crypto-rs-backtester 0.1.2

crypto-rs-backtester

Video

Your Crypto Backtest Is Lying to You - Fix It with Rust | crypto-rs-backtester

Japanese version: see README.ja.md.

A tick-level, high-precision backtester powered by Rust × Python (Polars), designed for researchers. WIP.

• Goal: Combine Python's agility with Rust's deterministic, high-performance simulation to eliminate performance bottlenecks, look-ahead bias, and poor reproducibility.
• Scope: Mainly crypto spot/futures (CEX). Validates microstructure such as multi-exchange, latency, and queue position.

---

Key Highlights

• Performant by design: Rust event-driven core with fixed-point arithmetic; Python focuses on research interface.
• Zero-copy pipeline: Use Polars (Arrow) in Python and hand off to Rust with minimal copying.
• Microstructure fidelity: Latency modeling, L2/L3 queue logic, and realistic race conditions (e.g., PendingCancel).
• Determinism first: Seeded RNGs, stable tie-breaking for identical timestamps.

See docs/SPEC.md for details.

Features (current state)

• Hybrid architecture: Rust core (event-driven, fixed-point i64) + Python strategy interface.
• Separated timelines: ts_exchange (ground truth) / ts_local (what the strategy observes) / ts_sim (total order of all events).
• Look-ahead prevention: Strategies only see MarketView after feed latency. Order arrival and ACK are strictly ordered on ts_sim.
• Execution modes: Tick mode (on_tick) and Batch mode (on_ticks / on_order_updates).
• Data ingestion: via Polars LazyFrame (minimal copying), or zero-copy via Arrow C Stream (run_arrow).
• Determinism: Fixed RNG seed, stable tie-breakers for simultaneous events, lexicographic assignment of symbol IDs.

See docs/SPEC.md for details.

---

Repository Structure

• backtester-core/: Rust simulation core (src/*.rs, tests/, benches/)
• backtester-py/: PyO3 wrapper exposing the core to Python
• python/: Python package rust_backtester/ and tests in python/tests/
• docs/: Specs and plans (SPEC.md, PLAN.md, etc.)
• Root Cargo.toml: Rust workspace, pyproject.toml: maturin build

---

Installation

pip install crypto-rs-backtester

---

Install & Build (dev)

Prerequisites: Python 3.9+ / Rust toolchain / maturin

# Virtualenv
python -m venv .venv && source .venv/bin/activate

# Dev install (builds Rust extension)
pip install -e .[dev]

# Alternative: direct build
maturin develop

---

Quickstart (Python)

Required columns: ts_exchange:Int64, price:Int64, qty:Int64, side:Int8 Recommended: seq:Int64 (stable order for same-timestamp), ts_local:Int64 (if missing, applies ts_exchange + feed_latency_ns) Aliases: ts_event ≈ ts_exchange, size ≈ qty

import polars as pl
from rust_backtester import Backtester

# Tiny deterministic dataset (1e-8 fixed point: 100.0 => 100_00000000)
lf = pl.DataFrame({
    "ts_exchange": [1_000, 2_000, 3_000, 4_000],
    "price": [100_00000000, 101_00000000, 99_00000000, 100_00000000],
    "qty":   [  1_00000000,   1_00000000,  1_00000000,   1_00000000],
    "side":  [            1,           -1,           1,           -1],
    "seq": list(range(4)),
}).lazy()

class MyStrategy:
    def on_tick(self, tick: dict, ctx):
        # Example: place a passive order using the received tick
        ctx.submit_order(
            symbol_id=int(tick["symbol_id"]),
            side=1,  # 1=Buy, -1=Sell
            price=int(tick["price"]),
            qty=1_00000000,
        )

bt = Backtester(
    data={"binance:BTC/USDT": lf},
    seed=42,
    python_mode="tick",    # or "batch"
    batch_ms=100,
    feed_latency_ns=1_000,  # applies ts_local = ts_exchange + 1_000 (ns)
)

result = bt.run(MyStrategy())
print(result.stats())
print(result.trades())

Batch mode (higher throughput)

class MyBatch:
    def on_ticks(self, ticks: list[dict], ctx):
        for t in ticks:
            ctx.submit_order(symbol_id=t["symbol_id"], side=1, price=t["price"], qty=1_00000000)

bt = Backtester(data={"binance:BTC/USDT": lf}, seed=42, python_mode="batch", batch_ms=50)
res = bt.run(MyBatch())

Arrow zero-copy path (for large datasets)

# Pass a PyArrow RecordBatchReader implementing __arrow_c_stream__
res = bt.run_arrow(stream=rb_reader, strategy=MyBatch())

---

Build, Test, Benchmarks

• Python tests: pytest -q
  • Benchmarks only: pytest -m bench -q
• Rust build: cargo build -p backtester-core
• Rust tests: cargo test -p backtester-core
• Rust benches: cargo bench -p backtester-core

Note: python/tests/conftest.py auto-runs maturin develop if the extension isn't installed.

---

Benchmark Configuration (Practical Conditions)

Criterion benches in Rust can be tuned via environment variables (defaults: 4 symbols × 250k ticks each):

• BACKTEST_BENCH_NSYMBOLS (default: 4)
• BACKTEST_BENCH_TICKS_PER_SYMBOL (default: 250000)
• BACKTEST_BENCH_DT_NS tick spacing in ns (default: 1000)
• BACKTEST_BENCH_SYMBOL_STAGGER_NS per-symbol start offset in ns (default: 10000)
• BACKTEST_BENCH_FEED_LATENCY_NS (default: 2000000)
• BACKTEST_BENCH_ORDER_UPDATE_LATENCY_NS (default: 1000000)
• BACKTEST_BENCH_ORDER_LATENCY_NS (default: 500000)
• BACKTEST_BENCH_SUBMIT_EVERY_N order submission interval in ticks (default: 256)
• BACKTEST_BENCH_MAX_BATCH_NS batch-mode window in ns (default: 10000000)

Example:

# 8 symbols × 500k ticks per symbol, 5ms batch window
BACKTEST_BENCH_NSYMBOLS=8 \
BACKTEST_BENCH_TICKS_PER_SYMBOL=500000 \
BACKTEST_BENCH_MAX_BATCH_NS=5000000 \
cargo bench -p backtester-core --bench bench_core

The E2E benches (bench_engine_e2e_*) measure both Tick and Batch modes under identical conditions. Synthetic data is deterministic and includes realistic order flow (opposite-side, same-price passive limit orders at a fixed interval).

Profile-Guided Optimization (PGO)

To build with PGO for maximum performance (Linux/macOS):

# Requires llvm-profdata (part of LLVM tools)
make pgo

This runs a 4-step pipeline:

1. Instrumentation build
2. Profile generation (runs benchmarks)
3. Profile merge
4. Optimized build using profiles

Expected improvement: 5-15% throughput.

---

Examples (example/)

• example/colab_backtester_demo.ipynb
  • Minimal E2E demo notebook. Click the badge above to run in Colab.
  • For local use, start Jupyter from the repo root and open files under example/.
• example/crypto_researcher_adoption_guide.md
  • Practical guide for researchers: onboarding, data schema, strategy modes (tick/batch), performance tuning.

Note: Large datasets are not bundled. Start with the minimal data generated by tests in python/tests/ or the Colab demo.

---

Coding Style & Core Principles

• Rust: edition 2024, cargo fmt / cargo clippy. Naming: functions/modules snake_case, types CamelCase.
• Python: PEP 8, 4-space indent. Type hints required for new/changed code.
• Determinism first: fixed RNG seeds, stable ordering. Avoid f64 for monetary logic (only at I/O boundaries).

---

Contribution & Commit Guidelines

• Start with docs/SPEC.md and docs/PLAN.md.
• Conventional Commits: e.g., feat(core): add queue model, chore(fmt): rustfmt.
• Branches: feature/..., fix/..., chore/....
• PRs should include What/Why, linked issues, test plan (commands + results), and performance notes if core paths changed. Update docs/ when APIs or architecture shift.

---

Status & Roadmap

This project is under active development (WIP). APIs and internals may change.

• Technical spec: docs/SPEC.md
• Plan/tests/benches: docs/PLAN.md
• Researcher adoption guide: example/crypto_researcher_adoption_guide.md
• Colab demo: example/colab_backtester_demo.ipynb