quantcore 0.2.4

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

Both bar data and tick data are supported. The engine is bar-agnostic internally - ticks become MarketDataEvent objects like bars do, so strategies work unchanged across both data types.

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Quick Start

Bar data

# pip install quantcore
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('data/aapl.csv', 'AAPL')},
    initial_capital=100_000.0,
)
print(results)

Tick data

results = qc.run_tick_backtest(
    strategy=MyStrategy(),
    tick_data={'AAPL': qc.load_tick_csv('data/aapl_ticks.csv', 'AAPL')},
    initial_capital=100_000.0,
    mm_refresh_interval_ns=1_000_000_000,   # refresh MM quotes once per second
    equity_snapshot_interval_ns=60_000_000_000,  # snapshot equity once per minute
)
print(results)

The same strategy class works for both. event.close is the tick price when running on tick data.

You can also aggregate ticks to bars before running:

ticks = qc.load_tick_csv('data/aapl_ticks.csv', 'AAPL')
bars  = qc.aggregate_ticks_to_bars(ticks, bar_duration_ns=60_000_000_000)  # 1-minute bars

results = qc.run_backtest(strategy=MyStrategy(), data={'AAPL': bars}, initial_capital=100_000.0)

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. The same strategy works for bar and tick data - event.close is the close price for bars and the trade price for ticks.

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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Tick Data

Loading

# from CSV - columns: timestamp, price, quantity  (or with side: B/S/buy/sell)
ticks = qc.load_tick_csv('data/aapl_ticks.csv', 'AAPL')

# from Parquet
ticks = qc.load_tick_parquet('data/aapl_ticks.parquet', 'AAPL')

# numpy fast path - (N, 4) array: [timestamp_ns, price, quantity, side]
# side: 0.0 = Buy, 1.0 = Sell
arr = qc.load_tick_parquet('data/aapl_ticks.parquet', use_numpy=True)
engine.add_tick_data('AAPL', arr)

Data Normalization

QuantCore does not adjust raw prices for corporate actions internally. Feed it adjusted data, where close prices are continuous across splits and dividends, for correct results.

Most data vendors offer adjusted data:

• Yahoo Finance: use Adj Close instead of Close
• Polygon.io: use the adjusted=true parameter
• CRSP: adjusted by default
• Quandl/NASDAQ Data Link: WIKI/PRICES table uses adjusted prices

If you have raw unadjusted data, use CorporateActionsAdjuster:

from quantcore import CorporateActionsAdjuster

adjuster = CorporateActionsAdjuster.from_csv(
    splits_csv='data/aapl_splits.csv',
    dividends_csv='data/aapl_dividends.csv',
)
raw_bars    = qc.load_csv_data('data/aapl_raw.csv', 'AAPL')
adj_bars    = adjuster.adjust(raw_bars)
results     = qc.run_backtest(strategy=MyStrategy(), data={'AAPL': adj_bars}, ...)

Aggregation

# aggregate to any bar duration
bars_1min  = qc.aggregate_ticks_to_bars(ticks, bar_duration_ns=60_000_000_000)
bars_1hour = qc.aggregate_ticks_to_bars(ticks, bar_duration_ns=3_600_000_000_000)
bars_1day  = qc.aggregate_ticks_to_bars(ticks, bar_duration_ns=86_400_000_000_000)

Engine configuration for tick data

Two settings matter most for tick performance:

engine = qc.BacktestEngine(100_000.0)
engine.add_tick_data('AAPL', ticks)

# How often the market maker refreshes its quotes.
# 0 = every tick (default, slowest). 1s interval gives ~5x speedup on 1-second tick data.
engine.set_mm_refresh_interval(1_000_000_000)       # 1 second

# How often the equity curve is snapshotted.
# 0 = every tick (default). Has negligible performance impact but keeps the
# equity curve manageable for large tick datasets.
engine.set_equity_snapshot_interval(60_000_000_000) # 1 minute

run_tick_backtest() sets both to sensible defaults (1s and 60s respectively).

CSV format

timestamp,price,quantity
1700000000,150.25,100
1700000001,150.30,50

# with aggressor side
timestamp,price,quantity,side
1700000000,150.25,100,buy
1700000001,150.30,50,sell

Timestamps in seconds, milliseconds, microseconds, or nanoseconds - detected automatically.

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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

End-to-end backtest - bar mode

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	-

End-to-end backtest - tick mode

Scenario	Ticks/s	Wall time
10K ticks, no MM throttle	315 K/s	31.7 ms
10K ticks, 1s MM throttle	1.66 M/s	6.0 ms
10K ticks, 10s MM throttle	2.78 M/s	3.6 ms
1M ticks → 1-min bars (aggregation)	247 M/s	4.1 ms

The market-maker refresh interval is the main performance lever for tick data. With a 1-second throttle the engine processes 1-second tick data at ~5x the unthrottled rate. See benchmarks/RESULTS.md for the full breakdown.

Run the benchmarks yourself:

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

python benchmarks/bench_python.py
python benchmarks/bench_tick_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
│   │   ├── tick_data.h       # TickData struct and TickSeries
│   │   └── tick_data_loader.h# CSV loader and aggregate_to_bars
│   ├── 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
│   │   └── tick_parquet_loader.py  # Parquet loader for tick data
│   ├── bindings.cpp          # pybind11 bindings
│   └── build_module.py       # Build helper
├── examples/                 # Jupyter notebooks
├── benchmarks/               # Benchmark suite
│   ├── bench_backtest_engine.cpp
│   ├── bench_tick_data.cpp
│   ├── bench_python.py
│   ├── bench_tick_python.py
│   └── RESULTS.md
├── CMakeLists.txt
└── README.md

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vs. Alternatives

	QuantCore	Backtrader	Zipline
Core language	C++20	Python	Python
Order book simulation	✅ Real LOB	❌	❌
Tick data support	✅ Native	❌	❌
Event-driven	✅	✅	✅
Look-ahead prevention	✅ Priority queue	✅	✅
Python strategy API	✅ pybind11	✅ native	✅ native
Throughput (bars/s)	~300K	~7.7K	unverified
Maintenance	Active	Stale	Inactive

Throughput measured on SMA(50/200) crossover, 50K daily bars, Release build, Windows. Reproduce: python benchmarks/qcVsBacktrader.py --bars 50000 --runs 20

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
• 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. Feed it clean adjusted data and none of those are problems.

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License

MIT: see LICENSE.