Building a Python Trading Bot: Step by Step Guide in 2021

 

In the world of cryptocurrency, trading bots are invaluable. Their main purpose is to take advantage of any discrepancies in trade after analyzing the exchange prices. Their presence has made trading cryptocurrency easier, helping to make the right decision at the right time.

 

Possessing a trading bot or building one can give you an edge in the crypto world. This is something that can be achieved using a high-level language like Python. Making a bot by using this language will be of great use, especially in 2021 when crypto has exploded. In this global pandemic that made the crypto world volatile, let’s see how to build a secure Python trading bot to win more profit.

 

Contents:

Python Trading Bots: Why Should You Build It?

Importance of trading bots

How trading bots work

Steps to Building Trading Bots

1. Define handler function
2. Computing indicators from data
3. Fetch portfolio
4. Fetch the symbol’s position
5. Finalize buy or sell signals

Putting the Finishing Touches & Backtesting

Final Thoughts

 

Python Trading Bots: Why Should You Build It?

Trading bots are simply defined as programs that connect to a preferred exchange’s API and automatically interact with it. This program will then use all collected data to trade in place of you. A set of criteria is put forth to be their guide and buying and selling is based on this.

Importance of trading bots

Trading bots are important in the financial world for their mere efficiency. Data collection on a volatile stock market, for example, can't be processed quickly by humans. Also, the high probability for human error limits precision.

 

Trading bots can collect and process data easily or in short, implement arbitrage. Arbitrage is a strategy that allows bots to execute orders instantly. This is a strategy that bots possess and humans don’t.

How trading bots work

As mentioned above, a set of criteria determines what a trading bot will do. These requirements fall under factors like the price movement of the market. Other factors that affect a trading bots decision are:

• Current orders
• Market volume
• Time 

 

These tools can be used to protect your assets even in the volatile year 2020-21. With the Finage trading data and API, you are bound to have more of a chance to be safe.

Steps to Building Trading Bots

Using Python code editors, you can build functional trading bots. There are certain steps to take to create a trading bot and they are as follows:

1. Define handler function

This is more of a checkpoint before the real work begins. All functions are annotated with a schedule decorator. This is where all symbol data is received. These functions are referred to as handlers.

 

Two arguments accompany these handlers with the first one stating and the second being data. The symbol data will always be received in the second argument for the specified interval. This bot trades in hour-long candle intervals with the BTC USDT symbol.

2. Computing indicators from data

This is the first step of creating the algorithm with two moving averages. EMA is the one with a longer look-back period of 40 candles and another with 20. It is also possible for data to be skipped if it is missing.

3. Fetch portfolio

This step demands that a snapshot of your current portfolio be fetched. This snapshot should have key information including the current balance of a quoted asset, USDT. This is where the bot will trade. This can leave room for a new position due to liquid

4. Fetch the symbol’s position

Here, a query is made for a position open by the symbol. Calling this function grants access to a boolean value that verifies the existence of an open position.

5. Finalize buy or sell signals

This is the point at which the algorithm is defined. This of course would be the trading strategy. The order APIs will then be used to create orders. This algorithm places market orders going on long, particularly if the shorter EMA crosses it. Thus making a defined buy in value.

 

All necessary position information is printed out and every order is tracked in your logs. You have no issues defining a clear sell logic. This is in case the algorithm finds an open position where a shorter crosses beneath a longer EMA.

Putting the Finishing Touches & Backtesting

When these steps are followed and put together, you will end up with a piece of code that can be used in your initial backtest. If the code presented appears to be too long or complex, we can sort it out for you.

 

Your trading bot appears to be ready at this point and needs to be put to the test. A myriad of tests can be done to test efficiency and accuracy. The time taken to put out this information is also checked. If the steps are done correctly, the result should be a functioning trading bot.

 

Any further analysis of your trading bot can be done by the use of a position management system. The API will track critical metrics as your bot is being tested, further verifying its functionality.

Final Thoughts

The volatile nature of cryptocurrency and the stock market requires the best Python trading bots. However, building one is not enough as they need to be run through tests to ensure reliability. By using the Finage widgets and API, all this can be done without compromising data collection and analysis!

You can build your trade bot with Finage free Market Data API key.

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