we# Eienstein-trade trading platform

The final Einstein -Trade - Final

name: CI

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()

    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()# Controls when the workflow will run
on:
  # Triggers the workflow on push or pull request events but only for the "main" branch
  push:
    branches: ["main"]
  pull_request:
    branches: ["main"]

  # Allows you to run this workflow manually from the Actions tab
  workflow_dispatch:

# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
  # This workflow contains a single job called "build"
  build:
    # The type of runner that the job will run on
    runs-on: ubuntu-latest

    # Steps represent a sequence of tasks that will be executed as part of the job
    steps:
      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
      - uses: actions/checkout@v3

      # Set the account_id as an environment variable
      - name: Set account_id
        run: echo "ACCOUNT_ID=ReeseReese23" >> $GITHUB_ENV

      # Install Python
      - name: Setup Python
        uses: actions/setup-python@v2
        with:
          python-version: "3.x" # Specify the Python version you need

      # Install required Python packages
      - name: Install dependencies
        run: |
          python -m pip install -U pip
          pip install requests

      # Run the Python script for trading data analysis
      - name: Run Trading Data Analysis Script
        run: |
          python - <<EOF
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Define functions for trading algorithm
def get_data(filename):
    """https://drive.google.com/file/d/1OVW7aVDKOKgZWrkHnFEoN8NTBB_BGerJ/view?usp=sharing"""
    df = pd.read_csv(BTC-USD.csv)
    df['Date'] = pd.to_datetime(df['Date'])
    return df

def plot_data(df):
    """Plots the data."""
    plt.plot(df['Date'], df['Close'])
    plt.show()

def train_model(df):
    """Trains a linear regression model on the data."""
    X = df['Date'].values.reshape(-1, 1)
    y = df['Close'].values

    model = LinearRegression()
    model.fit(X, y)

    return model

def predict_prices(model, start_date, end_date):
    """Predicts the prices for the specified dates."""
    X = np.array([start_date, end_date]).reshape(-1, 1)
    predicted_prices = model.predict(X)

    return predicted_prices

# Implement Liquid Neural Network (LNN) integration here

if __name__ == '__main__':
    """The main function."""
    filename = 'data.csv'
    df = get_data(filename)
    plot_data(df)
    model = train_model(df)
    start_date = pd.to_datetime('2023-06-19')
    end_date = pd.to_datetime('2023-06-26')
    predicted_prices = predict_prices(model, start_date, end_date)
    print(predicted_prices)

# Integrate concepts from Einstein Trade summary
print("Einstein Trade: A Simple Yet Powerful Trading Algorithm")
print("Three months ago, I coded a trading algorithm called Einstein Trade with the help of Bard.")
print("The algorithm is based on the Fibonacci sequence, which is a well-known mathematical pattern that has been shown to be effective in trading.")
print("The algorithm works by identifying Fibonacci retracements, which are pullbacks in the price of a security that occur after a strong move.")
print("We have backtested the algorithm on historical data and found that it has generated positive returns over a long period of time.")
print("However, it is important to note that past performance is not a guarantee of future results.")
print("We are still in the early stages of developing Einstein Trade, but we are excited about the potential of this algorithm to help traders make profitable trades.")

# Additional features and explanations
print("\nAdditional Features and Explanations:")
print("1. The Fibonacci Sequence:")
print("   - The Fibonacci sequence is a series of numbers where each number is the sum of the two previous numbers.")
print("   - The sequence starts with 0 and 1, and the next few numbers are 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, and so on.")
print("   - The Fibonacci sequence has been found to be present in many natural phenomena and is used in trading to identify support and resistance levels.")
print("2. Fibonacci Retracements:")
print("   - Fibonacci retracements are pullbacks in the price of a security that occur after a strong move.")
print("   - Common retracement levels include 23.6%, 38.2%, and 61.8%, used as support and resistance levels.")
print("3. How Einstein Trade Works:")
print("   - Einstein Trade uses the Fibonacci sequence to identify Fibonacci retracements.")
print("   - When the price of a security reaches a Fibonacci retracement level, the algorithm triggers a buy or sell signal.")
print("   - The algorithm also considers other factors, such as volatility and market sentiment, to generate high-probability signals.")

# Liquid Neural Network (LNN) Integration
print("\nLiquid Neural Network (LNN) Integration:")
print("Integrate Liquid Neural Network (LNN) functionality into your code here.")
print("LNNs are designed to be adaptable and efficient neural networks inspired by the brain's neural networks.")

# Additional explanations and contact information
print("\nAdditional Explanations and Contact Information:")
print("Einstein Trade is a simple yet powerful trading algorithm based on the Fibonacci sequence.")
print("It has been backtested on historical data, showing positive returns over a long period, but remember that past performance doesn't guarantee future results.")
print("We are continuously developing Einstein Trade and adding new features, such as backtesting under different market conditions.")
print("If you want to learn more about Einstein Trade, please visit our website or contact us.")
print("Update:")
print("Since this blog post was published, we have continued to develop Einstein Trade and have made some improvements to the algorithm.")
print("We have also added new features, such as the ability to backtest the algorithm on different market conditions.")
print("We are confident that Einstein Trade is a valuable tool for traders, and we are excited to continue developing it.")

EOF