Synthetic Cross-Rate Arbitrage with Python and MetaTrader 5
Summary
The article outlines a foreign-exchange arbitrage prototype that uses Python with MetaTrader 5. It retrieves tick data for multiple currency pairs, removes duplicate timestamps, calculates synthetic prices from combinations of quoted pairs, and compares those estimates with observed prices to identify potential discrepancies. Candidate opportunities are recorded, and the described test-order function limits the number of simultaneously open trades. The workflow also includes historical backtesting and a periodic operating loop.
The author emphasizes that apparent arbitrage can disappear quickly and may be eroded by execution delays, broker behavior, and other costs. The article discusses risk controls and suggests limit-order handling and machine learning as possible future additions. It presents implementation details and mentions backtesting and live trading, but the supplied text does not provide enough results to assess profitability, slippage, or robustness. Synthetic price discrepancies are signals to investigate, not guaranteed risk-free returns; practical performance depends on synchronized quotes, executable prices, and fast, reliable order handling.
Key ideas
- The prototype estimates synthetic exchange rates from combinations of currency pairs and compares them with direct quotes.
- Tick data is gathered through MetaTrader 5 and organized by timestamp for analysis.
- The system records candidate discrepancies and describes a cap on concurrent test trades.
- Short-lived opportunities may be lost to latency, price movement, or broker execution delays.
- Backtest evidence and live performance require careful evaluation of costs and executable prices.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.