Trading Strategy Backtesting and Performance Analysis

This repository contains a Python script designed for backtesting trading strategies, specifically focusing on a long/short equity strategy. The script includes functions to manage trading positions, handle cash flow, and calculate key performance metrics.

Table of Contents

• Introduction
• Functions Overview
  • find_last_day_of_month
  • metrics
  • plot_performance
  • handle_cash_flow
  • handling_positions
  • make_positions

Introduction

This script is intended for users interested in backtesting and analyzing trading strategies. It provides a set of functions to execute trades, calculate performance metrics, and visualize the results. The strategy is primarily focused on long/short equity trades, allowing users to simulate trading conditions and evaluate the effectiveness of their strategies.

Functions Overview

find_last_day_of_month Finds the last trading day of the specified month. Parameters: period (datetime): The target period (year and month). date_index (pd.DatetimeIndex): The index of dates to search within. Returns: The last trading day of the month or None if not found.

metrics Calculates various performance metrics for the trading strategy.

Parameters:
    df (pd.DataFrame): DataFrame containing strategy returns and benchmarks.
    rf (float): Risk-free rate for Sharpe/Sortino calculations (default 0).
    period_param (int): Periods per year (default 252).
Returns: A dictionary of calculated performance metrics.

plot_performance Plots cumulative performance of the strategy against Ibovespa and CDI.

Parameters:
    returns (pd.Series): Strategy returns.
    ibov (pd.Series): Ibovespa returns.
    metrics_plot (pd.DataFrame): Metrics data to display.
    cdi (pd.Series): CDI returns.
    input_data_plot (str): Plot title or label.

handle_cash_flow Updates the cash flow with information from the current trading round.

Parameters:
    cash_flow (pd.DataFrame): DataFrame tracking cash flow.
    date (datetime): Current round date.
    equity (float): Total equity available.
    total_equity_usage_buy (float): Equity used for buying.
    total_equity_usage_short (float): Equity used for shorting.
    cash_buy (float): Cash remaining after buying.
    cash_short (float): Cash remaining after shorting.
    round_control (int): Current trading round.

handling_positions Executes and handles trading positions, applying stop-loss if necessary.

Parameters:
    pos_control (pd.DataFrame): Position data.
    cash_flow_control (pd.DataFrame): Cash flow control DataFrame.
    prices_enfoque (pd.DataFrame): Stock prices DataFrame.
    date (datetime): Current date.
    fee (float): Transaction fee.
    round_control (int): Current trading round.
    cum_cdi (pd.Series): Cumulative CDI for adjusting short positions.
    stop (bool): Apply stop-loss (default False).
Returns:
    pd.DataFrame: Updated position data.
    float: Updated total equity.

make_positions Creates trading positions based on available equity, ATR values, and other parameters.

Parameters:
    use_equity (pd.DataFrame): Assets to be used for positions.
    prices_enfoque (pd.DataFrame): Stock prices.
    equity (float): Total equity available.
    date (datetime): Current date.
    fee (float): Transaction fee.
    round_control (int): Current trading round.
    buy (bool): Create buy positions (default True).
    adjust_equity (float): Equity proportion for positions (default 0.3).
    atr_values (pd.DataFrame): ATR values for adjusting positions (default None).
    long_biased (bool): Favor long positions (default False).
Returns:
    pd.DataFrame: Created positions.
    float: Total equity usage.
    float: Remaining cash after creating positions.