Developing Custom Trading Strategies for freqtrade

Algorithmic trading has become increasingly popular among individual traders and financial institutions. Tools like freqtrade, an open-source cryptocurrency trading bot, allow traders to automate the implementation of trading strategies across various markets and exchanges. In this article, we will explore how to develop custom trading strategies using freqtrade.

Setting Up Freqtrade

To get started with freqtrade, you'll need to have Python installed on your system. Freqtrade requires Python version 3.6 or higher. You should also have a basic understanding of both Python and trading concepts.

$ git clone https://github.com/freqtrade/freqtrade.git
$ cd freqtrade
$ python3 -m venv .env
$ source .env/bin/activate
$ pip install -r requirements.txt

The above steps help you to clone the freqtrade repository, create a virtual environment, and install the necessary dependencies.

Understanding Freqtrade's Architecture

Freqtrade's architecture revolves around the separation of strategy logic from the core bot functionality. This approach allows you to focus on building your strategy without worrying about the other operational aspects of the bot.

Creating a Custom Strategy

Your trading strategy is defined by a Python class that specifies how and when orders should be placed and executed. Let's define a simple example strategy:

from freqtrade.strategy import IStrategy
from pandas import DataFrame

class SimpleStrategy(IStrategy):
    def __init__(self):
        super().__init__()

    # Define the minimal ROI values
    minimal_roi = {
        "0": 0.1,
        "10": 0.02
    }

    # Define stop loss
    stoploss = -0.10

    # Define trailing stop
    trailing_stop = True
    trailing_stop_positive = 0.01

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ma'] = dataframe['close'].rolling(window=10).mean()  # Simple Moving Average
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['close'] > dataframe['ma']), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['close'] < dataframe['ma']), 'exit_long'] = 1
        return dataframe

This simple strategy uses a simple moving average (SMA) to determine entry and exit points. The strategy buys when the price goes above the SMA and sells when it goes below.