Profitable Trading: Deep Dive into Backtesting Strategies in Python

Backtesting is an essential process in trading, where traders and investors test their strategies using historical data to predict how they would have performed in the past. This process is crucial for assessing the viability of a trading strategy before risking actual capital in live markets.

Here we will provide code for sophisticated backtesting framework. It is designed to offer a comprehensive approach to evaluating trading strategies across various market conditions.

Complete code you can get from GitHub by subscribing at quantjourney.substack.com

Importance of Backtesting

Backtesting serves multiple critical functions in the trading strategy development process:

• Market Adaptability: Testing across different market conditions ensures that a strategy is robust and can adapt to market changes.
• Risk Management: It allows traders to understand the risk characteristics of their strategy, including potential drawdowns and volatility of returns.
• Strategy Optimization: Traders can tweak strategy parameters to find the optimal settings that maximize returns and minimize risk.
• Overfitting Avoidance: A well-designed backtest helps in identifying if a strategy is overfit to historical data, which could lead to poor performance in live trading.

Essential Backtesting Requirements

A comprehensive backtesting framework should cater to several requirements to ensure its effectiveness and reliability:

1. Historical Data Management: The ability to efficiently manage and preprocess vast amounts of historical data is foundational. This includes cleaning, normalization, and possibly enriching data with additional features.
2. Strategy Implementation Flexibility: The framework should allow for the easy implementation of diverse trading strategies, ranging from simple moving average crossovers to complex machine learning models.
3. Performance Analysis: Providing detailed performance analytics is vital. This includes calculating key metrics such as Sharpe ratio, maximum drawdown, and cumulative returns.
4. Risk Management Tools: Incorporating risk management components that can simulate various position sizing strategies and risk mitigation techniques is crucial.
5. Simulation Fidelity: The ability to simulate trading with high fidelity, including the impact of trading costs, slippage, and market impact, is essential for realistic performance estimation.

Backtesting Framework for Trading Strategies

 Base Class - Base class for all trading strategies.
 Functions:
  - preprocess_data: Preprocess data for backtesting.
  - determine_eligibility: Determine the eligibility of instruments based on specific criteria defined in the strategy parameters.
  - define_strategy_logic: Abstract method where the core logic of the strategy is defined.
  - run_simulation: Orchestrates the backtesting process, day by day.
  - daily_routine: A comprehensive daily routine covering all aspects of a trading day.
  - update_portfolio_value: Update the portfolio value based on current positions and trading costs.
  - is_rebalance_day: Checks whether the current day is a rebalance day according to the strategy's rebalance period.
  - adjust_strategy_parameters: Placeholder method for adjusting strategy parameters based on market conditions or other factors.

 author: jpolec
 date: 27-02-2024 & 01-04-2024
"""
from abc import ABC, abstractmethod
from datetime import datetime
from typing import List, Optional, Dict, Any, Tuple, Union
import pandas as pd
import numpy as np
from enum import Enum, auto
# Importing the necessary modules
from engine.signal_generator import SignalGenerator    # Generate trading signals based on market data
from engine.order_manager import OrderManager      # Generate and manage orders based on trading signals
from engine.risk_manager import RiskManager      # Manage risk and position sizing
from engine.portfolio_manager import PortfolioManager    # Manage the portfolio and positions
from engine.data_manager import DataManager      # Manage data for trading strategies
from engine.cost_model import CostModel       # Calculate trading costs
from engine.performance_analyzer import PerformanceAnalyzer   # Analyze the performance of the strategy
from engine.performance_monitoring import PerformanceMonitoring # Monitor the performance of the strategy
from engine.market_regime import MarketRegime      # Determine the current market regime
from engine.volatlity_model import VolatilityModel     # Model the volatility of the market
# Import configuration classes and functions
from strategies.strategy_config import StrategyConfig   # StrategyConfig class
from strategies.strategy_config import StrategyComponent  # StrategyComponent class
from strategies.strategy_config import StrategyComponentFactory # StrategyComponentFactory class
from strategies.strategy_config import default_strategy_params # Default strategy parameters
from strategies.strategy_config import (
 create_data_manager,
 create_signal_generator,
 create_order_manager,
 create_risk_manager,
 create_portfolio_manager,
 create_cost_model,
 create_performance_analyzer,
 create_performance_monitoring,
 create_market_regime,
 create_volatility_model,
)
import utils.utils as ut # FailureType Enum
 
# Logger
from qlib.data.utils.data_logs import data_logger
logger = data_logger()

# Base Strategy Class ----------------------------------------------------------
class BaseStrategy(ABC):
 """
 Base class for all trading strategies.
 """
 def __init__(
  self,
  
  # Component instances
  data_manager: DataManager, 
  signal_generator: SignalGenerator,
  order_manager: OrderManager,
  risk_manager: RiskManager,
  portfolio_manager: PortfolioManager,
  cost_model: CostModel,
  performance_analyzer: PerformanceAnalyzer,
  performance_monitoring: PerformanceMonitoring,
  market_regime: MarketRegime,
  volatility_model: VolatilityModel,
  # General strategy parameters
  instruments: List[str],
  initial_capital: float,
  trading_range: Optional[Dict[str, datetime]] = None,
  rebalance_period: Optional[int] = None,
  positional_inertia: Optional[float] = 0.0,
  
  # Strategy specific parameters
  strategy_params: Optional[Dict[str, Union[int, float, str, Dict]]] = None,  
  default_strategy_params: bool = True
 ):
  # Initialize strategy parameters
  if default_strategy_params:
   # Use default params from default_strategy.py
   config = StrategyConfig({**default_strategy_params, **(strategy_params or {})})
  else:
   # Initialize strategy parameters without default params
   config = StrategyConfig(strategy_params)
  
  factory = StrategyComponentFactory()
  self.data_manager = factory.create_data_manager(config)
  self.market_regime = factory.create_market_regime(config)
  self.volatility_model = factory.create_volatility_model(config)
  self.signal_generator = factory.create_signal_generator(config)
  self.order_manager = factory.create_order_manager(config)
  self.risk_manager = factory.create_risk_manager(config)
  self.portfolio_manager = factory.create_portfolio_manager(config)
  self.cost_model = factory.create_cost_model(config)
  self.performance_analyzer = factory.create_performance_analyzer(config)
  self.performance_monitoring = factory.create_performance_monitoring(config)
  
  # Internal state
  self.current_date = None
  self.portfolio_value = initial_capital
  self.positions = {}  # Dict to hold current positions
  self.returns_df = pd.DataFrame()
  self.leverage_series = pd.Series()
  self.instruments = instruments
  self.initial_capital = initial_capital
  
  # Assign optional arguments if provided
  self.trading_range = trading_range
  self.rebalance_period = rebalance_period
  self.positional_inertia = positional_inertia
  
 def strategy_save_state(self, filepath: str):
  """
  Save the state of the strategy to a JSON file.
    """
  state = {
   "current_date": self.current_date,
   "portfolio_value": self.portfolio_value,
   "positions": self.positions,
   # Add other elements as needed
  }
  # save with qlib
 def strategy_load_state(self, filepath: str):
  """
  Load the state of the strategy from a JSON file.
    """
  # Load the state of the strategy from a JSON file.
  self.current_date = state["current_date"]
  self.portfolio_value = state["portfolio_value"]
  self.positions = state["positions"]
  # Load other elements as needed
 def preprocess_data(self):
  """
  Preprocess data for backtesting.
  Args:
   None
  Returns:
   pd.DataFrame: Preprocessed market data with additional columns 'Returns' and 'Volatility'.
  """
  logger.info("Preprocessing data...")
  market_data = self.data_manager.get_market_data(self.instruments, self.trading_range)
  for instrument in self.instruments:
   market_data[instrument]['Returns'] = market_data[instrument]['Close'].pct_change()
   market_data[instrument]['Volatility'] = market_data[instrument]['Returns'].rolling(20).std() * np.sqrt(252)
  return market_data
 def determine_eligibility(self):
  """
  Determine the eligibility of instruments based on specific criteria defined in the strategy parameters.
  Args:
   None
  Returns:
   dict: A dictionary where keys are instrument names, and values are booleans indicating eligibility.
  """
  logger.info("Determining eligibility of instruments...")
  eligibles = {}
  market_data = self.preprocess_data()
  # Extract the eligibility criteria from the strategy parameters
  eligibility_criteria = self.strategy_params.get("eligibility_criteria", {})
  for instrument in self.instruments:
   eligible = True
   # Check volatility eligibility
   volatility_threshold = eligibility_criteria.get("volatility_threshold", 0.00001)
   volatility_eligible = market_data[instrument]['Volatility'] > volatility_threshold
   eligible = eligible and volatility_eligible
   # Check liquidity eligibility
   liquidity_threshold = eligibility_criteria.get("liquidity_threshold", 1000000)
   liquidity_eligible = market_data[instrument]['Volume'].mean() > liquidity_threshold
   eligible = eligible and liquidity_eligible
   # Check sector eligibility
   excluded_sectors = eligibility_criteria.get("excluded_sectors", ['Banking'])
   sector_eligible = instrument.split('.')[1] not in excluded_sectors
   eligible = eligible and sector_eligible

   eligibles[instrument] = eligible
  return eligibles

Features in the Provided Code

The code snippet outlines a robust backtesting framework that addresses these requirements through its various components and classes:

• Abstract Base Classes: The BaseStrategy class serves as a template for all trading strategies, ensuring a consistent implementation interface.
• Data Management: The DataManager component is responsible for managing historical market data, which is the backbone of any backtesting system.
• Signal Generation: The SignalGenerator class facilitates the implementation of logic to generate buy or sell signals based on the analyzed data.
• Order Management: The OrderManager component simulates the execution of trading orders, accounting for constraints like order types and execution prices.
• Risk Management: Through the RiskManager, strategies can incorporate risk control measures, adjusting position sizes and executing stop-loss orders as needed.
• Portfolio Management: The PortfolioManager keeps track of positions, balances, and computes the portfolio's performance over time.
• Cost Modeling: Understanding the impact of transaction costs is facilitated by the CostModel, which simulates broker fees, slippage, and other trading costs.
• Performance Analysis: The PerformanceAnalyzer and PerformanceMonitoring components offer tools for analyzing and monitoring the strategy's performance over time.
• Market Condition Adaptation: The MarketRegime and VolatilityModel classes allow strategies to adjust their logic based on different market conditions, enhancing adaptability.

Complete code you can get from GitHub by subscribing at quantjourney.substack.com

Conclusion

The provided backtesting framework is a comprehensive tool designed to meet the rigorous demands of trading strategy development and evaluation. By integrating key components such as data management, signal generation, order execution simulation, risk and portfolio management, as well as performance analysis, it lays a solid foundation for developing, testing, and refining trading strategies. Effective backtesting is indispensable in the pursuit of creating robust, profitable trading strategies that can withstand the test of time and varying market conditions.