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Python Algorithmic Trading Cookbook

You're reading from   Python Algorithmic Trading Cookbook All the recipes you need to implement your own algorithmic trading strategies in Python

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781838989354
Length 542 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Pushpak Dagade Pushpak Dagade
Author Profile Icon Pushpak Dagade
Pushpak Dagade
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Toc

Table of Contents (16) Chapters Close

Preface 1. Handling and Manipulating Date, Time, and Time Series Data 2. Stock Markets - Primer on Trading FREE CHAPTER 3. Fetching Financial Data 4. Computing Candlesticks and Historical Data 5. Computing and Plotting Technical Indicators 6. Placing Regular Orders on the Exchange 7. Placing Bracket and Cover Orders on the Exchange 8. Algorithmic Trading Strategies - Coding Step by Step 9. Algorithmic Trading - Backtesting 10. Algorithmic Trading - Paper Trading 11. Algorithmic Trading - Real Trading 12. Other Books You May Enjoy Appendix I
1. Appendix II
2. Appendix III

EMA-Regular-Order strategy — fetching a backtesting report – statistics table

After submitting a backtesting job on the AlgoBulls platform, the AlgoBulls backtesting engine starts executing the strategy. During its execution, along with the logs and P&L table, the AlgoBulls backtesting engine also generates a summary from the P&L table in real time. This summary is a table of statistics containing various statistical numbers such as Net P&L (absolute and percentage), Max Drawdown (absolute and percentage), count of total trades, winning trades, losing trades, long trades and short trades, maximum gain and minimum gain (or maximum loss), and average profit per winning and losing trade. This table gives an instant overview of the overall strategy's performance.

In this recipe, you will fetch the statistics table report for your strategy. This report is available as soon as the first trade is punched in by your strategy after you submit a backtesting job. The...

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