A Complete Guide on Building a Crypto Bot with Python

Crypto bot with Python

This guide will provide a detailed step-by-step components to build a crypto bot with Python

Cryptocurrency is a digital asset or a digital form of money that is built on the blockchain technology. Similar to stock exchanges there are crypto exchanges through which the cryptocurrency can be traded. Trading cryptocurrency can feel overwhelming in the beginning. There are a lot of components to think about, data to collect, exchanges to integrate, and complex order management. This guide will provide a detailed step-by-step breakdown of different components you need in order to build a complete crypto bot with Python. Think of these as the building blocks of your trading bot.

Why Crypto bots?

Unlike humans, trading bots can consistently execute strategies that are precise. Trading bots can execute orders within milliseconds of an event occurring. Humans don’t have the reflexes or capacity to effectively implement such a strategy without some sort of trading bot.

There are many varieties of crypto bots. One of the most popular types is the arbitrage bot. Arbitrage bots are tools that examine prices across exchanges and make trades in order to take advantage of discrepancies. Because the price of a cryptocurrency like Bitcoin tends to vary somewhat from exchange to exchange, bots that can move fast enough can beat exchanges that are delayed in updating their prices.

Here are the Steps to Build Crypto Bot with Python According to Trality:

Step 0: Define handler function

To begin with, every function that is annotated with our schedule decorator is run on a specified time interval and receives symbol data. We call these annotated functions handlers, but you can name them however you want. They just have to take two arguments. We call the first one state and the second one data. The second argument will always receive the symbol data for the interval you specified. In this particular bot, we trade in 6 hour candle intervals and we specify a trading symbol, which is BTCUSDT. Ofcourse, multiple symbols are possible to trade on as well!

Step 1: Compute indicators from data

In the first step of our algorithm creation, we define two exponential moving averages (EMA), one with a shorter look-back period of 20 candles and one longer with a period of 50 candles.

Step 2: Fetch position for symbol

In a second step we query for any open position by symbol. By calling this function we receive a boolean value indicating whether an open position for that symbol exists or not.

Step 3: Fetch position for symbol

In a third step we query for any open position by symbol. By calling this function we receive a boolean value indicating whether an open position for that symbol exists or not.

Step 3: Resolve buy or sell signals

In a third step, the heart and soul of our algorithm is defined: its trading strategy. We use the order API to create orders. Specifically, the algorithm places a market order going long if the shorter EMA crosses above the longer for 80% of the account balance.

We also define a sell logic, which closes the open position if the algorithm detects an open position and the shorter crosses below the longer EMA.

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