git/RScriptForExchangeRateAnalysis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
git 2026-03-16 13:16:16 +00:00
parent 1c21515f4f
commit 55000043cc
1 changed files with 27 additions and 43 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

70
README.md
View File

@ -1,56 +1,40 @@
ECB FX Trend Analyzer & Pair Ranker # ECB FX Trend Analyzer & Pair Ranker
Overview
This R script automates the extraction and statistical analysis of daily foreign exchange rates from the European Central Bank (ECB). Designed for trend-following or long/short strategy research, it identifies the top FX pairs exhibiting the strongest, most predictable linear trends over a customizable rolling window.
Instead of relying on standard return/volatility metrics, it ranks pairs using a custom Signal-to-Noise Ratio derived from linear regression, isolating pairs that move cleanly in one direction with minimal chop. ### Overview
This R script automates the extraction and statistical analysis of daily foreign exchange rates from the European Central Bank (ECB). Designed for trend-following or long/short strategy research, it identifies the top FX pairs exhibiting the strongest, most predictable linear trends over a customizable rolling window.
Key Features Instead of relying on standard return/volatility metrics, it ranks pairs using a custom **Signal-to-Noise Ratio** derived from linear regression, isolating pairs that move cleanly in one direction with minimal chop.
Automated Data Ingestion: Securely downloads, unzips, and cleans the latest historical daily exchange rate dataset directly from the ECB repository.
Dynamic Cross-Pair Generation: Automatically calculates non-EUR cross-pairs (e.g., USD/JPY, GBP/JPY) on the fly based on any user-defined list of target currencies. ---
Custom Timeframes: Allows users to easily slice the dataset to analyze specific lookback periods (e.g., the last 720 days). ### Key Features
* **Automated Data Ingestion:** Securely downloads, unzips, and cleans the latest historical daily exchange rate dataset directly from the ECB repository.
* **Dynamic Cross-Pair Generation:** Automatically calculates non-EUR cross-pairs (e.g., USD/JPY, GBP/JPY) on the fly based on any user-defined list of target currencies.
* **Custom Timeframes:** Allows users to easily slice the dataset to analyze specific lookback periods (e.g., the last 720 days).
* **Statistical Ranking Methodology:** * Runs a simple linear regression for each currency pair (Exchange Rate ~ Time).
* Extracts the slope (**Beta**) to measure the strength and direction of the trend.
* Extracts the Residual Standard Error (**RSE**) to measure the volatility or "noise" around that trendline.
* Ranks all pairs by **|Beta| / RSE**, surfacing the top 3 pairs with the smoothest, most predictable trajectories regardless of whether they are trending up or down.
Statistical Ranking Methodology: * Runs a simple linear regression for each currency pair (Exchange Rate ~ Time). ---
Extracts the slope (Beta) to measure the strength and direction of the trend. ### Prerequisites
* **R (Version 3.0 or higher):** The script relies entirely on base R functions. No external libraries (like `dplyr` or `quantmod`) are required.
* **Internet Connection:** Required for the script to automatically fetch the latest dataset directly from the ECB servers.
Extracts the Residual Standard Error (RSE) to measure the volatility or "noise" around that trendline. ---
Ranks all pairs by |Beta| / RSE, surfacing the top 3 pairs with the smoothest, most predictable trajectories regardless of whether they are trending up or down. ### How to Use
Prerequisites **1. Open the Script**
R (Version 3.0 or higher): The script relies entirely on base R functions. No external libraries (like dplyr or quantmod) are required. Open the `.R` file in your preferred environment (RStudio, VS Code, or the standard R GUI).
Internet Connection: Required for the script to automatically fetch the latest dataset directly from the ECB servers. **2. Configure Your Parameters**
At the very top of the script, locate the `CONFIGURATION` section to customize your analysis:
* `target_currencies`: An array of the currency tickers you want to analyze. The script will automatically generate all possible cross-pairs from this list. *(Note: All original columns are priced with a EUR base by default).*
* `n_days_to_analyze`: An integer representing your lookback window in days (e.g., `720` for roughly two years). Set this to `NULL` to analyze the entire historical dataset dating back to 1999.
How to Use ```R
1. Open the Script
Open the .R file in your preferred environment (RStudio, VS Code, or the standard R GUI).
2. Configure Your Parameters
At the very top of the script, locate the CONFIGURATION section to customize your analysis:
target_currencies: An array of the currency tickers you want to analyze (e.g., c("USD", "JPY", "GBP", "CHF")). The script will automatically generate all possible cross-pairs from this list. Note: All currencies are priced with a EUR base by default.
n_days_to_analyze: An integer representing your lookback window in days (e.g., 720 for roughly two years). Set this to NULL to analyze the entire historical dataset dating back to 1999.
R
# Example Configuration # Example Configuration
target_currencies <- c("USD", "JPY", "GBP") target_currencies <- c("USD", "JPY", "GBP")
n_days_to_analyze <- 720 n_days_to_analyze <- 720
3. Run the Script
Execute the script. It will run silently, downloading the data to a temporary directory, unpacking it, and crunching the linear regressions.
4. Interpret the Output
The console will print a dataframe of the top 3 currency pairs.
Pair: The specific currency or cross-pair.
Beta: The daily linear trend (positive = upward trend, negative = downward trend).
AbsBeta: The absolute value of the trend slope.
RSE: The Residual Standard Error (volatility/noise around the trendline).
SignalToNoise: The final ranking metric (AbsBeta / RSE). A higher number indicates a steeper, smoother, and more predictable trend.