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.
Building AI Trading Bots
From basic algorithms to machine learning models, create your own automated trading systems.
Python for Financial AI
Essential Python skills for quantitative analysis, backtesting, and predictive modeling.
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 NowAI 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 NowAutoGPT 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 NowPython 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)