quantcore 0.1.8.6

High-performance C++20 backtesting engine with Python interface

QuantCore

High-performance backtesting engine for trading strategies, written in C++20 with a Python research interface.

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Overview

QuantCore is an event-driven backtester built around an enhanced version of my limit order book simulator. It processes market events chronologically through a priority queue, ensuring no look-ahead bias and no unrealistic assumptions about fill prices.

The C++ core handles all the performance-critical work: event dispatch, order matching, position tracking, and execution simulation. Python sits on top via pybind11 bindings and handles strategy development, parameter optimization, and visualization.

Market Data → EventQueue → Strategy → Signal → OrderBook → Fill → Portfolio

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Quick Start

import quantcore as qc

class MyStrategy(qc.Strategy):
    def on_data(self, event):
        if not self.has_position(event.symbol):
            self.generate_signal(event.symbol, qc.SignalType.BUY, 1.0, event.timestamp_ns)

results = qc.run_backtest(
    strategy=MyStrategy(),
    data={'AAPL': qc.load_csv_data('AAPL', 'data/aapl.csv')},
    initial_capital=100_000.0,
)
print(results)

The engine handles fills, position tracking, and PnL automatically. For a full tearsheet:

from quantcore.analytics import calculate_all_metrics, calculate_returns
from quantcore.plotting import plot_full_tearsheet
import numpy as np

equity = np.array(results['equity_curve'])
returns = calculate_returns(equity)
print(calculate_all_metrics(equity))
plot_full_tearsheet(equity, returns)

For the full API reference and usage guide, see docs/usage.md.

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Architecture

Every action goes through the event queue. When a strategy calls generate_signal, that signal becomes an OrderEvent, which goes through the order book, produces a FillEvent, which updates the portfolio. All in timestamp order. This is what prevents look-ahead bias: the strategy never sees data from the future.

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Strategy Development

Python Strategy

Subclass qc.Strategy and implement on_data. Signals drive order execution. You don't place orders directly, you generate signals and the engine handles the rest.

class BollingerBreakout(qc.Strategy):
    def __init__(self, window=20, n_std=2.0):
        super().__init__("BollingerBreakout")
        self.window = window
        self.n_std  = n_std
        self.prices = []

    def on_data(self, event):
        self.prices.append(event.close)
        if len(self.prices) < self.window:
            return

        window_prices = self.prices[-self.window:]
        mean = sum(window_prices) / self.window
        std  = (sum((p - mean) ** 2 for p in window_prices) / self.window) ** 0.5

        upper = mean + self.n_std * std
        lower = mean - self.n_std * std
        pos   = self.get_position(event.symbol)

        if event.close > upper and pos <= 0:
            self.generate_signal(event.symbol, qc.SignalType.BUY,  1.0, event.timestamp_ns)
        elif event.close < lower and pos >= 0:
            self.generate_signal(event.symbol, qc.SignalType.SELL, 1.0, event.timestamp_ns)

    def on_fill(self, fill):
        pass  # optional: react to fills

Portfolio Context

Strategies can access full portfolio state:

def on_data(self, event):
    portfolio = self.get_portfolio()
    if portfolio:
        equity     = portfolio.get_portfolio_value()
        cash       = portfolio.get_cash()
        position   = portfolio.get_position(event.symbol)

Position Sizing

The engine ships with several sizing methods:

from quantcore import FixedPercentage, RiskBased, KellyCriterion

engine = qc.BacktestEngine(100_000.0)
engine.set_position_sizer(qc.FixedPercentage(0.10))  # 10% of capital per trade

Built-in sizers: FixedPercentage, RiskBased, KellyCriterion, EqualWeight, VolatilityTargeting, FixedShares.

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Execution Simulation

Order Types

GOOD_TILL_CANCEL, IMMEDIATE_OR_CANCEL, FILL_OR_KILL, MARKET, GOOD_FOR_DAY

Fees & Slippage

from quantcore import ExecutionConfig

config = ExecutionConfig()
config.maker_fee     = 0.001   # 0.1% maker
config.taker_fee     = 0.002   # 0.2% taker
config.slippage_pct  = 0.0005  # 0.05% slippage
config.latency_ns    = 1_000_000  # 1ms order latency

engine = qc.BacktestEngine(100_000.0, config)

Risk Management

from quantcore import RiskLimits

limits = qc.RiskLimits()
limits.max_position_pct = 0.20   # max 20% per position
limits.max_leverage     = 2.0
limits.max_loss_pct     = 0.15   # halt at 15% drawdown

engine.set_risk_limits(limits)

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Performance

Single-threaded. Measured on Windows (Release build, MSVC). Full results in benchmarks/RESULTS.md.

Order book

Pattern	Ops/s
Add + cancel (market-maker quote refresh)	13.0 M ops/s
Add + match (taker sweep)	4.9 M ops/s

These are raw order book operations with no engine overhead. The matching engine is not the bottleneck at daily-bar scale.

End-to-end backtest

Scenario	Bars/s	Latency (p99)
1-year (252 bars)	~270 K bars/s	0.93 ms
5-year (1,260 bars)	~270 K bars/s	-
1,000-year stress (252,000 bars)	~290 K bars/s	-

Throughput is stable across dataset sizes. A 1-year daily backtest completes in under 1 ms at p99.

Run the benchmarks yourself:

cmake --build build --target bench_backtest_engine
./build/bench_backtest_engine

python benchmarks/bench_python.py

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Analytics

After running a backtest, the results dict contains an equity curve and trade log you can feed straight into the analytics module.

from quantcore.analytics import calculate_all_metrics, calculate_returns

equity  = np.array(results['equity_curve'])
returns = calculate_returns(equity)
metrics = calculate_all_metrics(equity)

print(metrics)
# Total Return:     24.31%
# Annualized:       11.82%
# Sharpe Ratio:     1.43
# Sortino Ratio:    2.01
# Max Drawdown:     -8.74%
# Win Rate:         58.3%

Available metrics: total return, CAGR, Sharpe, Sortino, Calmar, max drawdown, drawdown duration, win rate, profit factor, avg win/loss, largest win/loss.

Visualizations

from quantcore.plotting import (
    plot_full_tearsheet,
    plot_equity_curve,
    plot_underwater,
    plot_returns_distribution,
    plot_rolling_metrics,
    plot_monthly_returns_heatmap,
)

plot_full_tearsheet(equity, returns, timestamps=ts)

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Example Notebooks

Notebook	Strategy	Concepts
mean_reversion.ipynb	Z-score mean reversion	Parameter sensitivity, OU process
sma_crossover.ipynb	SMA crossover	Trend following, signal generation
pairs_trading.ipynb	Statistical arbitrage	Cointegration, spread trading
build_your_own_strategy.ipynb	Bollinger Band Breakout	Full walkthrough from scratch

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Installation

Prerequisites

• CMake 3.15+
• C++20 compiler (GCC 10+, Clang 12+, MSVC 2022)
• Python 3.8+
• pybind11 (pip install pybind11)

Build

git clone https://github.com/SLMolenaar/quantcore.git
cd quantcore

# build the C++ core
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build

# build the Python bindings
cd python
pip install pybind11
python build_module.py

# verify
python -c "import quantcore; print(quantcore.version())"

Run Tests

cmake --build build --target quantcore_tests
./build/quantcore_tests

For the full Python API reference, see docs/usage.md.

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Project Structure

quantcore/
├── cpp/
│   ├── backtesting/          # Engine, events, portfolio
│   ├── strategies/           # C++ strategy implementations
│   ├── orderbook/            # Order book (from orderbook-simulator-cpp)
│   └── tests/                # GoogleTest suite
├── python/
│   ├── quantcore/            # Python package
│   │   ├── __init__.py       # Public API
│   │   ├── analytics.py      # Performance metrics
│   │   └── plotting.py       # Visualizations
│   ├── bindings.cpp          # pybind11 bindings
│   └── build_module.py       # Build helper
├── examples/                 # Jupyter notebooks
├── benchmarks/               # Benchmark suite
├── CMakeLists.txt
└── README.md

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vs. Alternatives

	QuantCore	Backtrader	Zipline
Core language	C++20	Python	Python
Order book simulation	✅ Real LOB	❌	❌
Event-driven	✅	✅	✅
Look-ahead prevention	✅ Priority queue	✅	✅
Python strategy API	✅ pybind11	✅ native	✅ native
Throughput (bars/s)	~270 K	~50 K	~100 K
Maintenance	Active	Stale	Inactive

The main differentiator is the order book. Backtrader and Zipline assume you fill at the bar's close price. QuantCore routes orders through a real price-time priority matching engine, which gives you realistic partial fills, spread simulation, and tick-level execution when you have tick data.

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Contributing

See CONTRIBUTING.md. Open areas if you want to dig in:

• Stop / Stop-Limit orders: order type enum and matching engine
• VWAP / TWAP algos: ExecutionEngine, child order slicing
• Tick data pipeline: the engine is bar-agnostic internally; the data loader needs extending
• Trading calendar: holiday/early-close filtering before bars hit the engine
• Multi-strategy portfolio: shared capital across strategies with a meta-allocator
• Parallel sweeps on Linux: n_jobs exists but Windows spawn overhead kills it; a Linux worker pool would make it actually useful

The engine doesn't handle corporate actions, survivorship bias, or timezone normalization. That's the data layer's job. Feed it clean adjusted data and none of those are problems.

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License

MIT: see LICENSE.