Not connected Wallet
Address:
FAT
AMBR
AI & Tech Lab | Financial AI Tools

AI & Tech Lab

Master AI tools, explore tutorials, and access free resources to enhance your financial strategy

AI Tool Tutorials

ChatGPT for Financial Analysis

Learn how to leverage ChatGPT for market research, earnings analysis, and investment decision-making.

6 Lessons Intermediate
Start Course

Building AI Trading Bots

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

8 Lessons Advanced
Start Course

Python for Financial AI

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

10 Lessons Beginner
Start Course

Free Resources

ChatGPT Prompt Templates

Optimize your AI interactions with these ready-to-use prompts

A collection of 20+ prompt templates specifically designed for financial analysis, market research, and investment strategy.

Download Now

AI Financial Analysis Case Study

Learn how a hedge fund leveraged AI for market prediction

This detailed case study explores how a leading hedge fund implemented AI tools to improve their market analysis and decision-making process.

Download Now

AutoGPT Setup Guide

Step-by-step instructions for setting up your AI assistant

A comprehensive guide to setting up and configuring AutoGPT for financial research and analysis, including best practices and example configurations.

Download Now

Python Crypto Trading Bot Skeleton

# Basic Crypto Trading Bot Framework
import ccxt
import pandas as pd
import numpy as np
from time import sleep

# Initialize exchange
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)
🚀 Buy FAT Token