Einstein_trade_websocket_bot

[kalihatreese]

import pandas as pd
import matplotlib.pyplot as plt
import requests
import websocket

def on_message(ws, message):
# Process the received message
data = json.loads(message)
# Generate trading signals based on the latest market data
make_informed_trading_decision(data)

def on_error(ws, error):
print(f"Error: {error}")

def on_close(ws, code, reason):
print(f"Connection closed: {code} ({reason})")

def on_open(ws):
print("Connection opened")

def make_informed_trading_decision(data):
# Analyze the received market data and generate trading signals
# /storage/emulated/0/Download/BTC_usd.csv

def fibonacci(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fibonacci(n - 1) + fibonacci(n - 2)

def retracement_levels(start, end, retrace_percentages):
levels = []
for percentage in retrace_percentages:
level = end - percentage * (end - start)
levels.append(level)
return levels

def get_historical_data(symbol):
data = pd.read_csv('data.csv')
data.set_index('Date', inplace=True)
return data[symbol]

def plot_data_with_retracement_levels(symbol, retrace_percentages):
data = get_historical_data(symbol)
data.plot()
def calculate_retracement_levels(start, end, retrace_percentages):
levels = []
for percentage in retrace_percentages:
level = end - percentage * (end - start)
levels.append(level)
return levels
retracement_levels = calculate_retracement_levels(data.iloc[0]['Close'], data.iloc[-1]['Close'], retrace_percentages)
for level in retracement_levels:
plt.axhline(y=level, color='r', linestyle='--', label=f'{level:.2f}')

plt.title(f'Historical Data for {symbol} with Fibonacci Retracement Levels')
plt.legend()
plt.show()

def liquid_nero_networking(symbol, start_date, end_date):
# Access the Liquid Nero API to retrieve relevant social sentiment data
api_key = 'YOUR_API_KEY'
endpoint = f'https://api.liquidnero.com/sentiment/{symbol}?start={start_date}&end={end_date}'
headers = {'Authorization': f'Bearer {api_key}'}

response = requests.get(endpoint, headers=headers)
if response.status_code == 200:
    sentiment_data = response.json()
    # Analyze sentiment data and identify potential trading signals
    # ...

def fundamental_analysis(symbol):
# Analyze financial statements, company news, and industry trends
# ...

if name == 'main':
symbol = 'BTC-USD'

# Retrieve historical data and calculate Fibonacci retracement levels
data = get_historical_data(symbol)
retracement_levels = calculate_retracement_levels(data.iloc[0]['Close'], data.iloc[-1]['Close'], [0.236, 0.382, 0.5])

plot_data_with_retracement_levels(symbol, retracement_levels)
liquid_nero_networking(symbol, data.index[0], data.index[-1])
fundamental_analysis(symbol)

# Connect to the WebSocket stream and receive real-time market data
websocket.enableTrace(True)
ws = websocket.WebSocketApp("wss://api.websocket.com/v1/stream", on_message=on_message, on_error=on_error, on_close=on_close)
ws.run_forever()