wbt 0.1.2

Weight-based backtesting engine for quantitative trading

wbt

High-performance position-weighted backtesting engine, written in Rust with Python bindings.

中文文档

Overview

wbt (Weight Back Test) is a standalone library for backtesting trading strategies based on position weights. It computes daily P&L attribution, trade pair matching (FIFO), and comprehensive performance statistics — all powered by a zero-copy, cache-friendly Rust core with rayon-based parallelism.

• Rust crate (wbt): pure Rust library with core engine
• Python package (wbt): PyO3 bindings + pandas-friendly API via Arrow IPC

Installation

Python

# Requires: Rust toolchain, maturin, Python >= 3.9
git clone <repo-url> && cd wbt
uv venv .venv && source .venv/bin/activate
uv pip install pandas pyarrow numpy maturin
maturin develop --release

Rust

# Cargo.toml
[dependencies]
wbt = "0.1"

Quick Start

Python

import pandas as pd
from wbt import WeightBacktest

# Prepare input: DataFrame with columns [dt, symbol, weight, price]
dfw = pd.DataFrame({
    "dt":     ["2024-01-02 09:01:00", "2024-01-02 09:02:00", "2024-01-03 09:01:00", "2024-01-03 09:02:00"],
    "symbol": ["AAPL", "AAPL", "AAPL", "AAPL"],
    "weight": [0.5, 0.5, 0.0, 0.0],
    "price":  [185.0, 186.0, 187.0, 185.5],
})

wb = WeightBacktest(dfw, digits=2, fee_rate=0.0002, n_jobs=4, weight_type="ts")

wb.stats          # dict: Sharpe, Calmar, max drawdown, win rate, etc.
wb.daily_return   # DataFrame: per-symbol daily returns with 'total' column
wb.dailys         # DataFrame: per-symbol daily detail (edge, return, cost, turnover, ...)
wb.alpha          # DataFrame: strategy excess return vs. benchmark
wb.pairs          # DataFrame: all matched trade pairs (FIFO)

Rust

use wbt::core::{WeightBacktest, WeightType};
use polars::prelude::*;

let dfw: DataFrame = /* build your DataFrame with [dt, symbol, weight, price] */;
let mut wb = WeightBacktest::new(dfw, 2, Some(0.0002))?;
wb.backtest(Some(4), WeightType::TS, 252)?;

let report = wb.report.as_ref().unwrap();
println!("Sharpe: {}", report.stats.daily_performance.sharpe_ratio);

Input Format

Column	Type	Description
dt	datetime	Bar end time; must be a continuous trading time series with no gaps
symbol	str	Instrument code
weight	float	Position weight at bar end; independent across symbols; positive = long, negative = short, 0 = flat
price	float	Trade price (close, next-bar open, TWAP/VWAP, etc.)

Parameters

Parameter	Default	Description
digits	2	Decimal places for weight rounding
fee_rate	0.0002	One-way transaction cost (commission + slippage)
n_jobs	1	Number of parallel threads (rayon thread pool)
weight_type	"ts"	"ts" (time-series: equal-weight average across symbols) or "cs" (cross-section: sum across symbols)
yearly_days	252	Trading days per year for annualization

Output Properties

Property	Type	Description
stats	dict	Full performance report (Sharpe, Calmar, drawdown, win rate, trade metrics, ...)
daily_return	DataFrame	Pivoted daily returns per symbol + total column
dailys	DataFrame	Per-symbol daily detail: n1b, edge, return, cost, turnover, long/short splits
alpha	DataFrame	Strategy vs. benchmark excess return
alpha_stats	dict	Performance statistics on the excess return series
pairs	DataFrame	All FIFO-matched trade pairs with P&L, hold bars, direction
long_daily_return	DataFrame	Long-only daily returns
short_daily_return	DataFrame	Short-only daily returns
long_stats	dict	Long-only performance statistics
short_stats	dict	Short-only performance statistics
bench_stats	dict	Benchmark (equal-weight n1b) performance statistics

Architecture

wbt/
├── src/
│   ├── lib.rs              # PyO3 bindings (Arrow IPC in/out)
│   └── core/               # Pure Rust engine
│       ├── mod.rs           # WeightBacktest struct & public API
│       ├── native_engine.rs # Zero-copy parallel engine (rayon)
│       ├── daily_performance.rs # Sharpe, Calmar, drawdown, etc.
│       ├── evaluate_pairs.rs    # Trade pair statistics
│       ├── backtest.rs      # Orchestration & alpha computation
│       ├── report.rs        # Report structs & JSON serialization
│       ├── trade_dir.rs     # Trade direction & action enums
│       ├── errors.rs        # Error types
│       └── utils.rs         # WeightType, rounding, quantile
└── python/wbt/         # Python API wrapper
    ├── backtest.py     # WeightBacktest class (pandas-friendly)
    └── _df_convert.py  # Arrow <-> pandas conversion

Benchmark

Tested on China A-share daily data (2017-01 to 2025-04), Apple M-series, 8 threads, release build with LTO:

Dataset	Rows	Symbols	Time	Throughput
Full A-share daily	8,440,404	5,351	0.63s	13.4M rows/s

Outputs produced in a single pass:

Output	Size	Description
dailys	8,435,053 rows × 15 cols	Per-symbol daily attribution
pairs	942,679 rows × 11 cols	FIFO-matched trade pairs
daily_return	2,023 rows	Equal-weight portfolio daily return
alpha	2,023 rows × 4 cols	Strategy vs. benchmark excess return
stats	29 metrics	Sharpe, Calmar, drawdown, win rate, etc.

Sample stats output (click to expand)

{
  "开始日期": "2016-12-27",       "结束日期": "2025-04-28",
  "绝对收益": 0.4431,             "年化": 0.0552,
  "夏普": 0.345,                  "最大回撤": 0.2234,
  "卡玛": 0.2471,                 "日胜率": 0.5136,
  "日盈亏比": 1.0219,             "日赢面": 0.0384,
  "年化波动率": 0.16,              "下行波动率": 0.1167,
  "非零覆盖": 0.9802,             "盈亏平衡点": 0.9975,
  "新高间隔": 542.0,              "新高占比": 0.0277,
  "回撤风险": 1.3963,             "回归年度回报率": 0.0395,
  "长度调整平均最大回撤": 0.9516,   "交易胜率": 0.2697,
  "单笔收益": 17.51,              "持仓K线数": 9.79,
  "多头占比": 0.4611,             "空头占比": 0.5269,
  "与基准相关性": -0.2835,         "与基准空头相关性": -0.374,
  "波动比": 0.6665,               "与基准波动相关性": 0.1974,
  "品种数量": 5351
}

Performance Design

• O(N) counting sort for symbol grouping (replaces generic sort)
• Struct-of-Arrays (SoA) layout for cache-locality in hot loops
• Lazy DataFrame materialization — internal computation uses raw vectors; DataFrames built only on demand
• FIFO lot matching via stack-based SoA with dynamic resizing
• rayon data-parallelism across symbols with configurable thread pool
• Zero-copy slicing from Polars DataFrames into contiguous memory blocks
• Arrow IPC for zero-serialization-overhead Python <-> Rust data transfer

License

MIT