Real-time RSI Trading Bot of Bitcoin using Talib Library and Binance WebSocket Client

Automated trading has become increasingly popular in recent years, thanks to advancements in technology and the accessibility of financial markets. A trading bot is an automated trading system that is programmed to execute trades based on a set of predefined rules. In this article, we will build a real-time trading bot using the Binance WebSocket client and the RSI (Relative Strength Index) strategy using Talib in Python.

Binance is a cryptocurrency exchange that provides a WebSocket API for real-time data streaming. The WebSocket client allows us to subscribe to real-time price and volume data for any cryptocurrency listed on Binance. The RSI strategy is a popular technical analysis tool used by traders to identify overbought and oversold conditions in the market.

We will follow these steps to build the trading bot:

Install and Import Talib

Install and Import Websocket Client

Import other Libraries

Connect to Binance Websocket

Trading Strategy Parameters

Paper Trading Simulation Function

Fetch Live Data using Websocket Client

RSI Strategy Using Ta-lib

Generate Signal

Start Trading

Let's get started.

Step 1: Install and Import Talib

Talib (Technical Analysis Library) is a popular library for technical analysis. It provides a wide range of technical indicators, including the RSI (Relative Strength Index). To use Talib, we need to install it using pip and import it in our Python code:

# Install and Import Talib

!wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz

!tar -xzvf ta-lib-0.4.0-src.tar.gz

%cd ta-lib

!./configure --prefix=/usr

!make

!make install

!pip install Ta-Lib

import talib

Step 2: Install and Import WebSocket Client

The WebSocket protocol allows for real-time communication between a client and a server. In our case, we will use the Binance WebSocket client to fetch real-time price data. We can install the WebSocket client using pip and import it in our Python code:

# Install and Import Websocket Client

pip install websocket-client

import websocket

Step 3: Import other Libraries

Next, we will import other libraries that we will use in our trading bot. We will use the time module to introduce delays in our code, and the json module to parse the JSON data received from the WebSocket API.

# Import other Libraries

import talib

import requests

import pandas as pd

import numpy as np

import json

import ssl

Step 4: Connect to Binance Websocket

In this step, we will connect to the Binance WebSocket API using the WebSocket client library. We will subscribe to the kline_1m endpoint, which provides 1-minute candlestick data for the Bitcoin/USDT trading pair.

# Connect to Binance Websocket

cc= 'btcusd'

interval = "1m" 

socket = f"wss://stream.binance.com:9443/ws/{cc}t@kline_{interval}"

Step 5: Trading Strategy Parameters

In this step, we will define the trading strategy parameters. 

# Trading Strategy Parameters

amount=1000000

money_remaining = 1000000

portfolio = 0

investment, closes, highs, lows = [], [], [], []

buy_price = []

sell_price = []

rsi_signal = []

signal = 0

Step 6: Paper Trading Simulation Function

Before we start trading with real money, we will first simulate trading using a paper trading function. 

# Paper Trading Simulation Function

def buy(allocated_money, price):

    global closes,highs,lows, money_remaining, portfolio, investment, signal

    quantity = allocated_money  / price

    money_remaining -= quantity*price

    portfolio += quantity 

    if investment == []:

      investment.append(allocated_money)

    else:

      investment.append(allocated_money)

      investment[-1] += investment[-2]

def sell(allocated_money, price):

    global closes,highs,lows, money_remaining, portfolio, investment, signal

    quantity = allocated_money  / price

    money_remaining += quantity*price

    portfolio -= quantity 

    investment.append(-allocated_money)

    investment[-1] += investment[-2]

Step 7: Fetch Live Data using Websocket Client

Next, we will modify the on_message callback function to receive live price data from the WebSocket API and store it in a variable closes. 

# Fetch Live Data using Websocket Client

def on_close(ws):

  print("Close")

def on_message(ws, message):

  global closes,highs,lows, money_remaining, portfolio, investment, signal, position

  json_message = json.loads(message)

  cs = json_message["k"]

  candle_closed, close, high, low = cs["x"], cs["c"],cs["h"],cs["l"]

  if candle_closed:

    closes.append(float(close))

    highs.append(float(high))

    lows.append(float(low))

    last_price = closes[-1]

    print(f'Closes : {closes[-5:]}')

Step 8: RSI Strategy Using Ta-lib

We will define our RSI trading strategy using the RSI values that we will generate using Talib.

# RSI Strategy Using Ta-lib

    rsi = talib.RSI(np.array(closes), timeperiod=3)

    last_rsi=round(rsi[-1],2)

    print(last_rsi)

    if (rsi[-1] > 40 and rsi < 40).any():

              if signal != 1:

                buy_price.append(closes)

                sell_price.append(np.nan)

                signal = 1

                rsi_signal.append(signal)

              else:

                buy_price.append(np.nan)

                sell_price.append(np.nan)

                rsi_signal.append(0)

    elif (rsi[-1] < 60 and rsi > 60).any():

              if signal != -1:

                buy_price.append(np.nan)

                sell_price.append(closes)

                signal = -1

                rsi_signal.append(signal)

              else:

                buy_price.append(np.nan)

                sell_price.append(np.nan)

                rsi_signal.append(0)

    else:

                buy_price.append(np.nan)

                sell_price.append(np.nan)

                rsi_signal.append(0)

    print(rsi_signal[-5:])

Step 9: Generate Signal

We will generate Trading Signal here.

# Generate Signal and Trade

    if rsi_signal[-1] == 1:

           buy(250000, last_price)

           print(f'(Bitcoin Bought ="{portfolio}")')

    elif rsi_signal[-1] == -1:

           sell(250000, last_price)

           print(f'(Bitcoin Sold ="{portfolio}")')

    else:

           print("No Trade")

wsapp = websocket.WebSocketApp(socket, on_message = on_message, on_close = on_close)

Step 10: Start Trading

Finally, our Real-Time Trading Bot is ready

# Real Time Trading Bot

wsapp.run_forever()

Conclusion

Here we built a real-time trading bot using the Binance WebSocket client and the RSI strategy using Talib in Python. The trading bot monitors the price of Bitcoin in real-time and executes trades based on the RSI value.

There are several ways to improve this trading bot. For example, we can add more technical indicators, such as moving averages or MACD, to improve the trading signals. We can also implement a stop-loss and take-profit feature to manage the risk of the trades.