Event-Driven KuCoin Triangular Arbitrage Framework in Async Rust

This is an async Rust project to implement zero-risk crypto trinagular arbitrage, explore technical feasiblity of generating passsive income (i.e. sleep to earn!).

How the arbitrage works

Say we hold USDT, it checks all the coins(e.g. ETH) that can trade against BTC and USDT, and compare the profit by either:
- Buy-Sell-Sell: buy ETH (sell USDT), sell ETH (buy BTC), sell BTC (buy USDT)
- Buy-Buy-Sell: buy BTC (sell USDT), buy ETH (sell BTC), sell ETH (buy USDT)

Previous mistakes in Python

2 years ago I have made a python script that runs the triangular arbitrage in KuCoin, but it had several technical issues and ended up not following up.
https://github.com/kanekoshoyu/Kucoin-Triangular-Arbitrage
- Implementation with Python and REST polling was way too slow to obtain the valid arbitratge chances for execution. - Generally the Python REST API caused quite high rates of communication error, which took extra time for resorting. - It didn't count the actual size of the arbitrage order, which meant that the script kept buying some shitcoin and could not sell properly. - It simply took the mean price instead of best bid/ask, which means it took maker positions for each of three actions in one arbitrage, and did not execute arbitrage promptly.

How to run example executables

Copy/Rename config.ini.sample as config.ini and set the API key with your own KuCoin API credentials.
At the root directory of the project, run the below command cargo run --bin event_triangular event_triangular is one of the example executables. There are other executables in the bin directory.

Overview

The project is split into these components:

Example Executables

• bin contains example executable codes. Some of them are for network testing purpose.

  Internal Structure (Independent of Exchange APIs)

• model has internal generic data structures used for abstracted representations of markets. This should be independent of exchange APIs so that the the arbitrage strategy algorithm can be conducted across different exchanges.

• event has the events used to pass states and data passed across different components. It uses the internal model for the same reason.
• strategy has the implementations of arbitrage strategy algorithm. The algorithms are built upon internal model and event.
• global has the lazy_static globals that is used across the code. This may get replaced by passing Arc/Mutex to functions soon. I just wanted the code to be simple and readable with this way first.

Link to Exchange APIs (e.g. KuCoin)

• translator has the conversion of exchange API objects into internal models and vice versa. It uses traits and the traits are implemented per API models.
• broker has the tasks that runs API calls, and converts into internal data structure.

Major Structural Improvements

• Use compiled Rust code for neat, efficient and proper code
• WebSocket subscription of real-time order books to get all the latest maker/taker ask/bid
• Only take a taker position based on the latest best-bid/ask price/size
• Implement both data bandwidth monitor and arbitrage performance monitor as tasks
• Abstraction of orderbook sync and arbitrage strategies using internal model and event
• Concurrency of syncing and strategy tasks

Feature Progress List

| Feature | Status | | -------------------------------------------------------------------------------------------------- | --------- | | Whitelist all coins that can trade against two other quote coins (e.g. ETH, for ETH-BTC, ETH-USDT) | Available | | Look for arbitrage chance based on best ask/bid price and calculate the profit in percentage | Available | | Copy and sync local orderbook in real-time | Available | | Structurally allow multiple strategies to run in pararrel | Available | | Order placement upon triangular arbitrage chance | Available | | Resort against limit order that could not get filled | Pending | | Full triangular arbitrage with the middle coin other than BTC (e.g. ETH-USD, ALT-ETH, ALT-USD) | Pending |

To-dos

please refer to the Discussions channel in GitHub.