nanoback 0.1.1

C++20 event-driven trading backtester with Python bindings, snapshots, audit ledger, and microstructure-aware execution.

BACKTESTER

BACKTESTER is a low-latency, event-driven trading backtesting repo built around a C++20 execution engine and exposed through a Python package named nanoback.

What It Does

• Multi-asset backtests over contiguous matrix inputs
• Market and limit order simulation
• Partial fills, queue blocking, venue caps, and bid/ask-aware execution
• Parent/child order IDs with deterministic audit ledger output
• Snapshot/resume support for long-running simulations
• Risk controls for leverage, drawdown, cash, and per-asset limits
• Financing and borrow-cost accrual
• Compiled research policies and analytics in C++
• Python strategy/plugin fallback
• CSV and Parquet loaders
• Asserted benchmarks and pytest coverage

Repo Layout

• include/nanoback: C++ headers
• cpp: C++ engine, policies, and Python bindings
• python/nanoback: Python API, loaders, ledger utilities, and strategy helpers
• examples: runnable examples
• benchmarks: performance checks
• tests: regression and functional coverage

Quickstart

cd C:\Users\TAPESH\documents\BACKTESTEER
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -e .[dev]
python -m pytest

Core Capabilities

Execution Engine

• Event-driven C++20 core
• Market and limit orders
• Child-order slicing with delay steps
• Cancel/replace semantics
• Session-aware order cancellation
• Snapshot/resume from engine state

Market Realism

• Bid/ask execution path
• Queue-ahead fraction and venue volume share caps
• Slippage and participation-based impact
• Cost calibration helpers from empirical fill data

Risk and Audit

• Gross leverage checks
• Drawdown kill switch
• Cash and borrow accounting
• Per-asset max position and notional limits
• Deterministic ledger export to CSV/JSONL

Research Layer

• Compiled momentum, mean reversion, and moving-average crossover policies
• Compiled rolling volatility, cross-sectional ranking, and minimum-variance weights
• Python strategy hooks when custom event logic is needed

Minimal Example

import numpy as np
import nanoback as nb

result = nb.run_backtest(
    timestamps=np.array([1, 2, 3, 4], dtype=np.int64),
    prices=np.array([100.0, 101.0, 99.0, 102.0], dtype=np.float64),
    signals=np.array([1, 1, 0, -1], dtype=np.int64),
    config=nb.BacktestConfig(
        max_position=2,
        child_order_size=1,
        child_slice_delay_steps=1,
    ),
)

print(result.pnl)
print(len(result.ledger))

Benchmark

.\.venv\Scripts\python.exe benchmarks\benchmark_engine.py --max-seconds 0.50 --min-fills 1000

Status

The repo is strong as a research and simulation engine. It is not a full OMS/EMS, exchange adapter stack, or compliance platform.