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Financial Trading in Python

Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!

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4 Horas15 Videos50 Ejercicios
13.526 AprendicesTrophyDeclaración de cumplimiento

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Descripción del curso

Are you fascinated by the financial markets and interested in financial trading? This course will help you to understand why people trade, what the different trading styles are, and how to use Python to implement and test your trading strategies. Start your trading adventure with an introduction to technical analysis, indicators, and signals. You'll learn to build trading strategies by working with real-world financial data such as stocks, foreign exchange, and cryptocurrencies. By the end of this course, you'll be able to implement custom trading strategies in Python, backtest them, and evaluate their performance.
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  1. 1

    Trading Basics

    Gratuito

    What is financial trading, why do people trade, and what’s the difference between technical trading and value investing? This chapter answers all these questions and more. You’ll also learn useful tools to explore trading data, generate plots, and how to implement and backtest a simple trading strategy in Python.

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    What is financial trading
    50 xp
    The concept of trading
    50 xp
    Plot a time series line chart
    100 xp
    Plot a candlestick chart
    100 xp
    Getting familiar with your trading data
    50 xp
    Resample the data
    100 xp
    Plot a return histogram
    100 xp
    Calculate and plot SMAs
    100 xp
    Financial trading with bt
    50 xp
    The bt process
    100 xp
    Define and backtest a simple strategy
    100 xp
  2. 2

    Technical Indicators

    Let's dive into the world of technical indicators—a useful tool for constructing trading signals and building strategies. You’ll get familiar with the three main indicator groups, including moving averages, ADX, RSI, and Bollinger Bands. By the end of this chapter, you’ll be able to calculate, plot, and understand the implications of indicators in Python.

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  3. 3

    Trading Strategies

    You’re now ready to construct signals and use them to build trading strategies. You’ll get to know the two main styles of trading strategies: trend following and mean reversion. Working with real-life stock data, you’ll gain hands-on experience in implementing and backtesting these strategies and become more familiar with the concepts of strategy optimization and benchmarking.

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  4. 4

    Performance Evaluation

    How is your trading strategy performing? Now it’s time to take a look at the detailed statistics of the strategy backtest result. You’ll gain knowledge of useful performance metrics, such as returns, drawdowns, Calmar ratio, Sharpe ratio, and Sortino ratio. You’ll then tie it all together by learning how to obtain these ratios from the backtest results and evaluate the strategy performance on a risk-adjusted basis.

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Sets De Datos

Google Stock DataBitcoin Price DataAmazon Stock DataTesla Stock Data

Colaboradores

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Jen Bricker
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Hadrien Lacroix
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Justin Saddlemyer
Chelsea Yang HeadshotChelsea Yang

Data Science Instructor

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