Python Algorithmic Trading Cookbook

By Pushpak Dagade
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  1. Stock Markets - Primer on Trading

About 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.

Publication date:
August 2020
Publisher
Packt
Pages
542
ISBN
9781838989354

 
Stock Markets - Primer on Trading

When building algorithmic trading systems, it is essential to have an account open with a modern broker that provides APIs for placing and querying trades programmatically. This allows us to control the broking account, which is conventionally operated manually using the broker's website, using our Python script, which would be part of our larger algorithmic trading system. This chapter demonstrates various essential recipes that introduce the essential broker API calls needed for developing a complete algorithmic trading system.

This chapter covers the following recipes:

  • 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

Let's get started!

 

Technical requirements

You will need the following to successfully execute the recipes in this chapter:

  • Python 3.7+
  • The Python pyalgotrading package ($ pip install pyalgotrading)

The latest Jupyter Notebook for this chapter can be found on GitHub at https://github.com/PacktPublishing/Python-Algorithmic-Trading-Cookbook/tree/master/Chapter02.

This chapter demonstrates the APIs of a modern broker, ZERODHA, which is supported by pyalgotrading. You may wish to choose other brokers supported by pyalgotrading as well. The recipes in this chapter should be more or less the same for any other broker. The pyalgotrading package abstracts broker APIs behind a unified interface, so you don't need to worry about the underlying broker API calls.

To set up a broking account with ZERODHA, please refer to the detailed steps provided in Appendix I.

 

Setting up Python connectivity with the broker

The first thing you need to set up connectivity with the broker is API keys. The broker provides unique keys to each customer, typically as an api-key and api-secret key pair. These API keys are chargeable, usually on a monthly subscription basis. You need to get your copies of api-key and api-secret from the broker's website before you start this recipe. Please refer to Appendix I for more details.

How to do it…

We execute the following steps to complete this recipe:

  1. Import the necessary modules:
>>> from pyalgotrading.broker.broker_connection_zerodha import BrokerConnectionZerodha
  1. Get the api_key and api_secret keys from the broker. These are unique to you and will be used by the broker to identify your Demat account:
>>> api_key = "<your-api-key>"
>>> api_secret = "<your-api-secret>"
>>> broker_connection = BrokerConnectionZerodha(api_key, api_secret)

You will get the following result:

Installing package kiteconnect via pip...
Please login to this link to generate your request token: https://kite.trade/connect/login?api_key=<your-api-key>&v=3
  1. Get the request token from the preceding URL:
>>> request_token = "<your-request-token>"
>>> broker_connection.set_access_token(request_token)

How it works...

In step 1, you import the BrokerConnectionZerodha class from pyalgotrading. The BrokerConnectionZerodha class provides an abstraction around the broker-specific APIs. For step 2, you need your API key and API secret from the broker. If you do not have them, please refer to Appendix I for detailed instructions with screenshots on getting this keys. In step 2, you assign your API key and API secret to the new api_key and api_secret variables and use them to create broker_connection, an instance of the BrokerConnectionZerodha class. If you are running this for the first time and kiteconnect is not installed, pyalgotrading will automatically install it for you. (kiteconnect is the official Python package that talks to the Zerodha backend; BrokerConnectionZerodha is a wrapper on top of kiteconnect.) Step 2 generates a login URL. Here, you need to click on the link and log in with your Zerodha credentials. If the authentication process is successful, you will see a link in your browser's address bar that looks similar to the following:

https://127.0.0.1/?request_token=&action=login&status=success

For example, the full link would be as follows:

https://127.0.0.1/?request_token=H06I6Ydv95y23D2Dp7NbigFjKweGwRP7&action=login&status=success

Copy the alphanumeric-token, H06I6Ydv95y23D2Dp7NbigFjKweGwRP7, and paste it into request_token as part of step 3. The broker_connection instance is now ready to perform API calls.

 

Querying a list of instruments

Once the broker_connection handle is ready, it can be used to query the list containing all the financial instruments provided by the broker.

Getting ready

Make sure the broker_connection object is available in your Python namespace. Refer to the previous recipe in this chapter to set up this object.

How to do it…

We execute the following steps to complete this recipe:

  1. Display all the instruments:
>>> instruments = broker_connection.get_all_instruments()
>>> instruments

You will get an output similar to the following. The exact output may differ for you:

  instrument_token exchange_token tradingsymbol name last_price expiry strike tick_size lot_size instrument_type segment exchange
0 267556358 1045142 EURINR20AUGFUT EURINR 0.0 2020-08-27 0.0 0.0025 1 FUT BCD-FUT BCD
1 268660998 1049457 EURINR20DECFUT EURINR 0.0 2020-12-29 0.0 0.0025 1 FUT BCD-FUT BCD
2 266440966 1040785 EURINR20JULFUT EURINR 0.0 2020-07-29 0.0 0.0025 1 FUT BCD-FUT BCD
3 266073606 1039350 EURINR20JUNFUT EURINR 0.0 2020-06-26 0.0 0.0025 1 FUT BCD-FUT BCD
4 265780742 1038206 EURINR20MAYFUT EURINR 0.0 2020-05-27 0.0 0.0025 1 FUT BCD-FUT BCD
... ... ... ... ... ... ... ... ... ... ... ... ...
64738 978945 3824 ZODJRDMKJ ZODIAC JRD-MKJ 0.0 0.0 0.0500 1 EQ NSE NSE
64739 2916865 11394 ZOTA ZOTA HEALTH CARE 0.0 0.0 0.0500 1 EQ NSE NSE
64740 7437825 29054 ZUARI-BE ZUARI AGRO CHEMICALS 0.0 0.0 0.0500 1 EQ NSE NSE
64741 979713 3827 ZUARIGLOB ZUARI GLOBAL 0.0 0.0 0.0500 1 EQ NSE NSE
64742 4514561 17635 ZYDUSWELL ZYDUS WELLNESS 0.0 0.0 0.0500 1 EQ NSE NSE

64743 rows × 12 columns
  1. Print the total number of instruments:
>>> print(f'Total instruments: {len(instruments)}')

We get the following output (your output may differ):

Total instruments: 64743

How it works…

The first step fetches all the available financial instruments using the get_all_instruments() method of broker_connection. This method returns a pandas.DataFrame object. This object is assigned to a new variable, instruments, which is shown in the output of step 1. This output may differ for you as new financial instruments are frequently added and existing ones expire regularly. The final step shows the total number of instruments provided by the broker.

An explanation of the data that was returned by the preceding API call will be discussed in depth in Chapter 3, Analyzing Financial Data. For this recipe, it suffices to know the method for fetching the list of instruments.
 

Fetching an instrument

Instruments, also known as financial instruments or securities, are assets that can be traded in an exchange. In an exchange, there can easily be tens of thousands of instruments. This recipe demonstrates how to fetch an instrument based on its exchange and trading symbol.

Getting ready

Make sure the broker_connection object is available in your Python namespace. Refer to the first recipe in this chapter to set up this object.

How to do it…

Fetch an instrument for a specific trading symbol and exchange:

>>> broker_connection.get_instrument(segment='NSE', tradingsymbol='TATASTEEL')

You'll get the following output:

segment: NSE
exchange: NSE
tradingsymbol: TATASTEEL
broker_token: 895745
tick_size: 0.05
lot_size: 1
expiry:
strike_price: 0.0

How it works…

The broker_connection object provides a handy method, get_instrument, for fetching any financial instrument. It takes segment and tradingsymbol as attributes before returning an instrument. The return object is an instance of the Instrument class.

 

Querying a list of exchanges

An exchange is a marketplace where instruments are traded. Exchanges ensure that the trading process is fair and happens in an orderly fashion at all times. Usually, a broker supports multiple exchanges. This recipe demonstrates how to find the list of exchanges supported by the broker.

Getting ready

Make sure the instruments object is available in your Python namespace. Refer to the second recipe of this chapter to learn how to set up this object.

How to do it…

Display the exchanges supported by the broker:

>>> exchanges = instruments.exchange.unique()
>>> print(exchanges)

You will get the following output:

['BCD' 'BSE' 'NSE' 'CDS' 'MCX' 'NFO']

How it works…

instruments.exchange returns a pandas.Series object. Its unique() method returns a numpy.ndarray object consisting of unique exchanges supported by the broker.

 

Querying a list of segments

A segment is essentially a categorization of instruments based on their types. The various types of segments that are commonly found at exchanges include cash/equities, futures, options, commodities, and currency. Each segment may have a different operating time. Usually, a broker supports multiple segments within multiple exchanges. This recipe demonstrates how to find the list of segments supported by the broker.

Getting ready

Make sure the instruments object is available in your Python namespace. Refer to the second recipe of this chapter to learn how to set up this object.

How to do it…

Display the segments supported by the broker:

>>> segments = instruments.segment.unique()
>>> print(segments)

You will get the following output:

['BCD-FUT' 'BCD' 'BCD-OPT' 'BSE' 'INDICES' 'CDS-FUT' 'CDS-OPT' 'MCX-FUT' 'MCX-OPT' 'NFO-OPT' 'NFO-FUT' 'NSE']

How it works…

instruments.segment returns a pandas.Series object. Its unique method returns a numpy.ndarray object consisting of unique segments supported by the broker.

 

Knowing other attributes supported by the broker

For placing an order, the following attributes are needed: order transaction type, order variety, order type, and order code. Different brokers may support different types of order attributes. For example, some brokers may support just regular orders, while others may support regular and bracket orders. The value for each of the attributes supported by the broker can be queried using the broker specific constants provided by the pyalgotrading package.

How to do it…

We execute the following steps to complete this recipe:

  1. Import the necessary class from the pyalgotrading module:
>>> from pyalgotrading.broker.broker_connection_zerodha import BrokerConnectionZerodha
  1. List the order transaction types:
>>> list(BrokerConnectionZerodha.ORDER_TRANSACTION_TYPE_MAP.keys())

We'll get the following output:

[<BrokerOrderTransactionTypeConstants.BUY: 'BUY'>,
<BrokerOrderTransactionTypeConstants.SELL: 'SELL'>]
  1. List the order varieties:
>>> list(BrokerConnectionZerodha.ORDER_VARIETY_MAP.keys())

We'll get the following output:

[<BrokerOrderVarietyConstants.MARKET: 'ORDER_VARIETY_MARKET'>,
<BrokerOrderVarietyConstants.LIMIT: 'ORDER_VARIETY_LIMIT'>,
<BrokerOrderVarietyConstants.STOPLOSS_LIMIT: 'ORDER_VARIETY_STOPLOSS_LIMIT'>,
<BrokerOrderVarietyConstants.STOPLOSS_MARKET: 'ORDER_VARIETY_STOPLOSS_MARKET'>]
  1. List the order types:
>>> list(BrokerConnectionZerodha.ORDER_TYPE_MAP.keys())

We'll get the following output:

[<BrokerOrderTypeConstants.REGULAR: 'ORDER_TYPE_REGULAR'>,
<BrokerOrderTypeConstants.BRACKET: 'ORDER_TYPE_BRACKET'>,
<BrokerOrderTypeConstants.COVER: 'ORDER_TYPE_COVER'>,
<BrokerOrderTypeConstants.AMO: 'ORDER_TYPE_AFTER_MARKET_ORDER'>]
  1. List the order codes:
>>> list(BrokerConnectionZerodha.ORDER_CODE_MAP.keys())

We'll get the following output:

[<BrokerOrderCodeConstants.INTRADAY: 'ORDER_CODE_INTRADAY'>,
<BrokerOrderCodeConstants.DELIVERY: 'ORDER_CODE_DELIVERY_T0'>]

How it works…

In step 1, we import the BrokerConnectionZerodha class from pyalgotrading. This class holds the order attributes mapping between pyalgotrading and broker specific constants as dictionary objects. The next steps fetch and print these mappings. Step 2 shows that your broker supports both BUY and SELL order transaction types.

Step 3 shows that your broker supports MARKET, LIMIT, STOPLOSS_LIMIT, and STOPLOSS_MARKET order varieties. Step 4 shows that your broker supports REGULAR, BRACKET, COVER, and AFTER_MARKET order types. Step 5 shows that your broker supports INTRADAY and DELIVERY order codes.

The outputs may differ from broker to broker, so consult your broker documentation if you are using a different broker. A detailed explanation of all these types of parameters will be covered in Chapter 6, Placing Trading Orders on the Exchange. This recipe is to just give an overview of the parameters, as they are needed in the subsequent recipes of this chapter.
 

Placing a simple REGULAR order

This recipe demonstrates how to place a REGULAR order on the exchange via the broker. REGULAR orders are the simplest types of orders. After trying out this recipe, check your broking account by logging into the broker's website; you will find that an order has been placed there. You can match the order ID with the one that's returned in the last code snippet shown in this recipe.

Getting ready

Make sure the broker_connection object is available in your Python namespace. Refer to the first recipe of this chapter to learn how to set up this object.

How to do it…

We execute the following steps to complete this recipe:

  1. Import the necessary constants from pyalgotrading:
>>> from pyalgotrading.constants import *
  1. Fetch an instrument for a specific trading symbol and exchange:
>>> instrument = broker_connection.get_instrument(segment='NSE', 
tradingsymbol='TATASTEEL')
  1. Place a simple regular order – a BUY, REGULAR, INTRADAY, MARKET order:
>>> order_id = broker_connection.place_order(
instrument=instrument,
order_transaction_type= \
BrokerOrderTransactionTypeConstants.BUY,
order_type=BrokerOrderTypeConstants.REGULAR,
order_code=BrokerOrderCodeConstants.INTRADAY,
order_variety= \
BrokerOrderVarietyConstants.MARKET,
quantity=1)
>>> order_id

We'll get the following output:

191209000001676

How it works…

In step 1, you import constants from pyalgotrading. In step 2, you fetch the financial instrument with segment = 'NSE' and tradingsymbol = 'TATASTEEL' using the get_instrument() method of broker_connection. In step 3, you place a REGULAR order using the place_order() method of broker_connection. The descriptions of the parameters accepted by the place_order() method are as follows:

  • instrument: The financial instrument for which the order must be placed. Should an instance of the Instrument class. You pass instrument here.
  • order_transaction_type: The order transaction type. Should be an enum of type BrokerOrderTransactionTypeConstants. You pass BrokerOrderTransactionTypeConstants.BUY here.
  • order_type: The order type. Should be an enum of type BrokerOrderTypeConstants. You pass BrokerOrderTypeConstants.REGULAR here.
  • order_code: The order code. Should be an enum of type BrokerOrderCodeConstants. You pass BrokerOrderCodeConstants.INTRADAY here.
  • order_variety: The order variety. Should be an enum of type BrokerOrderVarietyConstants. You pass BrokerOrderVarietyConstants.MARKET here.
  • quantity: The number of shares to be traded for the given instrument. Should be a positive integer. We pass 1 here.

If the order placement is successful, the method returns an order ID which you can use at any point in time later on for querying the status of the order.

A detailed explanation of the different types of parameters will be covered in Chapter 6, Placing Trading Orders on the Exchange. This recipe is intended to give you an idea of how to place a REGULAR order, one of the various types of possible orders.
 

Placing a simple BRACKET order

This recipe demonstrates how to place a BRACKET order on the exchange via the broker. BRACKET orders are two-legged orders. Once the first order is executed, the broker automatically places two new orders a STOPLOSS order and a TARGET order. Only one of them is executed at any time; the other is canceled when the first order is completed. After trying out this recipe, check your broking account by logging into the broker's website; you will find that an order has been placed there. You can match the order ID with the one that's returned in the last code snippet shown in this recipe.

Getting ready

Make sure the broker_connection object is available in your Python namespace. Refer to the first recipe of this chapter to learn how to set up this object.

How to do it…

We execute the following steps to complete this recipe:

  1. Import the necessary modules:
>>> from pyalgotrading.constants import *
  1. Fetch an instrument for a specific trading symbol and exchange:
>>> instrument = broker_connection.get_instrument(segment='NSE', 
tradingsymbol='ICICIBANK')
  1. Fetch the last traded price of the instrument:
>>> ltp = broker_connection.get_ltp(instrument)
  1. Place a simple BRACKET order – a BUY, BRACKET, INTRADAY, LIMIT order:
>>> order_id = broker_connection.place_order(
instrument=instrument,
order_transaction_type= \
BrokerOrderTransactionTypeConstants.BUY,
order_type=BrokerOrderTypeConstants.BRACKET,
order_code=BrokerOrderCodeConstants.INTRADAY,
order_variety=BrokerOrderVarietyConstants.LIMIT,
quantity=1, price=ltp-1,
stoploss=2, target=2)
>>> order_id

We'll get the following output:

191212001268839
If you get the following error while executing this code, it would mean that Bracket orders are blocked by the broker due to high volatility in the markets:

InputException: Due to expected higher volatility in the markets, Bracket orders are blocked temporarily.

You should try the recipe later when the broker starts allowing Bracket orders. You can check for updates on the Broker site from time to time to know when Bracket orders would be allowed.

How it works…

In step 1, you import the constants from pyalgotrading. In step 2, you fetch the financial instrument with segment = 'NSE' and tradingsymbol = 'ICICBANK' using the get_instrument() method of broker_connection. In step 3, you fetch the last traded price or LTP of the instrument. (LTP will be explained in more detail in the Last traded price of a financial instrument recipe of Chapter 3, Analyzing Financial Data.) In step 4, you place a BRACKET order using the place_order() method of broker_connection. The descriptions of the parameters accepted by the place_order() method are as follows:

  • instrument: The financial instrument for which the order must be placed. Should be an instance of the Instrument class. You pass instrument here.
  • order_transaction_type: The order transaction type. Should be an enum of type BrokerOrderTransactionTypeConstants. You pass BrokerOrderTransactionTypeConstants.BUY here.
  • order_type: The order type. Should be an enum of type BrokerOrderTypeConstants. You pass BrokerOrderTypeConstants.BRACKET here.
  • order_code: The order code. Should be an enum of type BrokerOrderCodeConstants. You pass BrokerOrderCodeConstants.INTRADAY here.
  • order_variety: The order variety. Should be an enum of type BrokerOrderVarietyConstants. You pass BrokerOrderVarietyConstants.LIMIT here.
  • quantity: The number of shares to be traded for the given instrument. Should be a positive integer. You pass 1 here.
  • price: The limit price at which the order should be placed. You pass ltp-1 here, which means 1 unit price below the ltp value.
  • stoploss: The price difference from the initial order price, at which the stoploss order should be placed. Should be a positive integer or float value. You pass 2 here.
  • target: The price difference from the initial price, at which the target order should be placed. Should be a positive integer or float value. You pass 2 here.

If the order placement is successful, the method returns an order ID which you can use at any point in time later on for querying the status of the order.

A detailed explanation of the different types of parameters will be covered in Chapter 6, Placing Trading Orders on the Exchange. This recipe is intended to give you an idea of how to place a BRACKET order, one of the various types of possible orders.
 

Placing a simple DELIVERY order

This recipe demonstrates how to place a DELIVERY order on the exchange via the broker. A DELIVERY order is delivered to the user's Demat account and exists until it is explicitly squared-off by the user. Positions created by delivery orders at the end of a trading session are carried forwarded to the next trading session. They are not explicitly squared-off by the broker. After trying out this recipe, check your broking account by logging into the broker's website; you will find that an order has been placed there. You can match the order ID with the one that's returned in the last code snippet shown in this recipe.

Getting ready

Make sure the broker_connection object is available in your Python namespace. Refer to the first recipe of this chapter to learn how to set up this object.

How to do it…

We execute the following steps to complete this recipe:

  1. Import the necessary modules:
>>> from pyalgotrading.constants import *
  1. Fetch an instrument for a specific trading symbol and exchange:
>>> instrument = broker_connection.get_instrument(segment='NSE', 
tradingsymbol='AXISBANK')
  1. Place a simple DELIVERY order – a SELL, REGULAR, DELIVERY, MARKET order:
>>> order_id = broker_connection.place_order(
instrument=instrument,
order_transaction_type= \
BrokerOrderTransactionTypeConstants.SELL,
order_type=BrokerOrderTypeConstants.REGULAR,
order_code=BrokerOrderCodeConstants.DELIVERY,
order_variety= \
BrokerOrderVarietyConstants.MARKET,
quantity=1)
>>> order_id

We'll get the following output:

191212001268956

How it works…

In step 1, you import the constants from pyalgotrading. In step 2, you fetch the financial instrument with segment = 'NSE' and tradingsymbol = 'AXISBANK' using the get_instrument() method of broker_connection. In step 3, you place a DELIVERY order using the place_order() method of broker_connection. This method accepts the following arguments:

  • instrument: The financial instrument for which the order must be placed. Should be an instance of the Instrument class. You pass instrument here.
  • order_transaction_type: The order transaction type. Should be an enum of type BrokerOrderTransactionTypeConstants. You pass BrokerOrderTransactionTypeConstants.SELL here.
  • order_type: The order type. Should be an enum of type BrokerOrderTypeConstants. You pass BrokerOrderTypeConstants.REGULAR here.
  • order_code: The order code. Should be an enum of type BrokerOrderCodeConstants. You pass BrokerOrderCodeConstants.DELIVERY here.
  • order_variety: The order variety. Should be an enum of type BrokerOrderVarietyConstants. You pass BrokerOrderVarietyConstants.MARKET here.
  • quantity: The number of shares to be traded for the given instrument. Should be a positive integer. We pass 1 here.

If the order placement is successful, the method returns an order ID which you can use at any point in time later on for querying the status of the order.

A detailed explanation of the different types of parameters will be covered in Chapter 6, Placing Trading Orders on the Exchange. This recipe is intended to give you an idea of how to place a DELIVERY order, one of the various types of possible orders.
 

Placing a simple INTRADAY order

This recipe demonstrates how to place an INTRADAY order via the broker API. An INTRADAY order is not delivered to the user's Demat account. Positions created by intraday orders have a lifetime of a single day. The positions are explicitly squared off by the broker at the end of a trading session and are not carried forward to the next trading session. After trying out this recipe, check your broking account by logging into the broker's website; you will find that an order has been placed there. You can match the order ID with the one that's returned in the last code snippet shown in this recipe.

Getting ready

Make sure the broker_connection object is available in your Python namespace. Refer to the first recipe of this chapter to learn how to set up this object.

How to do it…

We execute the following steps to complete this recipe:

  1. Import the necessary modules:
>>> from pyalgotrading.constants import *
  1. Fetch an instrument for a specific trading symbol and exchange:
>>> instrument = broker_connection.get_instrument(segment='NSE', 
tradingsymbol='HDFCBANK')
  1. Fetch the last traded price of the instrument:
>>> ltp = broker_connection.get_ltp(instrument)
  1. Place a simple INTRADAY order – a SELL, BRACKET, INTRADAY, LIMIT order:
>>> order_id = broker_connection.place_order(
instrument=instrument,
order_transaction_type= \
BrokerOrderTransactionTypeConstants.SELL,
order_type=BrokerOrderTypeConstants.BRACKET,
order_code=BrokerOrderCodeConstants.INTRADAY,
order_variety=BrokerOrderVarietyConstants.LIMIT,
quantity=1, price=ltp+1, stoploss=2, target=2)
>>> order_id

We'll get the following output:

191212001269042
If you get the following error while executing this code, it would mean that Bracket orders are blocked by the broker due to high volatility in the markets:

InputException: Due to expected higher volatility in the markets, Bracket orders are blocked temporarily.

You should try the recipe later when the broker starts allowing Bracket orders. You can check for updates on the Broker site from time to time to know when Bracket orders would be allowed.

How it works…

In step 1, you import the constants from pyalgotrading. In step 2, you fetch the financial instrument with segment = 'NSE' and tradingsymbol = 'HDFCBANK' using the get_instrument() method of broker_connection. In step 3, you fetch the LTP of the instrument. (LTP will be explained in detail in the Last traded price of a financial instrument recipe of Chapter 3, Analyzing Financial Data.) In step 4, you place a BRACKET order using the place_order() method of the broker_connection. The descriptions of the parameters accepted by the place_order() method are as follows:

  • instrument: The financial instrument for which the order must be placed. Should be an instance of the Instrument class. You pass instrument here.
  • order_transaction_type: The order transaction type. Should be an enum of type BrokerOrderTransactionTypeConstants. You pass BrokerOrderTransactionTypeConstants.SELL here.
  • order_type: The order type. Should be an enum of type BrokerOrderTypeConstants. You pass BrokerOrderTypeConstants.BRACKET here.
  • order_code: The order code. Should be an enum of type BrokerOrderCodeConstants. You pass BrokerOrderCodeConstants.INTRADAY here.
  • order_variety: The order variety. Should be an enum of type BrokerOrderVarietyConstants. You pass BrokerOrderVarietyConstants.LIMIT here.
  • quantity: The number of shares to be traded for the given instrument. Should be a positive integer. You pass 1 here.
  • price: The limit price at which the order should be placed. You pass ltp+1 here, which means 1 unit price above the ltp value.
  • stoploss: The price difference from the initial order price, at which the stoploss order should be placed. Should be a positive integer or float value. You pass 2 here.
  • target: The price difference from the initial order price, at which the target order should be placed. Should be a positive integer or float value. You pass 2 here.

If the order placement is successful, the method returns an order ID which you can use at any point in time later on for querying the status of the order.

A detailed explanation of the different types of parameters will be covered in Chapter 6, Placing Trading Orders on the Exchange. This recipe is intended to give you an idea of how to place an INTRADAY order, one of the various types of possible orders.
 

Querying margins and funds

Before placing orders, it is important to ensure that you have enough margins and funds available in your broking account to place the orders successfully. A lack of sufficient funds would result in the rejection of any orders placed by the broker, which means the others would never get placed on the exchange. This recipe shows you how to find the available margins and funds in your broking account at any point in time.

Getting ready

Make sure the broker_connection object is available in your Python namespace. Refer to the first recipe of this chapter to learn how to set it up.

How to do it…

We execute the following steps to complete this recipe:

  1. Display the equity margins:
>>> equity_margins = broker_connection.get_margins('equity')
>>> equity_margins

We'll get the following output (your output may differ):

{'enabled': True,
'net': 1623.67,
'available': {'adhoc_margin': 0,
'cash': 1623.67,
'opening_balance': 1623.67,
'live_balance': 1623.67,
'collateral': 0,
'intraday_payin': 0},
'utilised': {'debits': 0,
'exposure': 0,
'm2m_realised': 0,
'm2m_unrealised': 0,
'option_premium': 0,
'payout': 0,
'span': 0,
'holding_sales': 0,
'turnover': 0,
'liquid_collateral': 0,
'stock_collateral': 0}}
  1. Display the equity funds:
>>> equity_funds = broker_connection.get_funds('equity')
>>> equity_funds

We'll get the following output (your output may differ):

1623.67
  1. Display the commodity margins:
>>> commodity_margins = get_margins(commodity')
>>> commodity_margins

We'll get the following output (your output may differ):

{'enabled': True,
'net': 16215.26,
'available': {'adhoc_margin': 0,
'cash': 16215.26,
'opening_balance': 16215.26,
'live_balance': 16215.26,
'collateral': 0,
'intraday_payin': 0},
'utilised': {'debits': 0,
'exposure': 0,
'm2m_realised': 0,
'm2m_unrealised': 0,
'option_premium': 0,
'payout': 0,
'span': 0,
'holding_sales': 0,
'turnover': 0,
'liquid_collateral': 0,
'stock_collateral': 0}}
  1. Display the commodity funds:
>>> commodity_funds = broker_connection.get_funds('commodity')
>>> commodity_funds

We'll get the following output (your output may differ):

0

How it works…

The broker_connection object provides methods for fetching the available margins and funds for your broking account:

  • get_margins()
  • get_funds()

The broker Zerodha keeps track of margins and funds separately for equity and commodity products. If you are using a different broker supported by pyalgotrading, it may or may not track the funds and margins separately for equity and commodity.

Step 1 shows how margins can be queried for the equity product using the get_margins() method of the broker_connection object, with equity as an argument. Step 2 shows how funds can be queried for the equity product using the get_funds() method of the broker_connection object, with the equity string as an argument.

Steps 3 and 4 show how margins and funds can be queried for the commodity product in a similar way with the commodity string as an argument.

 

Calculating the brokerage charged

For every order completed successfully, the broker may charge a certain fee, which is usually a small fraction of the price at which the instrument was bought or sold. While the amount may seem small, it is important to keep track of the brokerage as it may end up eating a significant chunk of your profit at the end of the day.

The brokerage that's charged varies from broker to broker and also from segment to segment. For the purpose of this recipe, we will consider a brokerage of 0.01%.

How to do it…

We execute the following steps to complete this recipe:

  1. Calculate the brokerage that's charged per trade:
>>> entry_price = 1245
>>> brokerage = (0.01 * 1245)/100
>>> print(f'Brokerage charged per trade: {brokerage:.4f}')

We'll get the following output:

Brokerage charged per trade: 0.1245
  1. Calculate the total brokerage that's charged for 10 trades:
>>> total_brokerage = 10 * (0.01 * 1245) / 100
>>> print(f'Total Brokerage charged for 10 trades: \
{total_brokerage:.4f}')

We'll get the following output:

Total Brokerage charged for 10 trades: 1.2450

How it works…

In step 1, we start with the price at which a trade was bought or sold, entry_price. For this recipe, we have used 1245. Next, we calculate 0.01% of the price, which comes to 0.1245. Then, we calculate the total brokerage for 10 such trades, which comes out as 10 * 0.1245 = 1.245.

For every order, the brokerage is charged twice. The first time is when the order has entered a position, while the second time is when it has exited the position. To get the exact details of the brokerage that's been charged for your trades, please refer to the list of charges offered by your broker.
 

Calculating the government taxes charged

For every order that's completed successfully, the government may charge a certain fee, which is a fraction of the price at which the instrument was bought or sold. While the amount may seem small, it is important to keep track of government taxes as they may end up eating a significant chunk of your profit at the end of the day.

The government charge depends on the location of the exchange, and varies from segment to segment. For the purpose of this recipe, we will consider government taxes at a rate of 0.1%.

How to do it…

We execute the following steps to complete this recipe:

  1. Calculate the government taxes that are charged per trade:
>>> entry_price = 1245
>>> brokerage = (0.1 * 1245)/100
>>> print(f'Government taxes charged per trade: {brokerage:.4f}')

We'll get the following output:

Government taxes charged per trade: 1.2450

  1. Calculate the total government taxes that are charged for 10 trades:
>>> total_brokerage = 10 * (0.1 * 1245) / 100
>>> print(f'Total Government taxes charged for 10 trades: \
{total_brokerage:.4f}')

We'll get the following output:

Total Government taxes charged for 10 trades: 12.4500

How it works…

In step 1, we start with the price at which a trade was bought or sold, entry_price. For this recipe, we have used 1245. Next, we calculate 0.1% of the price, which comes to 1.245. Then, we calculate the total brokerage for 10 such trades, which comes out as 10 * 1.245 = 12.245.

For every order, government taxes are charged twice. The first time is when the order has entered a position, while the second time is when it has exited the position. To get the exact details of the government taxes that are charged for your trades, please refer to the list of government taxes provided by your exchange.

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