A comprehensive suite of seven standalone Python tools for high-performance quantitative simulation and financial risk modeling.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install quant-sim-toolkit
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install quant-sim-toolkit using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Quant Simulation Toolkit?
The Quant Simulation Toolkit is a collection of professional-grade Python scripts designed to implement advanced quantitative techniques used by top-tier trading desks. This set of Openclaw Skills enables developers to move beyond basic models by providing robust implementations of importance sampling, particle filters, and agent-based market simulations. Every tool is self-contained and derived from industry-proven methodologies for pricing, risk assessment, and market microstructure analysis.
By utilizing these Openclaw Skills, users can perform complex simulations like pricing binary options against Black-Scholes benchmarks or modeling non-stationary hidden probabilities in real-time. The toolkit is optimized for efficiency, requiring only numpy and scipy, making it an ideal resource for quants, data scientists, and developers building financial agents.
Quant Simulation Toolkit Use Cases
- Pricing binary (digital) contracts with 95% confidence intervals and calibration via Brier scores.
- Estimating rare tail risk and crash probabilities (P < 0.001) using variance-reducing importance sampling.
- Tracking hidden states in non-stationary environments, such as live election results or sports betting markets.
- Reducing Monte Carlo variance by 100-500x through stacked antithetic, control, and stratified sampling.
- Stress testing portfolios for correlated defaults using Gaussian, Student-t, and Clayton copulas.
- Modeling information asymmetry and price discovery in prediction markets using agent-based microstructures.
How Quant Simulation Toolkit Works
- Identify the specific quantitative challenge, such as option pricing, risk estimation, or real-time probability tracking.
- Configure the standalone Python tool with the required financial parameters like strike price, volatility, or time-to-expiry.
- Run the simulation engine which applies specialized stochastic processes (e.g., Geometric Brownian Motion) or Bayesian updates.
- Apply variance reduction techniques or importance sampling to optimize the simulation's computational efficiency.
- Analyze the generated report, which includes statistical benchmarks, error rates, and formatted performance tables.
- Use the integrated pipeline to chain multiple Openclaw Skills together for an end-to-end quantitative research workflow.
Quant Simulation Toolkit Setup
To begin using these Openclaw Skills, ensure you have a standard Python 3 environment. No complex package managers are required beyond the core scientific stack.
git clone <repository-url>
cd quant-sim-toolkit
pip install numpy scipy
You can then run individual simulations or the full suite:
# Run the complete end-to-end pipeline
python3 pipeline.py
# Run a specific tool (e.g., Tail Risk estimation)
python3 tail_risk.py
Quant Simulation Toolkit Data Schema & Taxonomy
The toolkit organizes its data through high-performance numpy arrays and structured console outputs. The data organization includes:
| Component | Format | Description |
|---|---|---|
| Simulation Paths | Numpy ndarray | Stores stochastic trajectories for assets and agents. |
| Probabilities | Float | Estimates of win/loss probabilities with confidence intervals. |
| Agent Log | Class Objects | Tracks PnL and trade history for Informed, Noise, and Market Maker agents. |
| Metrics Table | Console String | Formatted summary of Brier scores, MAE, and Variance Reduction factors. |
| Copula Marginals | Matrix | Correlated outcomes generated via Gaussian, Student-t, or Clayton transformations. |
Quant Simulation Toolkit Advanced Features
- Exponential tilting in importance sampling to shift distributions toward rare crash regions for stable estimation.
- Sequential Importance Resampling (SIR) in particle filters to prevent particle depletion via low-variance systematic resampling.
- Kyle lambda estimation using OLS to recover price-impact coefficients from simulated market flows.
- Stackable variance reduction that combines antithetic and stratified sampling with control variates for maximum precision.
- Asymmetric lower-tail dependence modeling via Clayton copulas to accurately simulate market contagion effects.
SKILL.md
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