Event-Driven Backtesting Engine

Event-driven backtesting engine implemented in two languages with a unified Streamlit interface.

DataHandler  →  MarketEvent
Strategy     →  SignalEvent
Portfolio    →  OrderEvent
Broker       →  FillEvent  →  Portfolio::update()

Structure

backtest/
├── core/                   ← C++17 engine (performance-critical)
│   ├── include/
│   │   ├── events.hpp      # std::variant<MarketEvent, SignalEvent, OrderEvent, FillEvent>
│   │   ├── bar.hpp         # OHLCV struct
│   │   ├── data_handler.hpp
│   │   ├── strategy.hpp    # Strategy base + SMACrossStrategy
│   │   ├── portfolio.hpp
│   │   ├── broker.hpp      # fill at open, 0.1% flat commission
│   │   ├── performance.hpp # Sharpe, CAGR, Max DD, Calmar
│   │   └── export.hpp      # CSV export
│   └── src/
│       └── main.cpp
│
├── strategies/             ← Python engine (reference implementation)
│   ├── events.py
│   ├── data_handler.py
│   ├── strategy.py
│   ├── portfolio.py
│   ├── broker.py
│   ├── performance.py
│   └── runner.py
│
├── app.py                  ← Streamlit, single entry point
├── scripts/
│   └── download_data.py
├── CMakeLists.txt
├── Justfile
└── pyproject.toml

Option 1 : Run on host directly

1. Download data

just download AAPL,MSFT
# or: python scripts/download_data.py AAPL MSFT --start 2015-01-01 --end 2024-01-01

2. Build C++ core

just build
# or: cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j4

3. Launch the UI (you could just run this and skip step 1 and 2)

just app
# or: streamlit run app.py

Select the engine in the sidebar: Python strategies (runs inline) or C++ core (subprocess + CSV).

Full workflow in one command

just all AAPL,MSFT

Option 2 : Use Docker

docker compose up --build

Open http://localhost:8501. The data/ and results/ directories are mounted as local volumes — data and results persist across container restarts.

Engines

	Python strategies/	C++ core/
Dispatch	queue.Queue + if/elif	std::queue + std::visit
Events	dataclasses	std::variant
Data	yfinance live	pre-downloaded CSV
Output	inline Streamlit	results/*.csv
Use case	rapid prototyping	long-period backtest

Metrics

Total Return / CAGR / Sharpe Ratio / Max Drawdown / Annualised Volatility / Calmar Ratio — all compared against a configurable benchmark (default: SPY).