From d499d8242fde8074ddc8fa1842863e1e31d8e7d6 Mon Sep 17 00:00:00 2001 From: breezy1245 Date: Wed, 24 Jul 2024 23:10:14 +0300 Subject: [PATCH] Create 48H bot pro v1.xml 48 bot pro v1.xml --- 48H bot pro v1.xml | 174 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 174 insertions(+) create mode 100644 48H bot pro v1.xml diff --git a/48H bot pro v1.xml b/48H bot pro v1.xml new file mode 100644 index 0000000..dd6390a --- /dev/null +++ b/48H bot pro v1.xml @@ -0,0 +1,174 @@ +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) + + + + + + Davis Pro 1 Bot + + + +
+

Market Prediction

+ +

+ + + + + + + + + + + + + + +
TimestampCloseRSIBollinger HighBollinger LowSMACandle Type
+
+ + + +python trading_bot.py