Runnable examples

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Start with the first backtest for a complete copyable script. The examples below are included in a source checkout or source distribution. An installed wheel includes sample data but not this examples directory.

Run a complete script

From the repository root, run a direct tutorial:

uv run --locked python examples/advanced/native_run.py

To run a decorated strategy, use the example runner to supply its data and settings:

uv run --locked python examples/advanced/run_author.py examples/strategies/ema_macd.py

Example index includes prerequisites and commands.

Choose by task

Example	What it demonstrates	How it runs
advanced/optimizer_results.py	Select inputs; save complete Portfolios and reopen for sorting	Run directly
strategies/local_value_helpers.py	Tick rounding and per-bar gradients	Use run_author.py
strategies/local_builtin_indicators.py	Conditional VWAP, mode, and five parameter extensions	Use run_author.py
strategies/closed_trade_statistics.py	Partial exits with fees and frozen statistics	Use run_author.py
strategies/strategy_logs.py	Three log levels, nonfatal error logs, explicit exceptions, and end records	Use run_author.py
advanced/strategy_logs.py	Timestamps, bars, source lines, failure retrieval, reruns, and host sessions	Run directly
strategies/bar_states.py	First-bar callbacks, boundary states, positions, labels, and local validity guards	Use run_author.py
strategies/custom_indicators.py	Custom functions, two parameter sets, named outputs, and reusable composition	Use run_author.py
strategies/plotting.py	Color Input, Color/strings, transparency, panes, and per-bar coloring	Use run_author.py
advanced/portfolio_report.py	All report sections, partial-exit identities, MFE/MAE, signals, and frozen JSON	Run directly
advanced/plotting_export.py	Color overrides, configuration replay, frozen snapshots, strict JSON, signals, and fills	Run directly
strategies/trading_lifecycle.py	Order events, rejection, partial fees, accounts, and end-of-run policies	Use run_author.py
strategies/fractional_sizing.py	Three sizing modes, default overrides, frozen snapshots, named targets, and reduce-only	Use run_author.py
strategies/managed_orders.py	Expiry, mixed-size OrderRequest OCO, protection, and trailing stops	Use run_author.py
strategies/first_backtest.py	First backtest: EMA registration, stepwise decisions, and decorator declarations	Use run_author.py
advanced/strategy_settings.py	Five enum categories, flat overrides, JSON versions, and interest recalculation	Run directly
advanced/chart_context.py	Instrument/timeframe injection, chart-local hours, statistical day boundaries, and DST repeated hours	Run directly
advanced/run_author.py	Host loads author scripts and reads results	Run directly
advanced/indicator_reference.py	Explore by indicator ID and check each channel for validity	Run directly
strategies/ema_macd.py	Preferred template: inputs, EMA, MACD; understand init/next	Use run_author.py
strategies/data_sources.py	Eight data sources, saved handles, and derived prices; read the preferred template first	Use run_author.py
strategies/cross_signals.py	Crossover composition and current is_* booleans; understand init/next	Use run_author.py
strategies/confirmed_events.py	Composition, confirmed events, patterns, and monthly anchors; understand validity/time alignment	Use run_author.py
strategies/ema_inputs.py	Built-in EMA and inputs; check indicator validity	Use run_author.py
strategies/all_inputs.py	All nine inputs, enum/color runtime values, and time/session boundaries	Use run_author.py
advanced/native_run.py	Native runs, statistics/equity/trade tables; direct Python script	Run directly
advanced/composable_strategies.py	Signal and trailing-stop base classes; understand super call order	Run directly
advanced/multiple_time_frames.py	Built-in daily/weekly RSI; understand resampling, completed periods, and calendar assumptions	Run directly
advanced/parameter_optimization.py	Four grid combinations and parameter scores; understand native parameters	Run directly
advanced/machine_learning.py	Feature columns, one-time/rolling training; requires ML and time-series evaluation knowledge	Run directly

Optimization requires the optimize extra; the ML tutorial requires scikit-learn and can take substantially longer. The indicator pages also contain self-contained samples requiring only the installed package.