Discussing the article: "Zero-Copy Tick Streaming (Part 1): Bridging MetaTrader 5 to Shared Memory with the Arrow C Data Interface"

 

Check out the new article: Zero-Copy Tick Streaming (Part 1): Bridging MetaTrader 5 to Shared Memory with the Arrow C Data Interface.

This article implements a lock-free shared-memory bridge in MetaTrader 5 that writes ticks in Apache Arrow’s columnar layout via the C Data Interface. It details the column layout, double buffering with a seqlock, and a batching strategy. You get full source for a writer class, a streaming Expert Advisor, and a self-test script that validates correctness before any Python reader is involved.

If you have ever tried to get live tick data out of MetaTrader 5 and into a proper Python analytics stack, you already know the annoying part isn't the trading logic - it's the plumbing. You write a CSV exporter, or you open a socket, or you reach for a shared-memory hack, and then you spend the rest of your afternoon writing a parser on the Python side that turns raw bytes back into something pandas can use. Every one of those approaches pays a "translation tax" on every single tick: serialize on the way out, deserialize on the way in, and somewhere in the middle you're allocating a fresh Python object per field per tick.

This two-part series removes that tax entirely, using the Arrow C Data Interface - the same zero-copy mechanism that lets DuckDB, Polars, and pandas hand data to each other without a single byte being copied. MetaTrader 5 writes ticks natively in Arrow's columnar memory layout into a block of shared memory; Python reconstructs the real ArrowArray/ArrowSchema C structs pointing straight at that memory and hands them to pyarrow, which materializes a RecordBatch with no parsing loop at all. Part 1 - this article - builds the MetaTrader 5 side: the columnar memory layout, the lock-free publish mechanism, the Expert Advisor that streams live ticks, and a diagnostic script that proves the MetaTrader 5 half is correct on its own before Python ever gets involved. Part 2 covers the Python reader and a benchmark that quantifies the zero-copy claim with real numbers.

Author: Adedayo David Gbadebo