The 7 must-have books everyone using Python for algorithmic trading needs to know:

My master's degree completely failed to teach me how to test trading strategies.

So I spent 40 hours looking for Python backtesting libraries.

Then I started using the best ones.

But unlike my quant finance degree, these won't cost you $90,000.

Here they are for free.

AutoTrader

AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading

Zipline

From Quantopian (acquired by Robinhood) the first to democratize quant trading, comes Zipline. It's a robust, fully-featured backtesting library which features slippage models, robust data handing and rich

backtrader

backtrader features live data and trading, filters and multiple data feeds at

backtesting .py

backtesting .py features a simple well-documented API, fast execution and a library of built-in base

9 trading strategies everyone should know (with Python code):

Bollinger Bands Pattern Recognition

The mid band is the moving average on the price series.

The upper and lower bands are two moving standard deviations away from the mid band.

MACD oscillator

MACD refers to Moving Average Convergence/Divergence.

MACD is a momentum trading strategy.

It assumes momentum has more impact on short-term moving average than long-term moving average.

Pairs Trading

Pairs trading is a basic form of statistical arbitrage.

It assumes that two cointegrated stocks do not drift too far away from each other.

When they do, it generates the trading signal.

Heikin-Ashi Candlestick

Heikin-Ashi refers to 'Average Bar' in Japanese.

It's an alternative style of candlestick chart.

The rules of Heiki-Ashi are designed to filter out the noise for momentum trading.

In 2012, my first options trade lost $9,000.

12 months later I was making $1,100 per week trading in my free time.

What changed?

I read 20 books on options and finished a master’s degree.

But what took my game to the next level was Python.

Here’s the code I still use today:

A quick primer on options in case you're not familiar:

• Over $450 billion in notional trades DAILY
• 39 million options contracts trade DAILY
• 25% of total options trading is from retail

So how do we make money?

Trade options with a simple, 3-part framework:

1. Design your risk
2. Value the position
3. Measure and monitor

Now pair this framework with Python and you get a potent combination for making money trading options.

Let's dig in:

To design our risk profile, we need the option payoff.

Let's start by defining the variables we need:

• Stock price
• Strike price
• Time to expiration
• Interest rate
• Volatility

We also define market prices for demonstrating trades.

Remember our payoff functions for calls and puts?

• call = max(S - K, 0)
• put = max(K - S, 0)

We can define them in Python in one line of code each.

And here's where it gets really interesting.

A Bloomberg Terminal costs $24,000 per year.

It's the portal to all the world's financial data.

Unaffordable for 99% of us.

Reserved for the Wall Street elite to outfox the normal investor.

Until now:

In case you're unfamiliar:

• Over 325,000 people use Bloomberg daily
• Used for market data, trading and research
• Launched in 1981 by billionaire Michael Bloomberg

It's a modern icon of financial markets.

So, how can you compete?

With OpenBB.

The OpenBB Terminal is a Python-based environment for investment research.

There are over 500 functions covering:

• ETFs
• Forex
• Stocks
• Crypto
• Options
• Portfolio
• Economy
• Mutual funds
• Econometrics

Strap on your seat belt.

Let's dive in:

Let's start by analyzing Tesla.

First, type *stocks* at the command prompt to show the menu.

These are all the sub menus you can explore to analyze stocks.

The menu items in grey are not available until you load stock data.

Let's load TSLA now.

Type *load TSLA*.

This will load TSLA price data (for free) and print out a performance summary.

Now we can start to analyze TSLA.

I've been using pandas since 2011.

It's the most important tool for analysts working in Python today.

If you're using Python, you need pandas.

Here's the 7 tutorials I wish I had when I started:

10 minutes to pandas

This is a short introduction to pandas, geared mainly for new

pandas tutorial on W3 Schools

A 14 section getting started guide for pandas. From an introduction to the library to more advanced features like correlation and

A Complete Introduction for Beginners

Learn some of the most important pandas features for exploring, cleaning, transforming, visualizing, and learning from

Hands-On Pandas

Solve short hands-on challenges to perfect your data manipulation