The top 5 mistakes beginners make learning algorithmic trading.
(And how to avoid them and keep your money):
(And how to avoid them and keep your money):
1/ Not understanding the markets
Economic, political, psychological, and random factors impact the markets. Most of the time, in ways we struggle to understand and can never predict.
Build *market intuition* by trading your strategy manually with a small size.
Economic, political, psychological, and random factors impact the markets. Most of the time, in ways we struggle to understand and can never predict.
Build *market intuition* by trading your strategy manually with a small size.
2/ Not properly backtesting strategies
Backtesting is a way to evaluate the performance of a strategy over a historical period. But it's hard to get right. Bias, randomness, and over-fitting will leave you broke.
Use an event-driven backtesting framework and test the backtest.
Backtesting is a way to evaluate the performance of a strategy over a historical period. But it's hard to get right. Bias, randomness, and over-fitting will leave you broke.
Use an event-driven backtesting framework and test the backtest.
3/ Data mining
Data mining leads to adjusting parameters to fit past data until performance looks good. This leads to a strategy that is not robust and will fail in the future.
Trade market inefficiencies. Don't data mine.
Data mining leads to adjusting parameters to fit past data until performance looks good. This leads to a strategy that is not robust and will fail in the future.
Trade market inefficiencies. Don't data mine.
4/ Failing to account for transaction costs
Brokerage fees and slippage significantly impact the performance of an algorithmic trading strategy. Especially for strategies that trade intraday.
Use a backtesting framework with a realistic commission and slippage models.
Brokerage fees and slippage significantly impact the performance of an algorithmic trading strategy. Especially for strategies that trade intraday.
Use a backtesting framework with a realistic commission and slippage models.
5/ Not having a risk management plan
Risk management is crucial in algorithmic trading and beginners often fail to have a plan in place. Overtrading, trading too large, and taking opposite bets will ruin you fast.
Focus on your exits first and automate position sizing.
Risk management is crucial in algorithmic trading and beginners often fail to have a plan in place. Overtrading, trading too large, and taking opposite bets will ruin you fast.
Focus on your exits first and automate position sizing.
The best way to avoid going broke trading:
• Include transaction costs
• Build market intuition
• *Properly* backtest
• Avoid data mining
• Manage your risk
• Include transaction costs
• Build market intuition
• *Properly* backtest
• Avoid data mining
• Manage your risk
The next best way to avoid going broke trading is to follow @pyquantnews.
To keep this list handy:
1. Click the link
2. Retweet the top tweet
I post Python code and tools for quant finance at 8:15 am EST and 8:15 pm EST every day.
To keep this list handy:
1. Click the link
2. Retweet the top tweet
I post Python code and tools for quant finance at 8:15 am EST and 8:15 pm EST every day.
The FREE 7-day masterclass that will get you up and running with Python for quant finance.
Here's what you get:
• Working code to trade with Python
• Frameworks to get you started TODAY
• Trading strategy formation framework
7 days. Big results.
pythonforquantfinancemasterclass.com
Here's what you get:
• Working code to trade with Python
• Frameworks to get you started TODAY
• Trading strategy formation framework
7 days. Big results.
pythonforquantfinancemasterclass.com
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