Code: Select all
import ccxt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import time
import smtplib
from email.mime.text import MIMEText
import schedule
from datetime import datetime
import random
# Configuration
config = {
'exchange': 'kraken', # Example exchange, replace with your choice
'api_key': 'your_api_key_here',
'api_secret': 'your_api_secret_here',
'email': 'your_email_here',
'email_password': 'your_email_password_here',
'cryptos': {
'DOGE/USD': {'enabled': True, 'patterns': []},
'TRUMP/USD': {'enabled': True, 'patterns': []},
},
'features': {
'doomsday_check': True,
'auto_trade': True,
'min_max_prediction': True,
'learning_confidence': True,
'manual_column': False,
'test_mode': False,
'crypto_scrape': True,
'pattern_recognition': True # New feature flag for pattern recognition
},
'learning_threshold': 0.8,
'small_amount': 0.001,
'max_cryptos': 20,
'financial_levels': [50, 1000],
'doomsday_reserve': 1000,
'min_cryptos_to_watch': 3 # Minimum number of cryptocurrencies to watch at once
}
# Initialize Exchange
exchange = getattr(ccxt, config['exchange'])({
'apiKey': config['api_key'],
'secret': config['api_secret'],
})
# Function to fetch current price
def fetch_price(symbol):
try:
ticker = exchange.fetch_ticker(symbol)
return ticker['last']
except Exception as e:
print(f"Failed to fetch price for {symbol}: {e}")
return None
# Function to send email
def send_email(subject, body):
try:
msg = MIMEText(body)
msg['Subject'] = subject
msg['From'] = config['email']
msg['To'] = config['email']
with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp_server:
smtp_server.login(config['email'], config['email_password'])
smtp_server.sendmail(config['email'], config['email'], msg.as_string())
except Exception as e:
print(f"Failed to send email: {e}")
# Function to scrape all available cryptocurrencies from the exchange
def crypto_scrape():
if not config['features']['crypto_scrape']:
return {}
try:
markets = exchange.fetch_markets()
all_cryptos = {market['symbol']: {'enabled': False, 'patterns': []} for market in markets if 'USD' in market['symbol']}
config['cryptos'].update(all_cryptos)
return all_cryptos
except Exception as e:
print(f"Error in crypto_scrape: {e}")
return {}
# Simple pattern recognition function
def recognize_patterns(symbol, historical_data):
if not config['features']['pattern_recognition']:
return []
# Placeholder for pattern recognition logic
# Here you might implement actual pattern recognition algorithms like moving averages, RSI, etc.
# For simplicity, we'll just look for a basic pattern: if the price has increased for the last 3 periods
if len(historical_data) < 3:
return []
price_changes = historical_data['close'].pct_change().dropna()
pattern = 'Bullish' if all(change > 0 for change in price_changes[-3:]) else 'Bearish'
return [pattern]
# Function to learn from a subset of cryptocurrencies
def learn_patterns():
# Select a random subset of cryptocurrencies to watch
available_cryptos = [symbol for symbol, details in config['cryptos'].items() if details['enabled']]
cryptos_to_watch = random.sample(available_cryptos, min(len(available_cryptos), max(config['min_cryptos_to_watch'], config['max_cryptos'])))
for crypto in cryptos_to_watch:
try:
# Fetch historical data - placeholder, needs real implementation
historical_data = pd.DataFrame({
'close': [fetch_price(crypto) for _ in range(10)] # Example: last 10 prices
})
patterns = recognize_patterns(crypto, historical_data)
config['cryptos'][crypto]['patterns'].extend(patterns)
# Here you would implement logic to act on these patterns if needed
print(f"Learned patterns for {crypto}: {patterns}")
except Exception as e:
print(f"Could not learn patterns for {crypto}: {e}")
# Function to handle the "Down the Drain" contingency
def down_the_drain_contingency():
if therefore_i_am():
send_email("Therefore I Am - Down the Drain Contingency Activated", "A catastrophic event has been detected. All trading paused for verification. Waiting 10 days for market stabilization.")
# Pause all trading activities
for crypto in config['cryptos']:
config['cryptos'][crypto]['auto_trade'] = False
# Daily morning update function
def send_daily_update():
status = "Operational" if all(crypto['auto_trade'] for crypto in config['cryptos'].values()) else "Contingency Mode Active"
performance = "Performance: TBD"
recommendations = ["Check the crypto_scrape function for new additions to trading list."]
market_conditions = "Market is stable with minor fluctuations."
body = f"""Good morning,
I am alive, I am working hard.
Current Status:
{status}
{performance}
Market Conditions:
{market_conditions}
Recommendations for Improvement:
{"\n".join(recommendations)}
Best,
Your Trading Bot
"""
send_email("Daily Morning Update", body)
# Function to verify if all placeholders have data
def verify_placeholders():
for crypto, details in config['cryptos'].items():
if 'patterns' not in details:
print(f"Warning: {crypto} missing patterns list")
return False
return True
# Placeholder for catastrophic scenario detection
def therefore_i_am():
return False
# Main loop for running scheduled tasks
def run_bot():
while True:
schedule.run_pending()
if config['features']['crypto_scrape']:
crypto_scrape()
if not verify_placeholders():
print("Some placeholders are missing data. Skipping trading actions.")
else:
learn_patterns() # Learn from a subset of cryptocurrencies
down_the_drain_contingency() # Check if we need to go into contingency mode
time.sleep(60) # Check every minute for scheduled tasks
# Schedule the daily update to run at 8 AM every day
schedule.every().day.at("08:00").do(send_daily_update)
if __name__ == "__main__":
run_bot()