simple-backtest-kankane 0.3.5

Simple backtesting framework for trading strategies

Simple Backtest

I have removed my githistory because i want to treat this as my base with imporvement in data loader and custom error message A high-performance, asset-agnostic backtesting framework for Python

Features • Installation • Quick Start • Data Loaders • Documentation

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📖 About

Simple Backtest provides a clean framework for running strategy backtests with strong validation, robust metrics, and extensible architecture.

You can still bring your own pandas DataFrame, but the project now also includes optional data source integrations so users can load and normalize OHLCV data faster.

✨ Features

• Backtesting Engine: Fast, deterministic strategy execution
• Validation First: Actionable errors for data, config, and strategy inputs
• Asset-Agnostic Design: Works with stocks, forex, crypto, ETFs, and more
• 20+ Metrics: Return, drawdown, Sharpe, Sortino, Calmar, alpha/beta, etc.
• Optimization: Grid search, random search, walk-forward
• Optional Data Integrations:
  • CSVLoader
  • YFinanceLoader
  • CCXTLoader
  • AlphaVantageLoader
  • PolygonLoader

📦 Installation

Core package

pip install simple-backtest

Optional loader dependencies

Install only what you use:

# Yahoo Finance
pip install yfinance

# Crypto exchange data
pip install ccxt

# REST API loaders (Alpha Vantage, Polygon)
pip install requests

Development setup

git clone <your-repository-url>
cd simple-backtest
pip install -e ".[dev]"

Requirements: Python 3.10+

🚀 Quick Start

Backtest from any OHLCV DataFrame

from simple_backtest import Backtest, BacktestConfig, MovingAverageStrategy

# data must contain: Open, High, Low, Close (Volume optional unless configured)
strategy = MovingAverageStrategy(short_window=10, long_window=30, shares=10)
config = BacktestConfig.default(initial_capital=10000)

backtest = Backtest(data, config)
results = backtest.run([strategy])

print(results.get_strategy(strategy.get_name()).summary())

Backtest using built-in CSV loader

from simple_backtest import Backtest, BacktestConfig, CSVLoader, MovingAverageStrategy

loader = CSVLoader()
data = loader.load("data/aapl.csv", start="2020-01-01", end="2023-12-31")

backtest = Backtest(data, BacktestConfig.default(initial_capital=10000))
results = backtest.run([MovingAverageStrategy(short_window=10, long_window=30, shares=10)])

🔌 Data Loaders

All loaders inherit from DataLoader and return a validated DataFrame with standardized columns:

Open, High, Low, Close, Volume

Validation is always run internally before the DataFrame is returned.

CSVLoader

• Reads local CSV files
• Auto-detects date column (Date, date, Datetime, datetime, or datetime index)
• Normalizes common column variants (open → Open, etc.)
• Supports optional date filtering via start and end

from simple_backtest import CSVLoader

data = CSVLoader().load("prices.csv", start="2021-01-01", end="2021-12-31")

YFinanceLoader

• Uses yfinance.download(...)
• Handles yfinance MultiIndex column outputs
• Raises clear import/data errors

from simple_backtest import YFinanceLoader

data = YFinanceLoader().load("AAPL", "2020-01-01", "2023-12-31")

CCXTLoader

• Uses ccxt exchange clients
• Supports constructor args: exchange_name, optional api_key, api_secret
• Converts millisecond timestamps to DatetimeIndex
• Auto-paginates OHLCV fetches for larger ranges

from simple_backtest import CCXTLoader

loader = CCXTLoader(exchange_name="coinbase")
dataw = loader.load("BTC/USD", "2021-01-01", "2021-12-31", timeframe="1d")

AlphaVantageLoader

• Uses Alpha Vantage daily REST endpoint
• Constructor requires api_key
• Parses API JSON into standardized OHLCV DataFrame
• Applies start / end filtering post-load

from simple_backtest import AlphaVantageLoader

loader = AlphaVantageLoader(api_key="YOUR_KEY")
data = loader.load("AAPL", "2020-01-01", "2023-12-31")

PolygonLoader

• Uses Polygon aggregates REST endpoint
• Constructor requires api_key
• Handles next_url pagination
• Parses o/h/l/c/v/t fields into standardized OHLCV DataFrame

from simple_backtest import PolygonLoader

loader = PolygonLoader(api_key="YOUR_KEY")
data = loader.load("AAPL", "2020-01-01", "2023-12-31", timespan="day", multiplier=1)

Create your own loader

import pandas as pd
from simple_backtest import DataLoader


class MyCustomLoader(DataLoader):
    def load(self, symbol, start, end) -> pd.DataFrame:
        # fetch/construct your data
        data = pd.DataFrame(...)
        return self._finalize_dataframe(data)

📚 Documentation

Built-in strategy helpers

When writing a custom strategy (subclass of Strategy), you can use:

• self.has_position()
• self.get_position()
• self.get_cash()
• self.get_portfolio_value()
• self.buy(shares)
• self.sell(shares)
• self.sell_all()
• self.hold()
• self.buy_percent(percent)
• self.buy_cash(amount)

Config presets

from simple_backtest import BacktestConfig

config = BacktestConfig.default(initial_capital=10000)
config_zero_fees = BacktestConfig.zero_commission(initial_capital=10000)
config_hft = BacktestConfig.high_frequency(initial_capital=100000)
config_swing = BacktestConfig.swing_trading(initial_capital=10000)

Optimizers

• GridSearchOptimizer
• RandomSearchOptimizer
• WalkForwardOptimizer

🧩 API Reference (What to Import)

This section explains:

• what can be imported directly from simple_backtest
• what should be imported from submodules
• what each import is typically used for

✅ Import from top-level package

These are re-exported in simple_backtest/__init__.py and are stable entry points for most users.

from simple_backtest import (
  # Core
  Backtest,
  BacktestConfig,
  Portfolio,
  Strategy,
  BacktestResults,
  StrategyResult,

  # Built-in strategies
  BuyAndHoldStrategy,
  DCAStrategy,
  MovingAverageStrategy,

  # Optimizers
  Optimizer,
  GridSearchOptimizer,
  RandomSearchOptimizer,
  WalkForwardOptimizer,

  # Commission models
  Commission,
  PercentageCommission,
  FlatCommission,
  TieredCommission,

  # Data loaders
  DataLoader,
  CSVLoader,
  YFinanceLoader,
  CCXTLoader,
  AlphaVantageLoader,
  PolygonLoader,
)

✅ Import from simple_backtest.utils

Use these for execution helpers, validation, logging, and custom commission wiring.

from simple_backtest.utils import (
  # Execution price helpers
  get_execution_price,
  create_execution_price_extractor,

  # Data/strategy validation
  validate_dataframe,
  validate_date_range,
  validate_strategies,

  # Validation exceptions
  BacktestError,
  DataValidationError,
  DateRangeError,
  StrategyError,

  # Commission helper factory
  get_commission_calculator,
  create_custom_commission,

  # Logging
  get_logger,
  setup_logging,
  disable_logging,
  enable_debug_logging,
)

✅ Import from simple_backtest.metrics

from simple_backtest.metrics import calculate_metrics, format_metrics

• calculate_metrics: compute full metric dictionary from returns/trades/portfolio data
• format_metrics: convert metric dictionary into readable report text

✅ Import from simple_backtest.visualization

from simple_backtest.visualization import (
  plot_equity_curve,
  plot_drawdowns,
  plot_returns_distribution,
  plot_monthly_returns,
  plot_trades,
  plot_strategy_trades,
  plot_rolling_metrics,
  create_comparison_table,
  plot_all,
)

⚠️ What is importable but not recommended as public API

You can technically import internals like:

from simple_backtest.metrics.definitions import calculate_sharpe_ratio
from simple_backtest.utils.execution import get_vwap

But these are lower-level internals and may change more often. Prefer top-level package imports and subpackage __init__ exports shown above.

Quick guide: “which import does what?”

• Backtest: runs strategies on price data
• BacktestConfig: execution settings (capital, commission, execution price, etc.)
• Strategy: base class for custom strategy logic
• Portfolio: tracks cash, positions, and trades
• ...Loader classes: fetch/normalize OHLCV data from source
• ...Optimizer classes: parameter search and evaluation
• Commission classes: trading-cost models
• utils functions: validation, execution-price extraction, logging helpers
• metrics functions: calculate/format performance metrics
• visualization functions: charts and comparison views

📌 One-page Import Cheat Sheet

You want to...	Import from	Import this
Run a backtest	simple_backtest	Backtest, BacktestConfig
Build custom strategy	simple_backtest	Strategy
Use built-in strategies	simple_backtest	MovingAverageStrategy, BuyAndHoldStrategy, DCAStrategy
Load CSV data	simple_backtest	CSVLoader
Load Yahoo Finance data	simple_backtest	YFinanceLoader
Load crypto exchange data	simple_backtest	CCXTLoader
Load Alpha Vantage data	simple_backtest	AlphaVantageLoader
Load Polygon data	simple_backtest	PolygonLoader
Create your own loader base	simple_backtest	DataLoader
Use commission models	simple_backtest	Commission, PercentageCommission, FlatCommission, TieredCommission
Optimize parameters	simple_backtest	GridSearchOptimizer, RandomSearchOptimizer, WalkForwardOptimizer
Validate input data/strategies	simple_backtest.utils	validate_dataframe, validate_date_range, validate_strategies
Build execution price logic	simple_backtest.utils	get_execution_price, create_execution_price_extractor
Configure logging	simple_backtest.utils	setup_logging, enable_debug_logging, disable_logging
Compute metrics	simple_backtest.metrics	calculate_metrics, format_metrics
Plot charts	simple_backtest.visualization	plot_equity_curve, plot_drawdowns, plot_returns_distribution, plot_monthly_returns, plot_trades, plot_strategy_trades, plot_rolling_metrics, create_comparison_table, plot_all

Rule of thumb: Prefer simple_backtest and package __init__ exports first. Use deep/internal module imports only when you intentionally need low-level internals.

📓 Notebooks

Jupyter examples are available in the notebooks folder:

• 01_basic_usage.ipynb
• 02_candle_strategies.ipynb
• 03_ta_strategies.ipynb
• 04_ml_strategies.ipynb
• 05_commission_usage.ipynb
• 06_advanced_optimization.ipynb

🛠️ Development

Run tests

pytest

Run linting

ruff check simple_backtest tests

Format

ruff format simple_backtest tests

🤝 Contributing

Contributions are welcome.

1. Fork repository
2. Create branch
3. Add tests for changes
4. Run pytest and ruff check
5. Open pull request

📄 License

MIT. See LICENSE.

📬 Support

• Issues: Use your repository issue tracker
• Discussions: Use your repository discussions page