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🐍 Python for Financial AI

Essential Python skills for quantitative analysis, backtesting, and predictive modeling.

📘 10 Lessons • Beginner

🧠 Athena’s Wisdom: The Complete Odyssey

Embark on an epic journey where ancient financial wisdom meets cutting-edge AI.

🧩 VI Lessons

🤖 Building AI Trading Bots

From basic algorithms to machine learning models, create your own automated trading systems.

📚 8 Lessons • Advanced

Crypto Trading Bot Skeleton

Python Trading Bot Framework

import ccxt
import pandas as pd
import numpy as np
from time import sleep

# Initialize exchange connection
exchange = ccxt.binance({
    'apiKey': 'YOUR_API_KEY',
    'secret': 'YOUR_SECRET',
    'enableRateLimit': True
})

# Trading parameters
symbol = 'BTC/USDT'
timeframe = '1h'
amount = 100  # USD amount to trade

def fetch_data():
    """Fetch OHLCV data from exchange"""
    ohlcv = exchange.fetch_ohlcv(symbol, timeframe, limit=100)
    df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
    return df

def simple_strategy(df):
    """Basic moving average crossover strategy"""
    df['sma_20'] = df['close'].rolling(20).mean()
    df['sma_50'] = df['close'].rolling(50).mean()
    
    # Buy signal when short MA crosses above long MA
    if df['sma_20'].iloc[-2] < df['sma_50'].iloc[-2] and df['sma_20'].iloc[-1] > df['sma_50'].iloc[-1]:
        return 'buy'
    # Sell signal when short MA crosses below long MA
    elif df['sma_20'].iloc[-2] > df['sma_50'].iloc[-2] and df['sma_20'].iloc[-1] < df['sma_50'].iloc[-1]:
        return 'sell'
    else:
        return 'hold'

def execute_trade(signal):
    """Execute trade based on signal"""
    if signal == 'buy':
        print(f"Buying {amount} USDT worth of {symbol}")
        # exchange.create_market_buy_order(symbol, amount)
    elif signal == 'sell':
        print(f"Selling position in {symbol}")
        # exchange.create_market_sell_order(symbol, amount)

# Main trading loop
while True:
    try:
        data = fetch_data()
        signal = simple_strategy(data)
        execute_trade(signal)
        sleep(3600)  # Wait 1 hour between checks
    except Exception as e:
        print(f"Error: {e}")
        sleep(60)

Bot Output Console

// Console output will appear here when you run the code