positions_backtester

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:

df_positions:

pd.DataFrame

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

pd.DataFrame

Prices by which we can buy assets at the given moment
is_to_neutralize=True,:

bool

Flag if to have long-short equal positions
td_trading_delay=None:

datetime.timedelta

Delay needed to calculate the wanted positions
td_execution_duration:

datetime.timedelta

How long should the execution take

Execution price will be the mean price over execution time period
const_trading_fees_percent:

float

Broker trading fees

Input example: df_positions_short

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:

df_prices_full:

pd.DataFrame

Prices of assets in higher resolution

Higher resolution needed for more pricise execution evaluation
td_trading_delay=None:

datetime.timedelta

Delay (Time) needed to calculate the wanted positions
td_execution_duration:

datetime.timedelta

How long should the execution take

Execution price will be the mean price over execution time period

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

Contacts

Email: stas.prokopiev@gmail.com

vk.com

Facebook

License

This project is licensed under the MIT License.

Metadata

Release files for positions-backtester 0.1.5

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Source distribution for positions-backtester 0.1.5
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Table of built distributions (wheels) for positions-backtester 0.1.5
File	Interpreter	ABI	Platform	Reset
positions_backtester-0.1.5-py3-none-any.whl 10.4 kB May 2, 2022 Without provenance	Python 3	none	any	Details

Total release size: 21.2 kB

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Release files / positions_backtester-0.1.5-py3-none-any.whl

Download URL	positions_backtester-0.1.5-py3-none-any.whl
Size	10.4 kB
Tags	Python 3
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Upload date	May 2, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.1.13 CPython/3.9.7 Windows/10`

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