Backtest Engine

A compact event-driven backtesting engine built from scratch in Python. Its main research rule is explicit: signals are generated at close[t], filled at open[t+1], and marked at close[t+1]. That prevents the same-close look-ahead error common in first backtests.

What version 1 includes

• Injected pandas DataFrames for deterministic, network-free tests
• Target-weight sizing, cash reservation, and order rejection
• Directional slippage and per-share/minimum commissions
• Order, fill, trade, position, cash, rejection, and equity ledgers
• A cost-matched buy-and-hold benchmark
• Transaction-cost sensitivity at 0, 1, 5, and 10 bps
• Tests for timing, cash, multiple symbols, costs, exits, missing bars, and final marking

Event flow

bar opens -> pending orders fill -> bar closes -> strategy signals
     ^                                                |
     |                                                v
next bar <--- order waits in broker <--- portfolio target weights

Install and test

python -m venv .venv
source .venv/bin/activate        # Windows PowerShell: .venv\Scripts\Activate.ps1
pip install -e ".[dev]"
python -m unittest discover -s tests -v

Run the SPY example (this step downloads market data):

python run.py

Run the cross-sectional sector-ETF example:

python run_cross_sectional.py

That experiment ranks nine US sector ETFs using a fixed 12-1 momentum signal, holds the top third, and rebalances approximately every 21 trading days. It compares the strategy with an equal-weight buy-and-hold basket through the same event engine and cost model. Results at 0, 5, 10, and 25 bps are written to data/cross_sectional/.

The implementation is intentionally long-only. Adding a negative target to a long-only portfolio would not constitute a valid short simulation: borrow, margin, short proceeds, financing, and forced-liquidation rules must be modeled explicitly first.

Outputs are saved under data/, including every ledger, the cost-sensitivity table, and an equity chart.

Minimal network-free use

import pandas as pd
from engine.backtest import Backtest
from engine.strategy import MovingAverageCross

bars = pd.DataFrame(
    {"Open": [100, 101, 102], "Close": [101, 102, 103]},
    index=pd.date_range("2024-01-01", periods=3),
)
results = Backtest(
    ["TEST"],
    price_data={"TEST": bars},
    strategy_cls=MovingAverageCross,
    strategy_kwargs={"short": 1, "long": 2},
).run()
print(results["fills"])

Scope and limitations

This is an educational daily-bar simulator, not a production trading system. It does not model partial market liquidity, limit orders, corporate-action edge cases, borrow, taxes, or intraday queue position. See methodology, design, and results.