A Cointegration and ARMA Workflow for Forex Spread Forecasting

Summary

The document outlines a proposed automated foreign-exchange strategy built around cointegration and forecasting a spread. Its listed workflow transforms price data into log returns, treats outliers, performs linear regression, tests for cointegration with an augmented Dickey–Fuller test, and fits an ARMA model if the spread is stationary. It then forecasts the spread and uses a beta calculation to manage risk. The author asks how to improve the algorithm and mentions a backtest, but no equity-curve values or detailed results are included in the text.

The post presents a research pipeline rather than a validated trading method. It gives no specification of how currency pairs are selected, how the regression and ARMA orders are estimated, or how forecasts map to entries and exits. It also does not explain outlier treatment, beta-based sizing, transaction costs, or safeguards against look-ahead and repeated testing. Since the code, data, and performance evidence are external and absent here, the strategy’s robustness and profitability cannot be assessed from this document alone.

Key ideas

• The proposed strategy regresses forex series and tests the resulting spread for stationarity.
• An ARMA model is fitted only when the spread passes the stated cointegration test.
• The workflow proposes forecasting the spread and using beta to control risk.
• The post gives no concrete performance figures or enough implementation detail to evaluate the backtest.
• Transaction costs, signal rules, parameter selection, and protection against look-ahead bias are not described.

Tags

# Cointegration for forex using ARMA model to forecast the spread


# Cointegration for forex using ARMA model to forecast the spread












I am working on an automatized quantitative strategy that use cointegration in Forex. I am backtesting this strategy in Python.

Please see below the python file: https://drive.google.com/file/d/0B1AEYFPAAAE6eW5XeHlkTXprVUU/view?usp=sharing

See below the data that I used to backtest: https://drive.google.com/file/d/0B1AEYFPAAAE6amx0RWI1MGh3SW8/view?usp=sharing

My Algorithm is:

- Read File

- Transform the data with the log return

- Treat Outliers

- Realize the linear regression

- Test for cointegration with ADF test

- If the spread is stationary then apply the best ARMA model

- Forecast and using beta calculation to control the risk

The equity curve of this strategy is:

Could you please help to understand how can I improve this algo?

Thanks

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.