positions-backtester 0.1.5

Package to backtest trading strategies (dataframe with positions) with execution costs modeling

Table of Contents

• Short Overview.

• Installation via pip:

• How to backtest your dataframe with positions

  • Input example: df_positions_short

  • Output: df_backtest_res

• How to create execution prices

  • Inputs:

• Formulas

• Links

• Project local Links

• Contacts

• License

Short Overview.

positions_backtester is a python package (py>=3.7) to backtest trading strategies (dataframe with positions) with execution costs modeling

This package is trying to solve a problem of slow trading with fast data.

Let’s say that you want to update your trading position once in a hour, day, week, …

But you have data with much higher time resolution - minutes, seconds, miliseconds

Then you can give the wanted positions dataframe with the tick with which you want to trade

And higher resolution will be used to calculate approximate execution prices

(Which are just the mean price over the choosen execution time period)

Installation via pip:

pip install positions_backtester

How to backtest your dataframe with positions

from positions_backtester import run_backtest

df_backtest_res = run_backtest(
    df_positions,
    df_real_exec_prices,
    is_to_neutralize=True,
    const_trading_fees_percent=0.01,
)

!!! df_real_exec_prices should have the same indices as df_positions

Arguments:

1. df_positions:

   pd.DataFrame

   Positions we want to take with the frequency with which we want to change our positions

2. df_real_exec_prices:

   pd.DataFrame

   Prices by which we can buy assets at the given moment

3. is_to_neutralize=True,:

   bool

   Flag if to have long-short equal positions

4. td_trading_delay=None:

   datetime.timedelta

   Delay needed to calculate the wanted positions

5. td_execution_duration:

   datetime.timedelta

   How long should the execution take

   Execution price will be the mean price over execution time period

6. const_trading_fees_percent:

   float

   Broker trading fees

Input example: df_positions_short

Output: df_backtest_res

How to create execution prices

from positions_backtester import create_df_simple_execution_prices

df_real_exec_prices = create_df_simple_execution_prices(
    df_prices_full,
    data_frequency="30min",  # 30min, 1h, 3h, 6h, 1d
    offset="-3min",  # "-3min"
    td_trading_delay=datetime.timedelta(minutes=3),
    td_execution_duration=datetime.timedelta(minutes=2),
)

Arguments:

1. df_prices_full:

   pd.DataFrame

   Prices of assets in higher resolution

   Higher resolution needed for more pricise execution evaluation

2. td_trading_delay=None:

   datetime.timedelta

   Delay (Time) needed to calculate the wanted positions

3. td_execution_duration:

   datetime.timedelta

   How long should the execution take

   Execution price will be the mean price over execution time period

Inputs:

df_prices_full

Formulas

PNL before_costs = (previous_position) * (price_change_%)

trading_volume = abs(new_wanted_position - previous_position)

PNL const_trading_fee = trading_volume * broker_commision

PNL after_costs = PNL before_costs - PNL const_trading_fee

PNL half_costs = PNL before_costs - PNL const_trading_fee/2.0

Links

• PYPI

• GitHub

Project local Links

• CHANGELOG.

• CONTRIBUTING.

Contacts

• Email: [e-mail]

• vk.com

• Facebook

License

This project is licensed under the MIT License.