After submitting a backtesting job on the AlgoBulls platform, the AlgoBulls backtesting engine starts executing the strategy. During its execution, along with the logs, the P&L table and the statistics table of the AlgoBulls backtesting engine generate an order history log in real time. This log contains state transitions of every order, along with the timestamps and additional information (if any) for each order state. The order history log is crucial for understanding how long it has taken for a trade to go from an 'OPEN' state to 'COMPLETE' or 'CANCELLED'. For example, the MARKET orders would immediately go from 'OPEN' to 'COMPLETE' but the LIMIT orders may take a while, based on the market conditions, to go from 'OPEN' to 'COMPLETE' – they may even get to 'CANCELLED'. All this information is available in...
Python Algorithmic Trading Cookbook
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Python Algorithmic Trading Cookbook
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Overview of this book
If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help.
Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders.
By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem.
Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice.
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Current Title:
Python Algorithmic Trading Cookbook
Table of Contents (16 chapters)
Preface
Handling and Manipulating Date, Time, and Time Series Data
Free Chapter
Stock Markets - Primer on Trading
Fetching Financial Data
Computing Candlesticks and Historical Data
Computing and Plotting Technical Indicators
Placing Regular Orders on the Exchange
Placing Bracket and Cover Orders on the Exchange
Algorithmic Trading Strategies - Coding Step by Step
Algorithmic Trading - Backtesting
Algorithmic Trading - Paper Trading
Algorithmic Trading - Real Trading
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Appendix I
Appendix II
Appendix III
Customer Reviews