10 ways to use machine learning in trading (with the Python library):
Reinforcement Learning
Optimizing portfolio management using rewards.
Uses agent-based rewards for dynamic portfolio management. It learns to balance risk and reward by trading different stocks over time.
Use: Stable Baselines3's A2C model
Support Vector Machines
Predicts future trends from patterns.
Uses historical market data for pattern recognition, predicting trends in a specific stock's price based on various indicators such as price and volume.
The genius mathematician who returned 20%+ over 30 years.
He traded for 19 years, with his worst loss being 1%.
He beat Vegas dealers at blackjack.
His top 7 trading strategies (and how they work):
1. Statistical Arbitrage
He used mathematical models to identify price discrepancies between different markets or securities, buying undervalued assets and selling overvalued ones.
2. Quantitative Analysis
Thorp pioneered the use of quantitative methods in financial markets, developing advanced algorithms to analyze market data and identify profitable trades.
1. The compounding effect of doing the same thing over and over for a long period of time is a force of nature. Learn how to compound.
2. Learn how to be comfortable with being uncomfortable. Wealth doesn't accrue to people solving easy problems.
3. As soon as you hit your 30s, you’ll understand. Your 20s actually suck. If you feel like it sucks right now, guess what? It’s supposed to. It gets better.
4. It is very hard to change the world when you are broke. So don't apologize for wanting wealth.
12 Python libraries for free market data everyone should know:
yfinance
Data for stocks (historic, intraday, fundamental), FX, crypto, and options. Uses Yahoo Finance so any data available through Yahoo is available through yfinance.
pandas-datareader used to be part of the pandas project. Now an independent project. Includes data for stocks, FX, economic indicators, Fama-French factors, and many others.