Machine learning for financial risk management
A research-grade tool that analyzes Solidity smart contracts for economic vulnerabilities such as unbounded minting, toxic fee mechanisms, liquidity traps, oracle manipulation, centralized control, and broken financial invariants. Focused on economic correctness, incentive risks, and DeFi system stability.
Testing Code abount quantitative finance algorithms
Stress Testing Financial Portfolios using S&P 500 Stock Data from Kaggle.
Independent public-data framework for FICC Treasury-clearing liquidity stress testing and model validation using Federal Reserve data.
Economic applications of the SymC framework. Applies χ ≈ 1 stability principles to market microstructure, distinguishing governed systems (HFT-stabilized) from ungoverned systems (selection-driven). Demonstrates framework universality in human adaptive systems.Retry
Reconstructing a Complete DAO Lending Lifecycle
A modular Python engine for banking book ALM, integrating IRRBB, liquidity risk (LCR/NSFR), stress testing, and treasury management actions.
Interest rate sensitivity and liquidity stress test model built in Excel to analyze the impact of parallel rate shocks on net interest income and cash position. The model applies scenario analysis with clearly defined assumptions to provide a transparent framework for understanding interest rate and liquidity risk exposure.
Python + Plotly Dash analytics dashboard for trading floor liquidity, funding, and collateral monitoring. 5,500+ synthetic positions. Docker-ready.
Counterparty Credit Risk (CVA/PFE) & Regulatory Liquidity (LCR/NSFR) Engine (Python, R, PostgreSQL, Power BI, Excel)
Liquidity Management Tools calibration workflow.
Temporal liquidity risk simulation demonstrating threshold failure under synchronised demand
Portfolio and order risk analytics for research, backtesting, and paper trading
Treasury decision deck for FX exposure, liquidity monitoring, and scenario-aware finance workflows.
Zero-key terminal dashboard for LiquiLens bank and lender early warning: India and US boards, JSON, cron alerts, and offline cache.
A quantitative risk‑modelling toolkit for Lombard lending, providing volatility models, liquidity and concentration adjustments, stress utilities, and a unified haircut/LTV evaluation pipeline.
Simulation-based EMI risk analytics system analyzing how liquidity stress distorts fraud signal interpretation under statistical detection models (SPI, CSI, Z-score).
Selected fund risk workflow examples using simulated UCITS and AIFMD-style data, covering liquidity, leverage and LMT mechanics.
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