RiskSim

Interactive risk management simulator for traders and portfolio managers.

RiskSim helps you understand how trading parameters and asset correlations affect portfolio outcomes. It uses Monte Carlo simulation and correlated Brownian motion to model thousands of scenarios, giving you a realistic picture of expected returns, drawdowns, and diversification benefits.

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Features

Risk-Return Analysis

Explore how win rate, trade frequency, position sizing, and reward-to-risk ratio interact to produce your expected P&L.

• Monte Carlo engine — runs up to 100,000 simulations per parameter set
• RPUR sweep — bar chart of average return across 16 Return Per Unit Risk levels (0.5x to 8x)
• Win-rate sweep — bar chart of average return across win rates (28% to 70%)
• Drawdown estimation — expected and worst-case consecutive-loss drawdown
• Live metrics — trades/year, win rate, risk/trade, RPUR, expected drawdown, max drawdown

Parameter	Range	Default
Trades per year	5 – 100	30
Win rate	28% – 70%	40%
Risk per trade	0.25% – 5%	1%
Return per unit risk	0.5x – 8x	3x
Simulations	10,000 – 100,000	10,000

Asset Correlation Simulator

Generate synthetic multi-asset portfolios and see how correlation structure drives risk and return.

• Flexible correlation — set a single uniform correlation or a random range (min/max)
• Configurable assets — 1 to 50 assets, 1 to 10 years of simulated prices
• Randomization — optionally vary mean returns and volatilities across assets
• Performance metrics — Sharpe, Sortino, Calmar ratios, annualized return/volatility, max drawdown
• Correlation sweep — compares portfolio performance across correlation levels from -1.0 to +1.0
• Heatmap — visualize the realized correlation matrix

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Screenshots

The app fully supports both light and dark themes.

Welcome Page

Light mode	Dark mode

Risk-Return Analysis

RPUR sweep — light mode	RPUR sweep — dark mode
Win-rate sweep — light mode	Win-rate sweep — dark mode

Asset Correlation Simulator

Synthetic asset price paths — light mode	Synthetic asset price paths — dark mode
Correlation matrix heatmap — light mode	Correlation matrix heatmap — dark mode
Correlation sweep — light mode	Correlation sweep — dark mode

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Getting Started

Prerequisites

• Python 3.10+

Install

git clone https://github.com/chrisduvillard/RiskSim.git
cd RiskSim
python -m venv .venv

# Activate the virtual environment
# Windows:
.venv\Scripts\activate
# macOS / Linux:
source .venv/bin/activate

pip install -r requirements.txt

Run

streamlit run Welcome.py

The app opens in your browser. Use the sidebar to navigate between pages, adjust parameters, and click Run Simulation.

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Development

Run tests

pip install pytest
pytest -v

Lint

pip install ruff
ruff check .

CI

GitHub Actions runs both ruff check . and pytest on every push and PR against main (Python 3.10 and 3.12).

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How It Works

Risk-Return Analysis — Monte Carlo Trading Simulation

The simulator models a year of trading as a sequence of independent bets:

1. Each trade risks a fixed percentage of current AUM (compounding, not flat sizing).
2. Wins return risk_amount * RPUR; losses lose risk_amount.
3. Trade outcomes are shuffled randomly each simulation to capture path dependency.
4. The process repeats thousands of times to build a distribution of final portfolio values.
5. Drawdown is estimated by counting the longest streak of consecutive losses across simulations.

This reveals the non-obvious interaction between win rate and reward-to-risk: a 35% win rate with a 4:1 payoff can outperform a 55% win rate with a 1.5:1 payoff.

Asset Correlation — Correlated Geometric Brownian Motion

The portfolio simulator generates realistic multi-asset price paths:

1. Correlation matrix — either uniform (all pairs share the same correlation) or random within a user-specified range. Non-positive-definite matrices are projected to the nearest valid matrix.
2. Covariance matrix — constructed from per-asset volatilities and the correlation matrix (S @ C @ S).
3. Log-returns — sampled from a multivariate normal distribution with drift adjustment (mu - 0.5 * sigma^2) to ensure prices follow geometric Brownian motion.
4. Metrics — computed from the resulting price paths: annualized return, volatility, max drawdown, and risk-adjusted ratios (Sharpe, Sortino, Calmar).
5. Correlation sweep — the entire simulation is repeated across correlation levels from -1.0 to +1.0 to illustrate the diversification benefit.

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Project Structure

RiskSim/
├── Welcome.py                          # Streamlit entrypoint
├── pages/
│   ├── 1_🎯_risk_return_analysis.py    # Risk-return Monte Carlo page
│   └── 2_📈_asset_correlation.py       # Correlation portfolio page
├── config/
│   └── slider_configs.py               # Centralized slider defaults
├── utils/
│   ├── risk_simulation.py              # TradingSimulator + drawdown estimation
│   └── style.py                        # Footer and metric box helpers
├── tests/
│   ├── test_risk_simulation.py         # Simulation logic tests
│   └── test_style.py                   # Style helper tests
├── .github/workflows/ci.yml            # CI pipeline
├── pyproject.toml                      # Ruff + pytest config
├── requirements.txt                    # Dependencies
├── CHANGELOG.md
├── LICENSE.txt                         # MIT
└── README.md

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Tech Stack

Layer	Tool
App framework	Streamlit
Visualization	Plotly
Numerical engine	NumPy + Pandas
Styling	htbuilder
Linting	Ruff
Testing	pytest
CI	GitHub Actions

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Contributing

Contributions are welcome. Fork the repo, create a branch, and open a pull request. Please make sure ruff check . and pytest pass before submitting.

License

MIT — see LICENSE.txt.

Author

Built by Christophe Duvillard