nanoback 0.6.0

C++20 event-driven multi-asset backtesting engine with Python APIs for analytics, WFO/Monte Carlo validation, corporate actions, tick replay, and execution realism.

BACKTESTER (nanoback)

BACKTESTER is a C++20 event-driven multi-asset backtesting engine with Python bindings, packaged as nanoback. It focuses on realistic execution simulation, data correctness, statistical validity, and backtest-to-paper continuity.

Highlights (v0.6.0)

• Fast C++ core with Python APIs
• Bar-mode and tick-mode simulation paths
• Corporate actions support: split, dividend, spinoff, delisting
• Smart execution realism primitives:
  • multi-venue routing model (fees, volume share, fill curves)
  • signal/order/fill latency modeling + adverse selection penalties
• Derivatives primitives:
  • instrument model for equity, options, futures, FX forwards
  • option expiry settlement, futures roll events, margin liquidation path
• Research validity stack:
  • analytics (Sharpe, Sortino, CAGR, drawdown, attribution)
  • parameter sweeps and heatmaps
  • walk-forward optimization
  • Monte Carlo shuffle/block-bootstrap stress tests
• Live bridge (new in v0.6):
  • PaperBroker streams ticks and runs the same engine/risk/ledger path in realtime
  • feed adapter protocol for Alpaca/yfinance/Binance integrations
  • reconciliation hooks that can run after fill events
• Position reconciliation (new in v0.6):
  • Reconciler diffs engine vs broker positions
  • optional auto-reconcile corrective orders
  • JSONL reconciliation log for auditability

Repository Layout

• include/nanoback: C++ public headers
• cpp: core engine and Python bindings
• python/nanoback: Python API, loaders, analytics, research modules
• benchmarks: performance and regression checks
• examples: runnable usage examples
• tests: regression and functional test suite

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 Usage

Basic Bar Backtest

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),
)

print(result.summary())

Paper Trading Bridge

from datetime import datetime, timedelta, timezone
import nanoback as nb
from nanoback.paper import PaperBroker, PaperTick

class DemoFeed:
    def __init__(self, ticks):
        self._ticks = list(ticks)
    def next_tick(self, timeout_seconds=None):
        return self._ticks.pop(0) if self._ticks else None
    def fetch_positions(self):
        return {"AAA": 0.0}
    def submit_order(self, symbol, quantity_delta):
        pass

def strategy(tick, state):
    return {"AAA": 1}

broker = PaperBroker(
    symbols=["AAA"],
    strategy=strategy,
    feed=DemoFeed([PaperTick(timestamp_ns=1, symbol="AAA", price=100.0)]),
)
broker.run_until(datetime.now(timezone.utc) + timedelta(seconds=1))

Position Reconciliation

from nanoback.reconcile import Reconciler

reconciler = Reconciler(adapter=broker.feed, log_path="outputs/reconcile.jsonl", auto_reconcile=False)
records = reconciler.reconcile(broker.positions)

Data Loaders

• load_csv, load_parquet for bar data
• load_ticks_parquet for tick event replay
• load_corporate_actions_csv for corporate action ingestion
• load_yahoo_adjusted for adjusted prices + suspicious jump warning checks

Benchmarks

Latency benchmark (CI-style):

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

Stress benchmark (large shapes):

.\.venv\Scripts\python.exe benchmarks\benchmark_engine.py --mode stress --profile xlarge