Geederiv bot

[geeabby254]

Save the provided script into a Python file

script_content = """
import pandas as pd
import numpy as np
import requests
import time
import logging

Set up logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()

Define the API endpoints and keys

api_url = 'https://api.broker.com' # Replace with the actual broker's API URL
api_key = 'YOUR_API_KEY' # Replace with your API key

RSI Calculation

def calculate_rsi(data, window=14):
delta = data['close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
rs = gain / loss
rsi = 100 - (100 / (1 + rs))
return rsi

MACD Calculation

def calculate_macd(data, short_window=12, long_window=26, signal_window=9):
short_ema = data['close'].ewm(span=short_window, adjust=False).mean()
long_ema = data['close'].ewm(span=long_window, adjust=False).mean()
macd = short_ema - long_ema
signal = macd.ewm(span=signal_window, adjust=False).mean()
macd_diff = macd - signal
return macd, signal, macd_diff

Fetch historical data

def fetch_data(api_url, asset, duration, api_key):
try:
response = requests.get(f'{api_url}/historical', params={'asset': asset, 'duration': duration}, headers={'Authorization': f'Bearer {api_key}'})
response.raise_for_status()
data = response.json()
df = pd.DataFrame(data)
df['rsi'] = calculate_rsi(df)
df['macd'], df['signal'], df['macd_diff'] = calculate_macd(df)
return df
except Exception as e:
logger.error(f"Error fetching data: {e}")
return None

Trading Logic

def trend_reversal_strategy(api_url, api_key, initial_stake, risk_reward_ratio, duration):
balance = 1000 # Starting balance for simulation purposes
stake = initial_stake
total_profit = 0
total_loss = 0

while total_profit < 100 and total_loss < 50:  # Example thresholds
    data = fetch_data(api_url, 'Volatility 50 (1s) Index', duration, api_key)
    if data is None:
        continue

    latest_data = data.iloc[-1]

    # Determine trade signal
    if latest_data['rsi'] < 30 and latest_data['macd_diff'] > 0:
        trade_signal = 'buy'
    elif latest_data['rsi'] > 70 and latest_data['macd_diff'] < 0]:
        trade_signal = 'sell'
    else:
        trade_signal = 'hold'

    if trade_signal != 'hold':
        try:
            # Place trade
            response = requests.post(
                f'{api_url}/trade',
                headers={'Authorization': f'Bearer {api_key}'},
                json={
                    'asset': 'Volatility 50 (1s) Index',
                    'trade_type': trade_signal,
                    'amount': stake,
                    'duration': 5  # 5 Ticks
                }
            )
            response.raise_for_status()
            trade_result = response.json()

            # Process trade outcome
            if trade_result['outcome'] == 'win':
                profit = stake * risk_reward_ratio
                total_profit += profit
                stake = initial_stake  # Reset stake after a win
                logger.info(f'Win: +{profit} USD, Total Profit: {total_profit} USD')
            else:
                loss = stake
                total_loss += loss
                stake *= risk_reward_ratio  # Increase stake proportionally to the risk-reward ratio
                logger.info(f'Loss: -{loss} USD, Total Loss: {total_loss} USD, Next Stake: {stake} USD')

        except Exception as e:
            logger.error(f"Error placing trade: {e}")

    time.sleep(1)  # Wait for 1 second before the next trade

    # Check loss threshold to stop the strategy
    if total_loss >= 50:
        logger.warning('Loss threshold reached, stopping the strategy.')
        break

if total_profit >= 100:
    logger.info('Profit threshold reached, stopping the strategy.')

return total_profit, total_loss

Run the Trend Reversal strategy

initial_stake = 1
risk_reward_ratio = 3 # 1:3 risk-reward ratio
duration = 1 # Duration of 1 tick for data fetching

trend_reversal_strategy(api_url, api_key, initial_stake, risk_reward_ratio, duration)
"""

Save to file

file_path = "/mnt/data/trend_reversal_strategy.py"
with open(file_path, "w") as file:
file.write(script_content)

file_path
blank.yml (1).txt

[pyscripter]

This is not a valid issue report. I am not sure what the P4D issue is. Even the issue title is confusing.
If you need support use the support forum.