Python Backtesting Engine Development

Python Backtesting Engine Development

Python is the dominant language for backtesting engine development thanks to its rich ecosystem: NumPy, pandas, scipy for calculations, ccxt for exchange integration, matplotlib/plotly for visualization. Developing a custom engine is justified when ready-made solutions (Backtrader, Freqtrade) don't meet specific requirements.

Custom Engine Architecture

# Core abstractions
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from decimal import Decimal
from typing import Optional
import pandas as pd

@dataclass
class Bar:
    timestamp: pd.Timestamp
    open: float
    high: float
    low: float
    close: float
    volume: float

@dataclass
class Order:
    id: str
    symbol: str
    side: str          # 'BUY' | 'SELL'
    type: str          # 'MARKET' | 'LIMIT' | 'STOP'
    quantity: float
    price: Optional[float] = None
    stop_price: Optional[float] = None
    status: str = 'PENDING'

@dataclass
class Position:
    symbol: str
    side: str
    quantity: float
    avg_entry_price: float
    unrealized_pnl: float = 0.0
    realized_pnl: float = 0.0

class Strategy(ABC):
    def __init__(self, context: 'BacktestContext'):
        self.ctx = context

    @abstractmethod
    def on_bar(self, bar: Bar) -> None:
        pass

    def buy(self, quantity: float, order_type: str = 'MARKET', price: float = None) -> Order:
        return self.ctx.submit_order(Order(
            id=self.ctx.generate_id(),
            symbol=self.ctx.symbol,
            side='BUY',
            type=order_type,
            quantity=quantity,
            price=price,
        ))

    def sell(self, quantity: float, order_type: str = 'MARKET', price: float = None) -> Order:
        return self.ctx.submit_order(Order(
            id=self.ctx.generate_id(),
            symbol=self.ctx.symbol,
            side='SELL',
            type=order_type,
            quantity=quantity,
            price=price,
        ))

    @property
    def position(self) -> Optional[Position]:
        return self.ctx.get_position(self.ctx.symbol)

    @property
    def cash(self) -> float:
        return self.ctx.portfolio.cash

Python backtesting engines provide the flexibility and control needed for professional algorithm development with full control over market simulation details.