Programmatic risk simulations and optimizers
Configure simulations to match your risk framework. Generate scenario-based probability estimates and parameter recommendations.
Estimate the probabilities that matter
Supported instruments:
- Lending positions (overcollateralized and undercollateralized)
- Perpetual futures and leveraged trading
- Any programmable financial instrument with margin requirements
Outputs generated:
- Probability of liquidation over custom time horizons
- Margin call likelihood and timing
- Underwater position risk (LTV exceeds 100%)
- Partial liquidation scenarios
- Asset correlation matrices
- Value at Risk (VaR) at multiple confidence levels
Optimization capabilities:
Determine margin levels, collateral ratios, or position sizes required to hit target risk thresholds (e.g., keep default probability below 2% over 30 days).
Choose the model that matches your assumptions
Modular and flexible architecture lets you select the model that fits your risk framework:
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Geometric Brownian Motion (GBM)
A simple price-path model that adds optional volatility shocks. Useful as a quick reality check for risk assumptions.
GARCH
captures volatility clustering and time varying risk parameters.
Historical simulation
Replays actual market data to capture how prices and volatility evolved in real conditions.
Agent-Based Models (ABM)
Simulates the most consequential risk scenarios by modeling behavior at the agent level rather than hard-wiring a distribution or extrapolating past returns, capturing non-linear interactions, cascading effects, and feedback loops.
Bring your own data or use Bitpulse’s Data Layer as inputs.
From position to parameters in four steps
Step 1
Define the position or portfolio (asset, size, entry price, margin requirements)
Step 2
Select risk model and parameters (volatility, correlation, time horizon)
Step 3
Run Monte Carlo simulation (thousands of price paths calculated)
Step 4
Receive probability distribution and parameter recommendations
Response includes default probability, VaR estimates, and suggested margin to meet specified risk tolerance.
request = {
"type": "GBM",
"params": {
"collateral_weights": {
"PEPE": 0.2,
"SOL": 0.8
},
"loan_weights": {
"BTC": 0.3,
"USDC": 0.7
},
"N": 1.7,
"M": 1.5,
"L": 1.4,
"R": 1.7,
"analysis_date": "11-28-2024",
"model_lookback": 720.0, // in days
"loan_duration": 30.0, // in days
"mc_top_up": 24.0, // margin call top up time in hours
"loan_value": 1000000,
"mc_iter": 25000 // monte carlo iterations
}
}
response = { "Output": {
"AvgLossPct": 0.0,
"AvgSurvivalRate": 1.0,
"Collateral Volatility": 0.03837350504388694,
"Collateral/Loan Correlation": 0.4289787072331317,
"Loan Volatility": 0.016597796754734623,
"PrCloseout": 0.0,
"PrLiquidation": 0.03604,
"PrMarginCall": 0.30121,
"PrUnderwater": 0.0,
"WorstCaseCCR": 1.2478372708096614
},
"Risk engine version": "1.6.2"
}
Estimate the probabilities that matter
Lender setting collateral requirements
Goal: Determine minimum collateral ratio for ETH-backed loan that keeps default probability below 1% over 90 days.
Optimizer returns: 150% collateral ratio required under GARCH volatility assumptions.
Desk managing leveraged position
Goal: Find maximum leverage for BTC position that maintains liquidation probability below 5% over 7 days.
Optimizer returns: 4x leverage maximum given current volatility regime and liquidity depth.
Portfolio Risk Analysis
Goal: Assess how your portfolio responds to different volatility shock scenarios and continuously monitor key risk indicators.
Under a stress scenario where market volatility doubles, the portfolio’s risk metrics show significant sensitivity. The probability of a margin call rises to 45%, while the probability of liquidation increases to 6%, crossing the 5% risk threshold.
Embed risk intelligence in your platform
Lending platforms get borrower-level insights
Maple Finance explores API integration to show borrower-level risk metrics and recommended margin adjustments inside their dashboard.
Prime brokers power position optimization
Membrane Labs integrates the Risk Suite API for loan optimization and position management within its UI.
Risk teams run batch simulations
Asset managers and desks query APIs to run batch simulations across portfolios and generate risk reports for committees.
Current deployments
Start an evaluation
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