traderbacktesteroptalpha 1.3

Package for backtesting trading strategies and updating required files

traderbacktesteroptalpha

This Python script provides three main functionalities:

1. Updating essential files from a server (update_files function).
2. Utility functions for backtesting and trading strategies (TraderBackTesterUtils class).
3. Data manipulation functions for backtesting and trading strategies (TraderBackTesterDM class).

Requirements

This script requires the following libraries:

• pandas_ta==0.3.14b0
• swifter==1.3.4
• openpyxl==3.1.2
• ta==0.10.2
• pandas==1.5.3
• numpy==1.23.5

To install these dependencies, use the following command:

pip install pandas_ta==0.3.14b0 swifter==1.3.4 openpyxl==3.1.2 ta==0.10.2 pandas==1.5.3 numpy==1.23.5  

Usage

1. File Updater (update_files function)

The update_files function downloads and saves three files required for backtesting:

• nse_holidays.xlsx: A list of market holidays.
• angel_tokens.csv: Token data for various trading instruments.
• all.csv: A list of available instruments.

To update these files, call the function:

update_files(files_path='path/to/save/files/', file_server_url='http://your_server_url/')

2. TraderBackTesterUtils Class

This class provides multiple utility methods for trading and backtesting. Initialize it with the path where required files are stored.

Initialization

trader_backtester_utils = TraderBackTesterUtils(files_path='path/to/files/')

Methods

get_delta_strike_def(name: str) -> pd.DataFrame

Returns strike price details for a given instrument, calculating the differences between consecutive strikes.

round_to(row: Any, num_column: str = 'open', precision_column_val: Any = .05) -> float

Rounds a given value to the nearest tick size (default: 0.05).

get_strike(x: Any, num_column: str = 'open', precision_column_val: Any = .05) -> Any

Rounding method that applies round_to on a DataFrame using swifter for parallel processing.

exp_cal(row: Any, x_org: pd.DataFrame, dat: str = 'date', nxt_exp: Any = 0, montly: Any = False) -> str

Calculates the next expiry date for an instrument. Determines weekly or monthly expiry based on input parameters.

get_exp(x: Any, x_org: pd.DataFrame, dat: str = 'date', nxt_exp: Any = 0, montly: bool = False) -> Any

Uses exp_cal to calculate expiries for a DataFrame.

get_lot(name: str) -> pd.DataFrame

Fetches lot size for a specified instrument name.

add_n_lot_only(data: pd.DataFrame, ticker_column: str, exp_colummn_name: str, column_name_to_create: str) -> pd.DataFrame

Adds a new column with lot sizes based on the ticker and expiry date.

is_holiday(date: str = '') -> bool

Checks if a given date is a market holiday. Defaults to checking today's date.

Example Usage

# Initializing
trader_backtester_utils = TraderBackTesterUtils(files_path='path/to/files/')

# Getting delta strike definitions
delta_strike = trader_backtester_utils.get_delta_strike_def(name='NIFTY')

# Rounding example
rounded_value = trader_backtester_utils.round_to(12569.67)

# Calculating expiry
expiry_date = trader_backtester_utils.get_exp(x='2022-04-20', x_org=your_dataframe)

# Adding lot sizes to DataFrame
updated_data = trader_backtester_utils.add_n_lot_only(data=your_dataframe, ticker_column='ticker', exp_colummn_name='expiry', column_name_to_create='lot_size')

3. TraderBackTesterDM Class

This class provides multiple data manipulation methods for trading and backtesting

Initialization

trader_backtester_dm = TraderBackTesterDM()