18182324 · November 29, 2025 16:07
diff --git a/Pairs Trading Backtesting Engine b/Pairs Trading Backtesting Engine
 You are a senior quantitative developer and trading strategist.  
 Your task is to design and implement, in Python, a modular PAIRS TRADING backtesting and evaluation engine that I can extend later.

 General requirements:
 - Use Python 3, pandas, NumPy and statsmodels, and clearly list all required packages.
 - Write clean, well commented code that a professional trader can understand.
 - Organize the logic into functions or simple classes, not a single script blob.
 - Assume I provide daily adjusted close prices for multiple tickers in a CSV or via a DataFrame.

 1) Data handling
 - Implement a function to:
  - Load price data for a universe of tickers from CSV.
  - Convert prices to log returns.
  - Align dates and handle missing data in a transparent way (e.g. forward fill, with a warning).

 2) Pair selection modules
 Implement separate modules (functions) for at least these methods:
 - Distance method:
  - Compute Euclidean distance between normalized cumulative return series over a formation window.
  - Return a ranked list of top N pairs.
 - Cointegration method:
  - Use Engle–Granger tests to find cointegrated pairs.
  - Return pairs that pass significance thresholds, with estimated hedge ratios.
 - Correlation based selection:
  - Compute Pearson, Spearman and Kendall correlations on returns.
  - Return pairs sorted by Kendall correlation.
 - (Optional, sketched only) Copula or PCA based selection:
  - Provide a placeholder function and comments describing how it would be integrated.

 3) Trading rules
 - Implement a generic pair trading rule that can work with any selected pair:
  - Inputs:
    - Price series of X and Y,
    - Hedge ratio,
    - Formation window length,
    - Entry Z score threshold (e.g. 2),
    - Exit Z score threshold (e.g. 0),
    - Side: trade both long–short directions.
  - Logic:
    - Construct the spread: spread = price_X – beta * price_Y.
    - Standardize spread using rolling mean and rolling standard deviation.
    - Enter trades when Z score crosses the entry threshold.
    - Exit trades when Z score reverts toward zero or when a max holding period is reached.
  - Include:
    - Per trade transaction costs,
    - Slippage parameter,
    - Position sizing in dollar terms or as a fixed notional.

 4) Backtest engine
 - Implement a backtest function that:
  - Takes:
    - A list of pairs with hedge ratios,
    - A trading rule configuration,
    - Start and end dates,
    - Transaction cost assumptions.
  - Iterates over all pairs and over time, simulates trades, and records:
    - Entry and exit dates,
    - Direction (long spread or short spread),
    - PnL per trade net of costs,
    - Max drawdown within each trade,
    - Holding period in days.

 5) Performance evaluation
 - Implement a performance module that aggregates results across all pairs and computes:
  - Total return,
  - Annualized return,
  - Annualized volatility,
  - Sharpe ratio,
  - Max drawdown,
  - Win rate,
  - Average profit per trade,
  - Average holding period,
  - Turnover.
 - Provide a simple textual report and a few key plots:
  - Equity curve over time,
  - Distribution of trade returns,
  - Histogram of holding periods.

 6) Configuration and extension
 - Show a short example script that:
  - Loads sample data,
  - Selects pairs using:
    - Distance method, then
    - Cointegration method,
  - Runs the backtest for each,
  - Prints and compares Sharpe ratio, max drawdown and win rate across methods.
 - Comment in the code where I could later:
  - Plug in machine learning based pair selection,
  - Add copula based signals,
  - Attach a RAG or MCP style layer that pulls configuration and parameters from external files or APIs.

 Output format:
 - First, show the high level module and function structure.
 - Then provide the full Python code, split into logical sections with comments.
 - Finally, provide a short “how to run this” section with example commands.
	You are a senior quantitative developer and trading strategist.
	Your task is to design and implement, in Python, a modular PAIRS TRADING backtesting and evaluation engine that I can extend later.

	General requirements:
	- Use Python 3, pandas, NumPy and statsmodels, and clearly list all required packages.
	- Write clean, well commented code that a professional trader can understand.
	- Organize the logic into functions or simple classes, not a single script blob.
	- Assume I provide daily adjusted close prices for multiple tickers in a CSV or via a DataFrame.

	1) Data handling
	- Implement a function to:
	- Load price data for a universe of tickers from CSV.
	- Convert prices to log returns.
	- Align dates and handle missing data in a transparent way (e.g. forward fill, with a warning).

	2) Pair selection modules
	Implement separate modules (functions) for at least these methods:
	- Distance method:
	- Compute Euclidean distance between normalized cumulative return series over a formation window.
	- Return a ranked list of top N pairs.
	- Cointegration method:
	- Use Engle–Granger tests to find cointegrated pairs.
	- Return pairs that pass significance thresholds, with estimated hedge ratios.
	- Correlation based selection:
	- Compute Pearson, Spearman and Kendall correlations on returns.
	- Return pairs sorted by Kendall correlation.
	- (Optional, sketched only) Copula or PCA based selection:
	- Provide a placeholder function and comments describing how it would be integrated.

	3) Trading rules
	- Implement a generic pair trading rule that can work with any selected pair:
	- Inputs:
	- Price series of X and Y,
	- Hedge ratio,
	- Formation window length,
	- Entry Z score threshold (e.g. 2),
	- Exit Z score threshold (e.g. 0),
	- Side: trade both long–short directions.
	- Logic:
	- Construct the spread: spread = price_X – beta * price_Y.
	- Standardize spread using rolling mean and rolling standard deviation.
	- Enter trades when Z score crosses the entry threshold.
	- Exit trades when Z score reverts toward zero or when a max holding period is reached.
	- Include:
	- Per trade transaction costs,
	- Slippage parameter,
	- Position sizing in dollar terms or as a fixed notional.

	4) Backtest engine
	- Implement a backtest function that:
	- Takes:
	- A list of pairs with hedge ratios,
	- A trading rule configuration,
	- Start and end dates,
	- Transaction cost assumptions.
	- Iterates over all pairs and over time, simulates trades, and records:
	- Entry and exit dates,
	- Direction (long spread or short spread),
	- PnL per trade net of costs,
	- Max drawdown within each trade,
	- Holding period in days.

	5) Performance evaluation
	- Implement a performance module that aggregates results across all pairs and computes:
	- Total return,
	- Annualized return,
	- Annualized volatility,
	- Sharpe ratio,
	- Max drawdown,
	- Win rate,
	- Average profit per trade,
	- Average holding period,
	- Turnover.
	- Provide a simple textual report and a few key plots:
	- Equity curve over time,
	- Distribution of trade returns,
	- Histogram of holding periods.

	6) Configuration and extension
	- Show a short example script that:
	- Loads sample data,
	- Selects pairs using:
	- Distance method, then
	- Cointegration method,
	- Runs the backtest for each,
	- Prints and compares Sharpe ratio, max drawdown and win rate across methods.
	- Comment in the code where I could later:
	- Plug in machine learning based pair selection,
	- Add copula based signals,
	- Attach a RAG or MCP style layer that pulls configuration and parameters from external files or APIs.

	Output format:
	- First, show the high level module and function structure.
	- Then provide the full Python code, split into logical sections with comments.
	- Finally, provide a short “how to run this” section with example commands.
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