

pip install bt_api_risk

Requirements

• Python >= 3.9
• numpy >= 1.26.0
• pydantic >= 2.0.0

Quick Start

frombt_api_riskimportRiskManager, RiskAssessor, RiskLevel, RiskEvent, RiskEventType# Initialize the risk managerrisk_mgr=RiskManager()
# Create a risk assessmentassessor=RiskAssessor()
risk_score=assessor.assess_position(
exchange="BINANCE___SPOT",
symbol="BTCUSDT",
position_value=100000.0,
account_equity=500000.0,
)
print(f"Risk level: {risk_score.level}") # RiskLevel.MEDIUM# Emit a risk eventevent=RiskEvent(
event_type=RiskEventType.POSITION_LIMIT,
level=RiskLevel.HIGH,
exchange="BINANCE___SPOT",
symbol="BTCUSDT",
message="Position exceeds single-symbol limit",
)
risk_mgr.emit(event)

Anomaly Detection

frombt_api_riskimportAnomalyDetectordetector=AnomalyDetector(threshold=0.8)
is_anomalous=detector.is_anomalous(
features={
"trade_frequency": 150,
"order_size_ratio": 0.95,
"price_impact": 0.12,
}
)

ML Ensemble Risk Model

frombt_api_riskimportRiskEnsembleModelmodel=RiskEnsembleModel()
risk_prob=model.predict_risk(
features={
"position_concentration": 0.75,
"leverage_ratio": 2.5,
"daily_volatility": 0.04,
"liquidity_score": 0.6,
}
)
print(f"Risk probability: {risk_prob:.3f}")

API Reference

Class	Description
RiskManager	Central orchestrator for risk controls
RiskAssessor	Real-time risk scoring engine
RiskEnsembleModel	ML ensemble for risk prediction
AnomalyDetector	Behavioral anomaly detection
RiskEvent	Risk incident event container
RiskEventType	Enum of risk event types
RiskLevel	Enum of risk severity levels
RiskMetrics	Aggregated risk metrics container

Compliance Standards

Standard	Description
MiFID II	EU markets instrument directive
SEC Rule 606	US trade reporting
AML/KYC	Anti-money laundering / Know your customer
Basel III	Banking capital requirements
MAR	EU market abuse regulation

Documentation

Full documentation available at bt_api_py documentation.

License

MIT License