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Hands-On Financial Trading with Python

Will Ward

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Published in Hands On Financial Trading With Python: A Practical Guide To Using Zipline And Other Python Libraries For Backtesting Trading Strategies

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Financial trading is a challenging but rewarding field that requires a combination of technical skills, financial knowledge, and a deep understanding of the markets. In recent years, Python has emerged as a powerful tool for financial traders, offering a wide range of libraries and tools that can help them with everything from data analysis to automated trading.

This comprehensive guide will provide you with everything you need to know to get started with financial trading with Python. We'll cover the basics of Python programming, introduce you to the essential libraries for financial trading, and show you how to develop and backtest your own trading strategies.

If you're new to Python, don't worry! This guide will assume no prior programming experience. We'll start by covering the basics of the language, including:

Hands-On Financial Trading with Python: A practical guide to using Zipline and other Python libraries for backtesting trading strategies

by Jiri Pik

4.4 out of 5

Language	:	English
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Variables and data types
Control flow
Functions
Object-oriented programming

Once you're comfortable with the basics, we'll move on to more advanced topics, such as:

Data analysis with Pandas
Machine learning with Scikit-learn
Automated trading with QuantRocket

There are a number of excellent Python libraries that can help you with financial trading. Some of the most popular include:

Pandas: A library for data analysis and manipulation
Scikit-learn: A library for machine learning
QuantRocket: A library for automated trading
TA-Lib: A library for technical analysis
Yahoo Finance: A library for accessing financial data

These libraries provide you with a wide range of tools for everything from data analysis to automated trading.

The first step in developing a trading strategy is to identify the market inefficiencies that you want to exploit. This could be anything from a price trend to a correlation between two different assets.

Once you've identified a market inefficiency, you need to develop a strategy for exploiting it. This strategy should include:

Entry criteria: The conditions that must be met before you enter a trade
Exit criteria: The conditions that must be met before you exit a trade
Position sizing: The amount of money you risk on each trade
Risk management: The steps you take to protect your capital

Once you've developed a trading strategy, the next step is to backtest it. This involves using historical data to simulate your trading strategy and see how it would have performed in the past.

Backtesting is an essential step in the trading process, as it allows you to identify and fix any flaws in your strategy before you start trading with real money.

Once you're confident in your trading strategy, you can start automated trading. This involves using a computer program to execute your trades automatically.

Automated trading can save you a lot of time and effort, and it can also help you to achieve better results by removing the emotional element from trading.

Financial trading with Python is a powerful and rewarding way to trade the markets. This guide has provided you with everything you need to know to get started, from the basics of Python programming to the essential libraries for financial trading.

If you're ready to take your trading to the next level, I encourage you to start using Python today.

Hands-On Financial Trading with Python: A practical guide to using Zipline and other Python libraries for backtesting trading strategies

by Jiri Pik

4.4 out of 5