Algorithmic Trading - Paper Trading | Python Algorithmic Trading Cookbook

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Python Algorithmic Trading Cookbook

By : Dagade

3.8 (10)

Buy this Book

Python Algorithmic Trading Cookbook

3.8 (10)

By: Dagade

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

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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

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

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

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

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

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

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

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

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

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

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

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Appendix I

Setting up your Zerodha account

Appendix II

Setting up your AlgoBulls account

Appendix III

Developing and improving strategies

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Algorithmic Trading - Paper Trading

After building algorithmic trading strategies in Chapter 8, Algorithmic Trading Strategies – Coding Step by Step, and successfully backtesting them with satisfactory results in the previous chapter, the next step is to paper trade the strategies in live markets.

Paper trading is the method of executing a trading strategy in the live market hours by simply recording trades coming from the strategy execution in real time. The trades are not executed with real money via a broker. Earlier, this recording of trades was done on paper, hence the name paper trading. These virtual trades can be used for analyzing the risk and return metrics. Typical paper trading metrics include profit and loss (P&L), maximum drawdown, the count of total trades, winning trades, losing trades, long trades and short trades, average profit per winning and losing...