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Python Algorithmic Trading Cookbook

Python Algorithmic Trading Cookbook

By : Dagade
3.8 (10)
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Python Algorithmic Trading Cookbook

Python Algorithmic Trading Cookbook

3.8 (10)
By: Dagade

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.
Table of Contents (16 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
1
Handling and Manipulating Date, Time, and Time Series Data
Handling and Manipulating Date, Time, and Time Series Data
Technical requirements
Creating datetime objects
Creating timedelta objects
Operations on datetime objects
Modifying datetime objects
Converting a datetime object to a string
Creating a datetime object from a string
The datetime object and time zones
Creating a pandas.DataFrame object
DataFrame manipulation—renaming, rearranging, reversing, and slicing
DataFrame manipulation—applying, sorting, iterating, and concatenating
Converting a DataFrame into other formats
Creating a DataFrame from other formats
Free Chapter
2
Stock Markets - Primer on Trading
Stock Markets - Primer on Trading
Technical requirements
Setting up Python connectivity with the broker
Querying a list of instruments
Fetching an instrument
Querying a list of exchanges
Querying a list of segments
Knowing other attributes supported by the broker
Placing a simple REGULAR order
Placing a simple BRACKET order
Placing a simple DELIVERY order
Placing a simple INTRADAY order
Querying margins and funds
Calculating the brokerage charged
Calculating the government taxes charged
3
Fetching Financial Data
Fetching Financial Data
Technical requirements
Fetching the list of financial instruments
Attributes of a financial instrument
Expiry of financial instruments
Circuit limits of a financial instrument
The market depth of a financial instrument
The total pending buy quantity of a financial instrument
The total pending sell quantity of a financial instrument
The total volume traded for the day of a financial instrument
The last traded price of a financial instrument
The last traded time of a financial instrument
The last traded quantity of a financial instrument
The recorded open price of the day of a financial instrument
The recorded highest price of the day of a financial instrument
The recorded lowest price of the day of a financial instrument
The recorded close price of the last traded day of a financial instrument
4
Computing Candlesticks and Historical Data
Computing Candlesticks and Historical Data
Technical requirements
Fetching historical data using the broker API
Fetching historical data using the Japanese (OHLC) candlestick pattern
Fetching the Japanese candlestick pattern with variations in candle intervals
Fetching historical data using the Line Break candlestick pattern
Fetching historical data using the Renko candlestick pattern
Fetching historical data using the Heikin-Ashi candlestick pattern
Fetching historical data using Quandl
5
Computing and Plotting Technical Indicators
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Computing and Plotting Technical Indicators
Technical requirements
Trend indicators – simple moving average
Trend indicators – exponential moving average
Trend indicators – moving average convergence divergence
Trend indicators – parabolic stop and reverse
Momentum indicators – relative strength index
Momentum indicators – stochastic oscillator
Volatility indicators – Bollinger Bands
Volatility indicators – average true range
Volume indicators – on balance volume
Volume indicators – volume-weighted average price
6
Placing Regular Orders on the Exchange
Placing Regular Orders on the Exchange
Technical requirements
Placing a regular market order
Placing a regular limit order
Placing a regular stoploss-limit order
Placing a regular stoploss-market order
7
Placing Bracket and Cover Orders on the Exchange
Placing Bracket and Cover Orders on the Exchange
Technical requirements
Placing a bracket limit order
Placing a bracket stoploss-limit order
Placing a bracket limit order with trailing stoploss
Placing a bracket stoploss-limit order with trailing stoploss
Placing a cover market order
Placing a cover limit order
8
Algorithmic Trading Strategies - Coding Step by Step
Algorithmic Trading Strategies - Coding Step by Step
Technical requirements
EMA-Regular-Order strategy – coding the __init__, initialize, name, and versions_supported methods
EMA-Regular-Order strategy – coding the strategy_select_instruments_for_entry method
EMA-Regular-Order strategy – coding the strategy_enter_position method
EMA-Regular-Order strategy – coding the strategy_select_instruments_for_exit method
EMA-Regular-Order strategy – coding the strategy_exit_position method
EMA-Regular-Order strategy – uploading the strategy on the AlgoBulls trading platform
MACD-Bracket-Order strategy – coding the __init__, initialize, name, and versions_supported methods
MACD-Bracket-Order strategy – coding the strategy_select_instruments_for_entry method
MACD-Bracket-Order strategy – coding the strategy_enter_position method
MACD-Bracket-Order strategy – coding the strategy_select_instruments_for_exit method
MACD-Bracket-Order strategy – coding the strategy_exit_position method
MACD-Bracket-Order strategy — uploading the strategy on the AlgoBulls trading platform
9
Algorithmic Trading - Backtesting
Algorithmic Trading - Backtesting
Technical requirements
EMA-Regular-Order strategy – fetching the strategy
EMA-Regular-Order strategy – backtesting the strategy
EMA-Regular-Order strategy – fetching backtesting logs in real time
EMA-Regular-Order strategy – fetching a backtesting report – profit and loss table
EMA-Regular-Order strategy — fetching a backtesting report – statistics table
EMA-Regular-Order strategy – fetching a backtesting report – order history
MACD-Bracket-Order strategy – fetching the strategy
MACD-Bracket-Order strategy – backtesting the strategy
MACD-Bracket-Order strategy – fetching backtesting logs in real time
MACD-Bracket-Order strategy – fetching a backtesting report – profit and loss table
MACD-Bracket-Order strategy – fetching a backtesting report – statistics table
MACD-Bracket-Order strategy – fetching a backtesting report – order history
10
Algorithmic Trading - Paper Trading
Algorithmic Trading - Paper Trading
Technical requirements
EMA-Regular-Order strategy – fetching the strategy
EMA-Regular-Order strategy – paper trading the strategy
EMA-Regular-Order strategy – fetching paper trading logs in real time
EMA-Regular-Order strategy – fetching a paper trading report – profit and loss table
EMA-Regular-Order strategy – fetching a paper trading report – statistics table
EMA-Regular-Order strategy – fetching a paper trading report – order history
MACD-Bracket-Order strategy – fetching the strategy
MACD-Bracket-Order strategy – paper trading the strategy
MACD-Bracket-Order strategy – fetching paper trading logs in real time
MACD-Bracket-Order strategy – fetching a paper trading report – profit and loss table
MACD-Bracket-Order strategy – fetching a paper trading report – statistics table
MACD-Bracket-Order strategy – fetching a paper trading report – order history
11
Algorithmic Trading - Real Trading
Algorithmic Trading - Real Trading
Technical requirements
EMA–Regular–Order strategy – fetching the strategy
EMA–Regular–Order strategy – real trading the strategy
EMA–Regular–Order strategy – fetching real trading logs in real time
EMA–Regular–Order strategy – fetching a real trading report – profit and loss table
EMA–Regular–Order strategy – fetching a real trading report – statistics table
MACD–Bracket–Order strategy – fetching the strategy
MACD–Bracket–Order strategy – real trading the strategy
MACD–Bracket–Order strategy – fetching real trading logs in real time
MACD–Bracket–Order strategy – fetching a real trading report – profit and loss table
MACD–Bracket–Order strategy – fetching a real trading report – statistics table
12
Other Books You May Enjoy
Other Books You May Enjoy
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Appendix I
Setting up your Zerodha account
Appendix II
Setting up your AlgoBulls account
Appendix III
Developing and improving strategies

Volatility indicators – Bollinger Bands

Bollinger Bands are a lagging volatility indicator. Bollinger Bands consist of three lines, or bands—the middle band, the lower band, and the upper band. The gap between the bands widens when the price volatility is high and reduces when the price volatility is low.

Bollinger Bands are an indicator of overbought or oversold conditions. When the price is near the upper band or the lower band, this indicator predicts that a reversal will happen soon. The middle band acts as a support or resistance level.

The upper band and lower band can also be interpreted as price targets. When the price bounces off of the upper band and crosses the middle band, the lower band becomes the price target, and vice versa.

The formulae for computing the Bollinger Bands are as follows.

Bollinger Bands define the typical price (TP) as the average of the high, low, and close of a candle. The TP is used for computing the middle band, lower band, and upper...

End of Section 9
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