Building a Twitter Bot for Crypto Trading Signals using Python


In this article, you will learn to develop a Twitter signal bot in Python. Our bot will pull real-time price data of cryptocurrencies like Bitcoin, Ethereum, Doge from a crypto exchange ( and examine it for any noticeable price movements according to a predefined set of rules. Once it detects a signal, the bot will automatically tweet about it using the Twitter API. Isn’t that cool?

In their simplest form, Twitter signaling bots can proactively inform their audience about relevant events in the market. Such an event can be a sharp rise or fall in price or a sudden spike in the trading volume. While this sounds simple, a well-defined signaling logic can constitute the first step toward algorithmic trading.

Think about it, if we examine data for certain price movements, we can also store these events and use them later to train a predictive model. More advanced signal bots use such models to inform their users when they identify a phase that is appropriate times to enter or exit the market. Or the bot executes such trading activities directly himself. And voilá! This is algorithmic trading. But one thing at a time. So let’s first develop a signal bot.

Bots can do a lot of cool things

The rest of this article is structured as follows. First, we’ll take a closer look at the different code modules of the bot. Then, we will briefly speak about the APIs that our bot will use. And finally, we get into the coding part and implement the signal bot using Python. Have fun! 🙂

Signal Bot Concept

The architecture of the signal bot to be developed can be divided into four different modules, each of which has a clear function. Let’s look at these modules next:

  1. The Data Collection Module retrieves price data from the crypto exchange The requests are being sent at a regular interval against the API. The module adds the data to separate data stores – one for each cryptocurrency. It then forwards the data to the preprocessing Module.
  2. The Data Preprocessing Module calculates the statistical indicators such as moving averages or means, which become the basis for the signaling logic.
  3. Based on the indicator values provided, the Signaling Module searches for relevant events. If a relevant event is detected, it is reported to the communication module.
  4. The Communication Module connects to the Twitter API. As soon as it is informed about a new event, it tweets about this event on Twitter.
Components of the Relataly Crypto Signal Bot
Components of the Relataly Crypto Signal Bot

About the APIs, used in this Tutorial

In this tutorial, we will be using two APIs: and Twitter


Firstly, we will be using the API to obtain prices for various cryptocurrencies. is one of the smaller crypto exchanges but offers a wide range of cryptocurrencies, some of which cannot be traded anywhere else. As of now, the market endpoint does not require authentication. To learn more about pulling data via the API in Python, check out our recent tutorial.

The Twitter API

The second API that our bot will use, is the Twitter API. We will use this API via the Python package Tweepy to post crypto price signals. If you are looking for a simple code example of how to submit tweets via the Twitter API, check out this article. In case, you don’t want to use Twitter, you can disable its use in the code.

Posting tweets via the API requires authentication with a valid developer account. You can apply for a developer account for free by following this link. Just, be aware that the confirmation can sometimes take several days.

Application to a developer account for the Twitter API can take some time

Implementing a Twitter Signal Bot using Python

Let’s begin with the implementation. If you want to use the Twitter functionality, you will require a Twitter developer account. Without an account, you can still print out trading signals, but you will not be able to post them via the Twitter API.

As always, you can clone the code from the relataly Github repository.

Python Prerequisites

Before we start the coding part, make sure that you have setup your Python 3 environment and required packages. If you don’t have an environment set up yet, you can follow this tutorial to setup the Anaconda environment.

Also make sure you install all required packages. In this tutorial, we will be working with the following standard packages: 

In addition, we will use the following two packages:

You can install packages using console commands:

  • pip install <package name>
  • conda install <package name> (if you are using the anaconda packet manager)

Step #1: Regular Retrieval of Price Data

First, we will define a “prices” class to handle the incoming flow of data. The prices class contains a “get_latest_prices” that retrieves price information from The function regularly calls the list_ticker market endpoint

The list_ticker endpoint returns a list of data fields for cryptocurrency pairs. Examples of price pairs are BTC_USD, BTC_ETH, BTC_ADA, and so on. We can limit the response to a single price pair by passing a single pair as a variable in the API call. However, it is not possible to restrict the response to multiple pairs. This means that we either get data for a single pair or for all pairs. The response contains a list with the following data fields:

Response returned by the API list_tickers operation
Overview of the data fields in the response

The following code maintains a separate dictionary for each cryptocurrency pair. The dictionary contains the name of the cryptocurrency pair and a data frame that contains the history of the price data. Each time the crypto bot receives a new response from the API, it goes through the response, extracts the price data(Price, Volume, etc.) and appends this data to the Data Frame of the respective cryptocurrency pair. Then the data is passed to the preprocessing module.

import pandas as pd
import numpy as np
import json
import requests
import datetime as dt
import logging
import threading
import time
from __future__ import print_function

import tweepy 
import gate_api
from gate_api.exceptions import ApiException, GateApiException
from twitter_secrets import twitter_secrets as ts # place the twitter_secrets file under <User>/anaconda3/Lib

class Prices:
    """Class that uses the gate api to retrieve currency data."""

    def __init__(self, config):
        self._config = config
        self._logger = logging.getLogger(__name__)
        configuration = gate_api.Configuration(host="")
        api_client = gate_api.ApiClient(configuration)
        self._api_instance = gate_api.SpotApi(api_client)
        self._price_history = {}
        self._cont_update_thread = None
        self._stop_cont_update_thread = None
        self._price_history_lock = threading.Lock()

    def get_price_history(self):
        """Returns a dictionary with the price histories for the currencies."""
        return self._price_history, self._price_history_lock

    def get_latest_prices(self):
        """Gets new price data and adds the values to a DataFrame.

        Returns the DataFrame in a dictionary with the currencies as keys."""
        timestamp =
            api_response = self._api_instance.list_tickers()
        except GateApiException as e:
                "Gate api exception, label: %s, message: %s\n" % (e.label, e.message)
            return {}
        except ApiException as e:
            logging.warning("Exception when calling SpotApi->list_tickers: %s\n" % e)
            return {}
        latest_prices = {}
        for response in api_response:
            currency = response.currency_pair
            if "USDT" not in currency or "BEAR" in currency:
            value_dict = {
                "base_volume": pd.to_numeric(response.base_volume),
                "change_percentage": pd.to_numeric(response.change_percentage),
                "etf_leverage": pd.to_numeric(response.etf_leverage),
                "etf_net_value": pd.to_numeric(response.etf_net_value),
                "etf_pre_net_value": pd.to_numeric(response.etf_pre_net_value),
                "etf_pre_timestamp": response.etf_pre_timestamp,
                "high_24h": pd.to_numeric(response.high_24h),
                "highest_bid": pd.to_numeric(response.highest_bid),
                "high_bid": pd.to_numeric(response.highest_bid),
                "last": pd.to_numeric(response.last),
                "low_24h": pd.to_numeric(response.low_24h),
                "lowest_ask": pd.to_numeric(response.lowest_ask),
                "quote_volume": pd.to_numeric(response.quote_volume),
                "timestamp": timestamp,
            latest_prices[currency] = pd.DataFrame(value_dict, index=[1])
        return latest_prices

    def start_cont_update(self):
        self._stop_cont_update_thread = threading.Event()
        self._cont_update_thread = threading.Thread(
        self._cont_update_thread.start()"Started continuous price logging")

    def _cont_update(self, stop_event, lock):
        """Continuously adds new prices to the price history."""
        while not stop_event.is_set():
            start_time = time.time()
            for currency, df in self.get_latest_prices().items():
                if currency in self._price_history.keys():
                    self._price_history[currency] = self._price_history[
                    ].append(df, ignore_index=True)
                    self._price_history[currency] = df
            self._logger.debug("Currency_dfs updated")

    def _wait_before_update(self, start_time):
        elapsed_time = time.time() - start_time
        self._logger.debug(f"Elapsed time: {elapsed_time}")
        if elapsed_time > self._config["price_update_delay"]:
            delay = 0
                #"It took longer to retrieve the price data than the update_delay!"
            delay = self._config["price_update_delay"] - elapsed_time
        self._logger.debug(f"Waiting {delay}s until next update")

Step #2: Calculate Indicator Values

Next, we will define a few functions that process the regular data inflow from and calculate indicator values for the different cryptocurrencies.

First, we need the price as an absolute value so that the bot understands whether the price of a cryptocurrency is moving up or down. However, our signaling logic will primarily work with thresholds on percentage values. In the code below, these are the indicators with a p at the end of the name.

In addition, we will guard against misleading signals by also incorporating moving averages into the signaling logic. For this we need the price history. This is therefore passed in the form of a data frame right away when the function is called. From the data frame we take over further indicators, which give us information about the indicators of the preceding price points. Further indicators are the 24h_low and the 24h_high.

All indicators are calculated separately for each crypto pair, passed to a dictionary, and then passed to the signaling logic. We can then use these indicator values in our signaling logic in the next step.

def calc_indicators(price_history):
    indicators = {}
    indicators_over_all = calc_indicators_over_all(price_history)
    for currency, df in price_history.items():
        if len(df) <= 2:
                f"Skipped '{currency} when calculating indicators due to a lack of information"
        volume = df["base_volume"].iloc[-1]
        last_price = df["last"].iloc[-1]
        moving_avg_price = df["last"].mean()
        moving_average_volume = df["base_volume"].mean()
        moving_average_deviation_percent = np.round(
            div(last_price, moving_avg_price) - 1, 2

        price_before = df["last"].iloc[-2]
        price_delta = last_price - price_before
        price_delta_p = div(price_delta, last_price)
        price_delta_before = price_before - df["last"].iloc[-3]
        price_delta_p_before = div((price_before - df["last"].iloc[-3]), price_before)
        low_24h = df["low_24h"].iloc[-1]
        high_24h = df["high_24h"].iloc[-1]
        low_high_diff_p = div(high_24h - low_24h, low_24h)
        change_percentage = df["change_percentage"].iloc[-1]

        indicator_values = {
            "last_price": last_price,
            "price_before": price_before,
            "volume": volume,
            "moving_avg_price": moving_avg_price,
            "moving_average_volume": moving_average_volume,
            "moving_average_deviation_percent": moving_average_deviation_percent,
            "price_delta_p": price_delta_p,
            "price_delta": price_delta,
            "price_delta_before": price_delta_before,
            "price_delta_p_before": price_delta_p_before,
            "high_24h": high_24h,
            "low_24h": low_24h,
            "low_high_diff_p": low_high_diff_p,
            "change_percentage": change_percentage,
        indicators[currency] = indicator_values
    return indicators

def calc_indicators_over_all(price_history):
    avg_change_p = 0
    for currency, df in price_history.items():
        avg_change_p += df["change_percentage"].iloc[-1]
    nr_of_currencies = len(price_history)
    avg_change_p = div(avg_change_p, nr_of_currencies)
    values = {
        "avg_change_p": avg_change_p,
    return values

def div(dividend, divisor, alt_value=0.0):
    return dividend / divisor if divisor != 0 else alt_value

Step #3: Define the Signaling Logic

Our bot will use a signaling logic that differentiates between the following price signals:

  • A simple up tick: Price_delta_p must be higher than the threshold (10%) in order to trigger.
  • A simple down tick: Price_delta_p must be lower than the threshold (10%) in order to trigger.
  • The bot does also report on new 24 hour lows and highs
  • Another event on which the bot reports, is when an up or down price trend begins to accelerate or slows down.
  • The bot reports, when a price performs a trend reversal (pullback and recovery)

Be aware that the price_delta_p measures the percentage deviation from the previous price point. Thus, the signaling logic that our bot has in place is very dependent on the interval in which the bots requests fresh price data. It obvious that shorter time intervals will have a lower chance to trigger, because larger changes typically occur over a longer timeframe. For more details regarding the signaling logic, please view the code below.

def check_signal(currency, indicators, cs_config):
    ind = indicators[currency]
    signal = ''
    if (ind['moving_avg_price'] > 0
            and ind['last_price'] > 0.0
            and abs(ind['price_delta']) > 0.0
            and abs(ind['price_delta_p']) >= cs_config["delta_threshold_p"]
            and ind['volume'] > 0
        # up
        if ind['price_delta'] > 0:
            movement_type = 'up +'
            if abs(ind['price_delta_p_before']) > cs_config["delta_threshold_p"]:
                if ind['price_delta_before'] <= 0:
                    movement_type = 'recovery from ' + str(ind['price_before']) + ' to ' + str(ind['last_price'])
                    if ind['price_delta_p'] * (1-cs_config["delta_threshold_p"]) > ind['price_delta_p_before']:
                        movement_type = 'upward trend accelerates +'
                    elif ind['price_delta_p'] < ind['price_delta_p_before'] * (1-cs_config["delta_threshold_p"]):
                        movement_type = 'upward trend slows down +'
                    elif ind['price_delta_p'] * (1+cs_config["delta_threshold_p"]) >= ind['price_delta_p_before'] >= ind['price_delta_p'] * (1-cs_config["delta_threshold_p"]):
                        movement_type = 'upward trend continues +'
        # down
        elif ind['price_delta'] < 0:
            movement_type = 'down '
            if abs(ind['price_delta_p_before']) > cs_config["delta_threshold_p"]:
                if ind['price_delta_before'] > 0:
                    movement_type = 'pullback from ' + str(ind['price_before']) + ' to ' + str(ind['last_price'])
                    if ind['price_delta_p'] * (1-cs_config["delta_threshold_p"]) > ind['price_delta_p_before']:
                        movement_type = 'down trend accelerates '
                    elif ind['price_delta_p'] * (1+cs_config["delta_threshold_p"]) >= ind['price_delta_p_before'] >= ind['price_delta_p'] * (1-cs_config["delta_threshold_p"]):
                        movement_type = 'down trend continues '
                    elif ind['price_delta_p'] < ind['price_delta_p_before'] * (1+cs_config["delta_threshold_p"]):
                        movement_type = 'downward trend slows down '

        signal = get_signal_log(movement_type, currency, ind['price_delta_p'], ind['last_price'],
                                ind['moving_avg_price'], ind['volume'], ind['price_delta'], ind['change_percentage'],
                                ind['high_24h'], ind['low_24h'], ind['low_high_diff_p'])

        check_24h_peak(currency, ind['last_price'], ind['low_24h'], ind['high_24h'])

    return signal
    # trade_signal

def check_24h_peak(currency, last_price, low_24h, high_24h):
    if last_price < low_24h:
        print(currency + ' new 24h low $' + str(last_price))
    elif last_price > high_24h:
        print(currency + ' new 24h high $' + str(last_price))

def get_signal_log(movement_type, currency, price_delta_p, last_price, moving_avg_price, volume, price_delta,
                   daily_up_p, high_24h, low_24h, low_high_diff_p):
    signal = f'{currency} {movement_type} ' \
             f'{np.round(price_delta_p * 100, 5)}% ' \
             f'MA:${np.round(moving_avg_price, 6)} ' \
             f'last_price:${np.round(last_price, 6)} ' \
             f'price delta:{np.round(price_delta, 6)} ' \
             f'volume:${np.round(volume, 1)} ' \
             f'daily_change:{np.round(daily_up_p, 2)}% ' \
             f'high_24h:${high_24h} ' \
             f'low_24h:${low_24h} ' \
             f'low_high_diff_p:{np.round(low_high_diff_p * 100, 2)}%'
    return signal

Step #4: Send Tweets via Twitter

Next, we define a simple function that calls the Twitter API and tweets our price signal. Because the Twitter API requires authentication, you will need to provide the API authentication credentials from a valid Twitter developer account.

It’s a best practice to not store the API credentials directly in code. Still not perfect but slightly better is to store the data in a separate python file (for example, called “twitter_secrets”) that you put into your package folder (for example under /anaconda3/Lib), from where you can import it directly into your code.

# Twitter Consumer API keys
CONSUMER_KEY    = "api123"

# Twitter Access token & access token secret
ACCESS_TOKEN    = "api123"
ACCESS_SECRET   = "api123"

BEARER_TOKEN = "api123"

class TwitterSecrets:
    """Class that holds Twitter Secrets"""

    def __init__(self):
        # Tests if keys are present
        for key, secret in self.__dict__.items():
            assert secret != "", f"Please provide a valid secret for: {key}"

twitter_secrets = TwitterSecrets()

Once, you have imported the file, you can then load the API credentials from the file in the following way:

consumer_key = ts.CONSUMER_KEY
consumer_secret = ts.CONSUMER_SECRET
access_token = ts.ACCESS_TOKEN
access_secret = ts.ACCESS_SECRET

### Print API Auth Data (leave disabled for security reasons)
# print(f'consumer_key: {consumer_key}')
# print(f'consumer_secret: {consumer_secret}')
# print(f'access_token: {access_token}')
# print(f'access_secret: {access_token}')

#authenticating to access the twitter API

def send_pricechange_tweet(signal):
    api.update_status(f"{signal} \n {relataly_url}")

Step #5 Starting the Crypto Signal Bot

Finally, we can hit the start button of our crypto signal bot. But before we do this, take a look at some configuration options of the bot.

  • CYCLE_DELAY is the standard interval in seconds in which the bot will call the API.
  • CURRENCY_PAIR is another API parameter, which can be used to limit the cryptocurrency pairs to a specific pair. In the standard setting, the bot will scan the whole market including all USDT pairs.
  • TWITTER_ACTIVE defines, whether signals are posted on Twitter. Be aware that if you enable it, your bot may instantly report any signal on your Twitter account.
  • RUNS defines the max number of prices that will be retrieved before the bot stops.

Now, let’s test the bot:

RUNS = 50 # the bot will stop after 50 price points
CYCLE_DELAY = 20 # the interval for checking the data and retrieving another price point
CURRENCY_PAIR = "" # the bot will retrieve data for all currency pairs listed on
PRICES_CONFIG = {"price_update_delay": 20}

    "moving_avg_threshold_down_p": 0.10,
    "moving_avg_threshold_up_p": 0.10,
    "delta_threshold_p": 0.07,
    'enable_twitter': TWITTER_ACTIVE,

if __name__ == "__main__":
        level=logging.INFO, format="\033[02m%(asctime)s %(levelname)s: %(message)s"
    logger = logging.getLogger(__name__)
    prices = Prices(PRICES_CONFIG)
    currency_dfs = {}"Crypto bot is starting - please wait")"Collecting crypto data from for {EVAL_PRICES_DELAY}s")
    time.sleep(EVAL_PRICES_DELAY)"\n<< Crypto signal bot started :-) >>")"<< Checking prices every {CYCLE_DELAY} seconds >>")"Now checking for signals - please wait\n")
    for i in range(RUNS):
        price_history, lock = prices.get_price_history()
        indicators = calc_indicators(price_history)
        for currency in indicators.keys():
            if not indicators[currency]:
            signal = check_signal(
            if signal:
                if CHECK_SIGNAL_CONFIG['enable_twitter']:
                    print('send via twitter')

And this is what the tweets will look like on Twitter:


Congratulations, in this tutorial you have learned to develop a Twitter Bot with Python. Now, when you run your Twitter bot, it will regularly fetch price data about cryptocurrencies from and look for price signals. As soon as. the bot detects a signal, it will post about it on Twitter.

The signaling logic is currently pretty basic, but you can easily experiment with the logic and create your own signals. For example, you can define signals based on changes in volume, or based on price volatility. Have fun trying it out!

Let us know in the comments if this article helped you or if you have any questions. We create these articles in our spare time and appreciate feedback and exchange with readers. Btw. we are currently working on an advanced version of the bot that uses Machine Learning. Stay tuned for more!


  • Hi, I am Florian, a Zurich-based consultant for AI and Data. Since the completion of my Ph.D. in 2017, I have been working on the design and implementation of ML use cases in the Swiss financial sector. I started this blog in 2020 with the goal in mind to share my experiences and create a place where you can find key concepts of machine learning and materials that will allow you to kick-start your own Python projects.

  • Hi, I am a student at the Technical University of Munich and currently pursuing a Masters degree in Electrical Engineering and Information Technology. I am very passionate about Machine Learning, Software Development, and Signal Processing.

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