quantleet 0.1.0b1

Single-symbol Python backtesting and quant research tooling

Quantleet

Quantleet is a Python backtesting and quant research toolkit focused on a polished first-beta single-symbol historical backtesting workflow.

The first public beta target is 0.1.0b1. It is built for users who want to load OHLCV history, author a compact strategy, run a deterministic backtest, inspect result.report, visualize result.plot(), compare a small finite parameter grid, and run rolling walk-forward validation without learning the internal repository workflow first.

Beta Scope

Current first-beta scope:

• single-symbol, single-timeframe historical OHLCV backtesting
• long-or-flat strategy workflows
• DataFrameDataSource, CSVDataSource, CCXTDataSource, TimeBar, and BarSeries
• Strategy, ta, qc, ParameterStudy, and WalkForwardStudy
• BacktestEngine.run(source=..., strategy=StrategyClass, config=...)
• BacktestEngine.run(bars=..., strategy=StrategyClass, config=...)
• market, limit, stop-market, and stop-limit orders
• fixed quantity and qty_percent sizing
• conservative reservation, fills, positions, reporting, plotting, finite grid parameter exploration, and rolling walk-forward validation

Unsupported in the first public beta:

• live trading
• paper trading
• shorting
• leverage or margin
• multi-symbol portfolios
• multi-timeframe strategies
• trading recommendations or optimizer claims

Installation

Requirements:

• Python 3.12
• uv for local contributor setup

Package-user installation for the first beta will use the published package once 0.1.0b1 is released:

uv add quantleet==0.1.0b1

From a local checkout today:

uv sync
uv run poe verify

Useful targeted checks:

uv run poe repo-check
uv run pytest tests/smoke/local -q

Live tests are excluded from the default verification lane and must be run explicitly when needed.

Quickstart

from quantleet.backtest import BacktestEngine, CostConfig
from quantleet.data import DataFrameDataSource
from quantleet.research import qc, ta
from quantleet.strategy import Strategy


class SmaCrossStrategy(Strategy):
    def init(self) -> None:
        self.fast = ta.sma(self.data.close, length=2)
        self.slow = ta.sma(self.data.close, length=3)

    def on_bar(self, bar) -> None:
        if qc.is_na(self.fast[0]) or qc.is_na(self.slow[0]):
            return
        if qc.crossover(self.fast, self.slow):
            self.buy(quantity=1.0, tag="sma-entry")
        elif self.position.is_open and qc.crossunder(self.fast, self.slow):
            self.sell(quantity=1.0, tag="sma-exit")


source = DataFrameDataSource(
    frame=[
        {"timestamp": "1970-01-01T00:01:00+00:00", "open": 10.0, "high": 12.0, "low": 8.0, "close": 10.0, "volume": 1.0},
        {"timestamp": "1970-01-01T00:02:00+00:00", "open": 9.0, "high": 11.0, "low": 7.0, "close": 9.0, "volume": 1.0},
        {"timestamp": "1970-01-01T00:03:00+00:00", "open": 8.0, "high": 10.0, "low": 6.0, "close": 8.0, "volume": 1.0},
        {"timestamp": "1970-01-01T00:04:00+00:00", "open": 11.0, "high": 13.0, "low": 9.0, "close": 11.0, "volume": 1.0},
        {"timestamp": "1970-01-01T00:05:00+00:00", "open": 12.0, "high": 14.0, "low": 10.0, "close": 12.0, "volume": 1.0},
    ],
    symbol="BTC/USDT",
    timeframe="1m",
)

engine = BacktestEngine(
    initial_cash=1_000.0,
    costs=CostConfig(tick_size=1.0, slippage_ticks=0.0, fee_rate=0.0),
)

result = engine.run(source=source, strategy=SmaCrossStrategy, label="sma-cross")

print(result.report)
figure = result.plot()

result.report provides structured run, return, risk, trade, cost, exposure, fill, and closed-trade information. result.plot() returns a Matplotlib figure with price, fills, equity, and drawdown panels.

Examples

The first public beta has exactly three canonical examples:

• SMA crossover quickstart
• Orders and sizing
• Parameter exploration

See docs/site/examples.md.

Documentation

Public user documentation lives under docs/site. Start with:

• Installation
• Quickstart
• Examples
• Walk-forward analysis
• Beta scope
• Public API reference

Contributing

See CONTRIBUTING.md for local setup, verification, docs expectations, pull request guidance, and AI-assisted contribution ownership requirements.

Security

See SECURITY.md for vulnerability reporting, secrets handling, and financial safety boundaries.

Financial Disclaimer

Quantleet is research and software tooling, not financial advice. Backtest results do not guarantee future performance. You are responsible for data quality, assumptions, execution risk, and trading decisions.

License

Quantleet is licensed under the MIT license.