alphabacktest 0.1.9

Module for backtesting algorithmic trading strategies

alphabacktest

DISCLAIMER: The results this backtesting software might produce may not be accurate, reliable or suppose any evidence ensuring a trading strategy profitability. The results are indicative and might not be appropiate for trading purposes. Therefore, the creator does not bear any responsibility for any losses anyone might incur as a result of using this software.

Please, be fully informed about the risks and costs associated with trading the financial markets, as it is one of the riskiest investment forms possible.

Description

alphabacktest is a library that aims at bringing algorithmic trading to all Pyhton programmers via a very simple set of methods that allow backtesting any trading strategy anyone can come up with, allowing external functions or modules.

You can find below an example of the structure to use when working with alphabacktest. The module is designed to be inherited in a new class created by the user and call the methods within the class. Backtest() has the engine to run the backtest chronologically and calls strategy() at each point in the data. The variables passed on to the function are list types with the previous quote for each point for all the prices (high,close...) and volume; except for dtime which is a string representing the current data point.

from alphabacktest import Backtest  

class mStrategy(Backtest):
    ''' Always call super().__init__() '''
    def __init__(self):
        super().__init__(ticker="AMZN")

    ''' You can choose the parameters name'''
    def strategy(self,_open, close, high, low, vol, dtime):
        ''' Fill in your strategy '''
        if not self.has_positions():
            q = int(self.free_balance / close[-1])
            self.long_order(security=self.symbol, amount=q, dtime=dtime, price=close[-1])

if __name__ == '__main__':
    mStrategy()      

As seen, the usage is pretty straightforward, and does not require a huge effort for the user to import and/or work with it, being a smooth process and giving freedom to apply any strategy, from those based on technical indicators to AI, going through many different tools.

Regarding the data source (explained at Usage), getting the proper data is sometimes rather difficult, especially when one is looking for tight timeframes (1m,5m,15m...). This data is not usually free, so this module gives the chance to either get the data from Yahoo Finance or from a csv file that the user can have in their local memory from a purchase or own harvest.

Installation

You can find all releases in PyPI.

$ pip install alphabacktest

Requirements

• Python 3.6+

Dependencies

As some of the features provided by the module are meant to be optional, the installation of alphabacktest does not imply the collection of TA-lib, dash nor pandas-datareader. Therefore, the package comes without these modules, which need to be installed by the user.

• TA-lib. As there are sometimes difficulties when installing this module depending on the OS and IDE configuration, there is an option for the backtesting engine not to calculate any technical indicator (as all are based on TA-lib), so in that case you don't necessarily need to have it installed. In case you want to enable the calculation of technical indicators, you will indeed need the module you can find here.
• Dash. alphabacktest uses dash to plot and display the results. However, this option can also be desactivated. In case you do want it, you can find it here.
• Pandas datareader. This module allows the program to import data from Yahoo Finance, you can find how to install it here.

Usage

The super().__init__() is highly important here to define exactly how we want our backtest to be. It is customisable to an extent and allows multiple features to be desactivated or included.

    super().__init__(self, 
            sym="",
            initial_time="first",
            final_time="last",
            dateformat="%Y-%m-%d",
            file_path="",
            ticker=None,
            indicators=True,
            slippage=0.0001,
            leverage=1,
            fees=0.005/100,
            capital=20000,
            save_results=True,
            save_path=os.getcwd(),
            plot_results=True)

Data source

In the first place, the data can be pulled from Yahoo Finance via pandas_datareader, or it can be imported from a local file.

From Yahoo Finance

In the first case, sym and file_path need to be left as they are, while the ticker needs to be filled with the security symbol (according to the symbols Yahoo Finance uses). The dateformat of Yahoo Finance is the default format.

    super().__init__(self,ticker="AAPL")

From a local CSV file

If it is the second case, the initialization varies a little and sym, file_path and dateformat parameters need to be specified, and do not specify ticker. It is important that the dateformat is specified correctly, otherwise an error will be raised by the datetime module.

    super().__init__(self, 
            sym="SP500",
            dateformat="%Y-%m-%d %H:%M:%S",
            file_path="yourCSVfiledirectory.csv")

However, the requirements for the file are very tight. The engine imports the data and assigns "Datetime","Open","High","Low","Close","Volume" values to the columns in that order. The format in Datetime column will be kept as a string and set as index whilst the format for Open-Volume will be converted to float. At last, the separator needs to be ','. If the format of your csv file is not this one, you can either transform it externally or provide your own data (which is recommended).

From a given DataFrame

The data format must be the same as the described above, meaning "Datetime" (str),"Open" (float),"High" (float),"Low" (float),"Close" (float),"Volume" (float) [IN THIS ORDER]. Datetime must be the dataframe's index.

Example:

data = pd.read_csv('csv/file/path.csv')
''' Data treatment'''
data= data.set_index("Datetime")
data.loc[:,'Open':'Close'] = data.loc[:,'Open':'Close'].astype(float)
class myStrategy(Backtest):
    def __init__(self,data):
        super().__init__(sym='BTCUSD',data=data,initial_time="04/01/2020 01:00:00",dateformat="%d/%m/%Y %H:%M:%S")
myStrategy(data)

Timeperiod

All the data available

If the aim of the user is to backtest the strategy for all the points in the data, the parameters initial_time and final_time should not be modified, they are already set as the very beginning and the last point respectively.

Specific timeframes

If the user wants to backtest certain scenarios, the initial_time and final_time need to be modified accordingly, always following the format specified in dateformat.

    super().__init__(self, 
            sym="GOLD",
            file_path="yourCSVfiledirectory.csv"),
            initial_time="01/01/2015 00:00:00",
            final_time="01/01/2020 23:55:00",
            dateformat="%Y-%m-%d %H:%M:%S",
            )

Extras

Technical Indicators

The technical indicators that come with the alphabacktest module are the SMA, EMA, RSI, Bollinger Bands and MACD. However if anyone would want to change it, you can also redefine the indicators() method.

from bcktclasses_cy import Backtest  

class mStrategy(Backtest):
    def __init__(self):
        super().__init__(ticker="AMZN")

    def strategy(self,_open, close, high, low, vol, dtime):

        ''' Your strategy'''

    def indicators(self, close):
        from talib import RSI, BBANDS, MACD, EMA, SMA #....
        '''Define your indicators'''

if __name__ == '__main__':
    mStrategy()

Otherwise, if the user does not wish to use any of them, the indicators calculation can be desactivated by setting indicators=False in the super().__init() declaration.

Trading environment conditions

In order to simulate a real brokerage activity, the orders are not placed and executed right away. Instead, they first pass through a check and are placed in the following period. This adds more realism and settles the real-pessimistic scenario.

Moreover, the parameters that are required by the engine to reproduce this behaviour are set by default but the user can change them and adapt it to what their broker sets as conditions. The attributes are the following.

• slippage. This parameter refers to the difference between the price at which the order is placed and the price at which the trade is executed CFI. Although the slippage can really be zero, positive or negative, in alphabacktest it is considered to always be playing against the interests of the trader.

• leverage. Investopedia describes the leverage as found in the following quote.

  Leverage refers to the use of debt (borrowed funds) to amplify returns from an investment or project.

  In this module the leverage is a multiplier that refers to the n times your capital is increased by the debt. Leverage can range from 1 (meaning no debt) up to 400 depending on the financial product. (Again, thanks Investopedia). Nonetheless, this number is usually set by your broker.

• fees. This refers to the commission the broker is charging per unit of currency, meaning that it will be dependent on the amount the trader allocates to the trade.

-capital. Total initial amount of liquidity the account is provided with. It is not considered to be in any specific currency; just the currency the security is traded with. Therefore, the backtest will not consider fluctuations due to the FX markets evolution.

Results treatment

The default configuration is set to save the results in csv files inside a folder the engine creates in the cwd and it runs a Dash app where the results are plotted and presented. In case the user wanted to desactivate any of this features, the initialisation allows it. Setting save_results or plot_results (bool) as False. Moreover, if the user wanted to change the directory where the results are stored, save_path (str) is the parameter to customize.

Example:

    super().__init__(self, 
            sym="",
            initial_time="first",
            final_time="last",
            dateformat="%Y-%m-%d",
            file_path="",
            ticker=None,
            indicators=True,
            slippage=0.0001,
            leverage=1,
            fees=0.005/100,
            capital=20000,
            save_results=True,
            save_path="your/preferred/directory",
            plot_results=False)

Class attributes

The attributes of the inherited class are the following.

• self.user_positions Pandas DataFrame containing the history of all positions

• self.user_portfolio Pandas DataFrame with the representation of the assets in the user's portfolio and the total value of their wallet.

• self.trades Pandas DataFrame with the information on the executed trades

• self.orders Pandas DataFrame with the information of all orders either they are placed or not.

Class methods

The methods of the inherited class are the following ones.

• self.long_order(security, amount, dtime, price) Sends a long (buy) order to the broker. The parameters are:

  • security: str. Name of the security to be traded, it is a must for the engine to account the traded asset.
  • amount: int. Quantity of contracts the order aims at. The minimum quantity is 1 and only whole (integer) numbers are accepted.
  • dtime: str. Time at which the long order is placed in the specified dateformat.
  • price: float. Price at which the order is aimed. This price will not have any influence, but is useful for further analysis on the results of one's trades.

• self.short_order(security, amount, dtime, price) Sends a short (sell) order to the broker. The parameters are:

  • security: str. Name of the security to be traded, it is a must for the engine to account the traded asset.
  • amount: int. Quantity of contracts the order aims at. The minimum quantity is 1 and only whole (integer) numbers are accepted.
  • dtime: str. Time at which the short order is placed in the specified dateformat.
  • price: float. Price at which the order is aimed. This price will not have any influence, but is useful for further analysis on the results of one's trades.

• self.closing_order(p_id, dtime, double price) Sends a closing order to the broker. The parameters are:

  • pID: str. This represents the position ID, which is generated randomly once a position starts after the trade is executed. It is placed at the Position DataFrame index and allows the selection of a particular position.
  • dtime: str. Time at which the closing order is placed in the specified dateformat.
  • price: float. Price at which the order is aimed. This price will not have any influence, but is useful for further analysis on the results of one's trades.

• self.has_positions() Returns a bool referring to the possession of an open contract. Returns True if there are open contracts and False if there is no open position. No parameters taken.

• self.get_positions(security,_open=True) Returns a pandas DataFrame with the positions related to the specified security. The parameters are:

  • security: str. Name of the security of the positions.
  • _open: bool. Refers to the state of the positions to be returned. If the method is called with _open=True, the positions that will be returned are the currently open posigions; whilst if it is _open=False, the method will return all positions regardless their state.

• self.get_long_positions(security,_open=True) Returns a pandas Dataframe with only long positions. The parameters are:

  • security: str. Name of the security of the positions.
  • _open: bool. Refers to the state of the positions to be returned. If the method is called with _open=True, the positions that will be returned are the currently open posigions; whilst if it is _open=False, the method will return all positions regardless their state.

• self.get_short_positions(security,_open=True) Returns a pandas Dataframe with only short positions. The parameters are:

  • security: str. Name of the security of the positions.
  • _open: bool. Refers to the state of the positions to be returned. If the method is called with _open=True, the positions that will be returned are the currently open posigions; whilst if it is _open=False, the method will return all positions regardless their state.

Results

The results are obtained via csv files and a dash app summary of the strategy performance that will be running on the local server http://127.0.0.1:port/ as the logs report.

Features

TO DO:

• Multiple assets (threading)
• Improve resulting dash app

Credits

This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter .. _audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage