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breezy1245/README.mdPRO V1 
pip install pandas numpy ta requests flask
import pandas as pd
import numpy as np
import requests
from ta.momentum import RSIIndicator
from ta.volatility import BollingerBands
from ta.trend import SMAIndicator
from flask import Flask, request, jsonify
import json

app = Flask(__name__)

# Configuration
API_URL = "https://api.example.com/market_data"  # Replace with actual API endpoint
RSI_PERIOD = 14
BB_WINDOW = 20
BB_STD_DEV = 2
SMA_PERIOD = 50

# Fetch market data
def fetch_market_data():
    try:
        response = requests.get(API_URL)
        data = response.json()
        df = pd.DataFrame(data)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        df.set_index('timestamp', inplace=True)
        return df
    except Exception as e:
        print(f"Error fetching market data: {e}")
        return pd.DataFrame()

# Analyze market data
def analyze_market_data(df):
    df['rsi'] = RSIIndicator(df['close'], window=RSI_PERIOD).rsi()
    bb = BollingerBands(df['close'], window=BB_WINDOW, window_dev=BB_STD_DEV)
    df['bb_high'] = bb.bollinger_hband()
    df['bb_low'] = bb.bollinger_lband()
    df['sma'] = SMAIndicator(df['close'], window=SMA_PERIOD).sma_indicator()
    return df

# Candlestick pattern recognition
def recognize_candlestick_patterns(df):
    df['candle_type'] = np.where(df['close'] > df['open'], 'bullish', 'bearish')
    df['candle_body'] = abs(df['close'] - df['open'])
    df['candle_wick'] = df[['high', 'low']].apply(lambda x: x[0] - x[1], axis=1)
    return df

# Make a prediction
def make_prediction(df):
    last_row = df.iloc[-1]
    prediction = "hold"
    
    # Simple prediction logic
    if last_row['rsi'] < 30 and last_row['close'] < last_row['bb_low']:
        prediction = "over 2"
    elif last_row['rsi'] > 70 and last_row['close'] > last_row['bb_high']:
        prediction = "under 7"
    elif last_row['close'] > last_row['sma']:
        prediction = "over 3"
    elif last_row['close'] < last_row['sma']:
        prediction = "under 6"
    
    return prediction

@app.route('/predict', methods=['GET'])
def predict():
    df = fetch_market_data()
    if df.empty:
        return jsonify({'error': 'Unable to fetch market data'})
    
    df = analyze_market_data(df)
    df = recognize_candlestick_patterns(df)
    prediction = make_prediction(df)
    
    result = {
        'prediction': prediction,
        'analysis': df.tail().to_dict(orient='records'),
        'owner': 'davis pro 1 bot'
    }
    
    return jsonify(result)

if __name__ == '__main__':
    app.run(debug=True)
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Davis Pro 1 Bot</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            margin: 50px;
            background-color: #f4f4f9;
        }
        .container {
            max-width: 800px;
            margin: 0 auto;
            padding: 20px;
            background: #fff;
            box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
        }
        .prediction {
            font-size: 24px;
            font-weight: bold;
            margin-top: 20px;
        }
        table {
            width: 100%;
            border-collapse: collapse;
            margin-top: 20px;
        }
        table, th, td {
            border: 1px solid #ddd;
        }
        th, td {
            padding: 8px;
            text-align: left;
        }
        th {
            background-color: #f2f2f2;
        }
    </style>
</head>
<body>
    <div class="container">
        <h1>Market Prediction</h1>
        <button onclick="getPrediction()">Get Prediction</button>
        <p class="prediction" id="prediction"></p>
        <table id="analysis-table">
            <thead>
                <tr>
                    <th>Timestamp</th>
                    <th>Close</th>
                    <th>RSI</th>
                    <th>Bollinger High</th>
                    <th>Bollinger Low</th>
                    <th>SMA</th>
                    <th>Candle Type</th>
                </tr>
            </thead>
            <tbody>
            </tbody>
        </table>
    </div>
    <script>
        async function getPrediction() {
            try {
                const response = await fetch('/predict');
                const data = await response.json();
                if (data.error) {
                    document.getElementById('prediction').innerText = `Error: ${data.error}`;
                    return;
                }
                document.getElementById('prediction').innerText = `Prediction: ${data.prediction} (${data.owner})`;
                const tableBody = document.getElementById('analysis-table').getElementsByTagName('tbody')[0];
                tableBody.innerHTML = '';
                data.analysis.forEach(row => {
                    const newRow = tableBody.insertRow();
                    Object.values(row).forEach(cell => {
                        const newCell = newRow.insertCell();
                        newCell.innerText = cell;
                    });
                });
            } catch (error) {
                console.error('Error fetching prediction:', error);
            }
        }
    </script>
</body>
</html>
python trading_bot.py

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  1. breezy1245 breezy1245 Public

    Config files for my GitHub profile.

    2