Robot for trading on bo
Most of my code resembles spaghettiand if I were to refactor the python code I would use a more object orientated model. You can watch the bot frequently trade at 0xffde17ee19d75d70fcf2e4baf What does it do? It is an arbitrage bot.
Final Thoughts shares If you are looking to become a more profitable forex trader, or if you are a beginner in need of guidance, then our review of the best forex robots will help you find the right one for you. Forex robots use an algorithm to look for profitable trades.
That means that it earns money from trading the difference between prices on two or more exchanges. I needed something more reliable; a failed transaction means losing money.
Every single one my GET requests needed a reply, even if the TCP packet got lost or the webserver on the other end was temporarily down. Therefore I decided to implement my own python etherscan API wrapper and used pythereum to create the transactions and etherscan to publish them.
I also wrote my own requests. Here is the code I used to encode the etherdelta json API responses as hexadecimal, rlp encoded, ethereum transactions not for the faint hearted : The raw hexadecimal values in robot for trading on bo closure at the bottom are the function signatures that correspond to each function.
Cryptocurrencies as an asset class are volatile, very volatile. However, for a trader, volatility is great. Being able to invest at lower prices when the market is having a tantrum and offloading risk when the market is in euphoria is literally the life-blood of a market maker at an Investment Bank. What exactly are crypto trading bots anyway? Cryptocurrency trading bots are computer programs that automagically buy and sell various cryptocurrencies at the right time with the goal of generating a profit.
A function signature is derived from the keccak of the function and its arguments. It must be appended to the data parameter of a transaction followed by the data that makes up the arguments. In total my code is around lines long and contained in 5 different files.
The Outcome I made a couple of graphs from the data I logged using pymatplotlib. Making that sweet, sweet ether A perspective of time FUN was the most popular trade for some reason unbeknownst to me Conclusion Overall the entire project took me around two weeks during my spare time at school and it was a blast all round.
The next version is going to include 86 different exchanges and a whole lot of trading pairs. To the moon!