Triangular Arbitrage for Crypto with Python - (Fast Track)

Explore triangular arbitrage and vanilla arbitrage, explaining how mispricing across markets creates edge. Learn about execution risks, latency, slippage, and testing with real prices in one place or across two places.

Learn to fetch crypto data from a public API using Python requests, parse JSON, and iterate over daily open, high, low, and close values from Poloniex.

Define a Python method (function) to fetch API data with requests and API. Reuse this method for different endpoints like ticker and order book, and handle returns.

Export the course code to a Python file or a Jupyter notebook from the resources package, explore the playground's Python folder, and work with it in PyCharm.

Master JavaScript basics by declaring variables with var, let, and const, using arrays and objects, printing results with console.log, and contrasting with Python.

Master the vanilla for loop in JavaScript by looping through arrays of objects, accessing coin names and prices, and printing results with console.log in a crypto context.

Install PyCharm community edition on macOS or Windows, create a new Python project with a virtual environment and a main.py script, and customize theme and fonts.

Identify tradable coin assets by filtering an API’s coin data to exclude frozen and post-only items, build a tradable coins list, and refresh daily for faster arbitrage calculations.

Learn how to build and populate a tradable coin list in Python by appending valid pairs, enforcing is_frozen and post_only zero, and validating against the API data.

Learn to structure Python code modularly by defining a step zero method, a main guard, and selectively running step one, step two, and beyond to control execution.

Structure the tradable pairs for triangular arbitrage by algorithmically selecting three pairs from exchange tokens, ensuring two of each, and save the list for daily updates.

Refactor the code by adding a structure triangular pairs function, pass the coin list, and annotate step zero and step one to organize and return tradable triangular arbitrage pairs.

Structure pair b from same coin list as pair a. Ensure b is not a and check a base or quote in pair box to form a joint with a.

Structure pair c from coin list by filtering pair a and pair b, build a pair box, and count two matches to identify triangular arbitrage pairs across all coins.

Pull price data for all structured triangular arbitrage pairs and assess surface-level opportunities using best bid and lowest ask prices. Learn a step-by-step workflow with the Poloniex API.

Retrieve the latest crypto prices from the same api url used for coin tickers, print prices, and show using a function to avoid duplicating code.

Structure prices for surface rate calculation by pulling ask and bid for each triangular pair, building a prices dictionary, and preparing data to detect triangular arbitrage opportunities.

Develop a method to calculate the surface rate of triangular arbitrage and build a dictionary of a, b, and c price data while setting min surface rate and starting amount.

Master bid and ask rate calculations for crypto trades using Poloniex prices; apply 1/ask for right-side trades and multiply by bid for left-side trades to account for the spread.

Generate triangular arbitrage calculations by looping forward and reverse through each path, using a direction list and base-to-quote logic, while initializing swap rates and tracking traded coins.

Learn to compute surface-rate triangular arbitrage in crypto with Python, by executing forward and reverse three-leg trades using base and quote currencies, swap rates, and bid/ask logic.

Extend the triangular arbitrage calculation in python by applying the same logic to a new scenario, handling quote-to-base and base-to-quote paths across btc and etherium using poloniex bid/ask for usdt.

Assess how a forward algorithm identifies crypto triangular arbitrage opportunities using Python, analyzes price dictionaries from Poloniex, and experiments with reverse calculations.

Learn to compute reverse triangular arbitrage by swapping base and quote, adjusting bid rates and direction, and identifying reverse arbitrage opportunities alongside forward calculations.

Output surface rate opportunities by building a surface dictionary, looping to detect arbitrage when profit exceeds the minimum surface rate, and printing trade details while respecting Poloniex API limits.

Review the end-to-end workflow for computing surface arbitrage rates across exchanges, test with Poloniex data, fix bugs, and plan order book depth and multi-exchange next steps using Python.

Retrieve and reformat the order book data via an api get request, obtaining prices and depth of at least 20 for contract one, two, and three to support programmatic arbitrage.

Learn how to reformat the order book for depth calculations, converting base to quote and quote to base, and invert prices while handling bid and ask data.

Explore calculating order book depth to determine acquired coins across three trades using reformatted depth prices and AK depth calculation, tracking starting amounts and profits.

Set up a fresh PyCharm project for Uniswap data work, establish a new virtual environment, install requests, and prepare to pull prices from the graph via queries.

Learn to pull Uniswap v3 price data via GraphQL queries and the subgraph, assess liquidity in pools, and identify triangular arbitrage opportunities using Python.

Automate pulling all Uniswap pool prices from a GraphQL endpoint using Python requests, posting a query, parsing JSON, and extracting mid prices from the pools.

Structures triangular groups from crypto pairs by selecting A, B, and C, building pair boxes, counting coin occurrences, and enforcing two of each coin for unique, sorted pairs.

Structure triangular arbitrage data in Python by building output dictionaries for each token pair, filtering duplicates with a remove duplicates list, and tracking tradeable pairs.

Pipe in and loop through structured triangular pairs to calculate surface rates with a triangular arbitrage function, adapting Python logic to JavaScript for Uniswap v3.

Walk through Python code that demonstrates surface rates for triangular arbitrage in crypto, detailing base-to-quote and quote-to-base trades and saving opportunities as JSON for later JavaScript Uniswap SDK use.

Save surface rates to a json file, set a threshold, and automate Python scans to identify triangular arbitrage opportunities for deeper depth analysis.