Integrating freqtrade with TA-Lib and pandas-ta Indicators

When it comes to crypto trading bots, Freqtrade is one of the most powerful open-source platforms available. It allows for automated trading strategies on cryptocurrency exchanges. However, to maximize its potential, you can integrate it with various technical analysis (TA) libraries such as TA-Lib and pandas-ta. These libraries offer a range of indicators that can help create more sophisticated trading strategies.

Setup Prerequisites

Before integrating Freqtrade with TA-Lib and pandas-ta, ensure you have a working installation of Freqtrade. Additionally, you will need Python installed on your machine. If you haven’t installed Freqtrade yet, follow the instructions below:

Installing Freqtrade

Install Freqtrade using the following command:

pip install freqtrade

After installing, you’ll need to set up a configuration by initializing Freqtrade in a new directory:

freqtrade new-config --config user_config.json

Installing TA-Lib

TA-Lib is a widely-used library that requires additional compiler files. Use the below command to install:

pip install TA-Lib

If you encounter issues, especially on Windows, you might need to download precompiled binaries from the TA-Lib website.

Installing pandas-ta

The pandas-ta library leverages pandas to provide simple and complex technical indicators. To install, execute:

pip install pandas-ta

Integrating Indicators into Freqtrade Strategies

Now that the libraries are installed, you can integrate them into your strategies.

Using TA-Lib with Freqtrade

To use TA-Lib indicators, import the library within your strategy file:

import talib

class AwesomeStrategy(IStrategy):
    def populate_indicators(self, dataframe, metadata):
        dataframe['rsi'] = talib.RSI(dataframe['close'])
        dataframe['sma_50'] = talib.SMA(dataframe['close'], timeperiod=50)
        return dataframe

In the example, the RSI and SMA are calculated using the close price from the dataframe supplied by Freqtrade.

Using Pandas-TA with Freqtrade

Similarly, you can incorporate pandas-ta indicators:

import pandas_ta as ta

class AwesomeStrategy(IStrategy):
    def populate_indicators(self, dataframe, metadata):
        dataframe['ema_200'] = ta.ema(dataframe['close'], length=200)
        dataframe['adx'] = ta.adx(dataframe['high'], dataframe['low'], dataframe['close'])['ADX_14']
        return dataframe

In this snippet, we use pandas-ta's ema and adx functions to compute the exponential moving average and ADX indicator.

Example Strategy Using Both Libraries

Combining indicators from both TA-Lib and pandas-ta can create a robust strategy:

class CombinedStrategy(IStrategy):
    def populate_indicators(self, dataframe, metadata):
        # TA-Lib indicators
        dataframe['rsi'] = talib.RSI(dataframe['close'])

        # pandas-ta indicators
        dataframe['ema_200'] = ta.ema(dataframe['close'], length=200)

        return dataframe

    def populate_buy_trend(self, dataframe, metadata):
        conditions = []
        conditions.append(dataframe['rsi'] < 30)
        conditions.append(dataframe['close'] > dataframe['ema_200'])

        if conditions:
            return pd.DataFrame({ 'buy': dataframe.eval(' and '.join(conditions)) }, index=dataframe.index)
        return pd.DataFrame({ 'buy': False }, index=dataframe.index)

This example uses an RSI lower than 30 as a buy signal if the current close price is also above the EMA-200. This combination demonstrates how TA analysis from both libraries can synergistically enhance strategy robustness.

Conclusion

Integrating TA-Lib and pandas-ta with Freqtrade expands your toolkit for creating crypto trading strategies. With hundreds of indicators available, you can optimize and test numerous strategies. Remember to backtest extensively to ensure that any new approach provides consistent and reliable results before applying it in the real market.