Library for testing and analysing trading strategies.
Project description
Stratestic 📈📊🛠️
The stratestic
module is a Python package for backtesting, analysing and optimizing trading strategies.
It includes pre-implemented strategies, but it is also possible to create new strategies or even to combine them.
The basic usage is as simple as importing a backtesting class, a strategy and run the backtest. The backtest can then be refined with optimizations or by incorporating leverage.
Overall it offers a powerful way to explore and experiment with different strategies, and to evaluate the performance under different conditions.
Installation
$ pip install stratestic
Usage
- Vectorized Backtesting
- Iterative Backtesting
- Backtesting with leverage and margin
3.1. Calculating the maximum allowed leverage - Optimization
- Strategies
5.1. Combined strategies
5.2. Create new strategies
Vectorized Backtesting
The VectorizedBacktester
is a backtesting class that allows you to test trading strategies
on historical price data. It has the advantage of being faster than the iterative backtesting, but at
a cost of flexibility, as it will be hard or outright not possible to accomplish this for some more
complex strategies. For all the strategies provided by this library, vectorized backtesting is supported.
Below is an example of how to use it for the MovingAverageCrossover
strategy:
from stratestic.backtesting import VectorizedBacktester
from stratestic.strategies import MovingAverageCrossover
symbol = "BTCUSDT"
trading_costs = 0.1 # This should be in percentage, i.e. 0.1%
mov_avg = MovingAverageCrossover(50, 200)
vect = VectorizedBacktester( # Initializes the VectorizedBacktester class with the strategy.
mov_avg,
symbol,
amount=1000,
trading_costs=trading_costs
)
vect.load_data() # Load the default sample data. You can pass your own DataFrame to 'load_data'
vect.run() # Runs the backtest and shows the results
This will output the results in textual and graphical form.
**************************************************
BACKTESTING RESULTS
**************************************************
Overview
--------------------------------------------------
Total Duration 4 years and 38 weeks
Start Date 2018-05-23 13:00:00
End Date 2023-02-13 00:00:00
Trading Costs [%] 0.1
Leverage [x] 1
Initial Equity [USDT] 1000
Exposed Capital [USDT] 1000.0
Exposure Time [%] 100.0
--------------------------------------------------
Returns
--------------------------------------------------
Total Return [%] 221.6
Equity Final [USDT] 3212.75
Equity Peak [USDT] 5351.51
Annualized Return [%] 21.49
Annualized Volatility [%] 73.95
Buy & Hold Return [%] 175.98
--------------------------------------------------
Drawdowns
--------------------------------------------------
Max Drawdown [%] -61.18
Avg Drawdown [%] -8.2
Max Drawdown Duration 1 year and 38 weeks
Avg Drawdown Duration 3 weeks and 2 days
--------------------------------------------------
Trades
--------------------------------------------------
Total Trades 267
Win Rate [%] 32.21
Best Trade [%] 87.77
Worst Trade [%] -21.11
Avg Trade [%] 0.44
Max Trade Duration 5 weeks and 3 days
Avg Trade Duration 6 days and 11 hours
--------------------------------------------------
Ratios
--------------------------------------------------
Sharpe Ratio 0.07
Sortino Ratio 0.28
Calmar Ratio 0.35
Profit Factor 1.0
Expectancy [%] 5.9
System Quality Number -0.02
--------------------------------------------------
**************************************************
Iterative Backtesting
The IterativeBacktester
is a backtesting class that allows you to test trading strategies
on historical price data. It works by iterating through each historical data point and simulating
trades based on your strategy. This feature allows for a greater degree of flexibility,
allowing you to add more complex logic to the strategies. Below is an example of how you would use this
class to backtest the MovingAverageConvergenceDivergence
strategy.
from stratestic.backtesting import IterativeBacktester
from stratestic.strategies import MovingAverageConvergenceDivergence
symbol = "BTCUSDT"
macd = MovingAverageConvergenceDivergence(26, 12, 9)
ite = IterativeBacktester(macd, symbol=symbol) # Initializes the IterativeBacktester class with the strategy
ite.load_data() # Load the default sample data. You can pass your own DataFrame to load_data
ite.run() # Runs the backtest and shows the results
This will output the results in textual and graphical form.
**************************************************
BACKTESTING RESULTS
**************************************************
Overview
--------------------------------------------------
Total Duration 4 years and 39 weeks
Start Date 2018-05-16 15:00:00
End Date 2023-02-13 00:00:00
Trading Costs [%] 0.0
Leverage [x] 1
Initial Equity [USDT] 1000
Exposed Capital [USDT] 1000.0
Exposure Time [%] 100.0
--------------------------------------------------
Returns
--------------------------------------------------
Total Return [%] 1614.57
Equity Final [USDT] 17145.68
Equity Peak [USDT] 29566.42
Annualized Return [%] 60.58
Annualized Volatility [%] 70.99
Buy & Hold Return [%] 163.16
--------------------------------------------------
Drawdowns
--------------------------------------------------
Max Drawdown [%] -56.09
Avg Drawdown [%] -5.46
Max Drawdown Duration 1 year and 22 weeks
Avg Drawdown Duration 1 week and 1 day
--------------------------------------------------
Trades
--------------------------------------------------
Total Trades 3136
Win Rate [%] 34.92
Best Trade [%] 45.61
Worst Trade [%] -12.84
Avg Trade [%] 0.09
Max Trade Duration 2 days and 14 hours
Avg Trade Duration 13 hours and 15 minutes
--------------------------------------------------
Ratios
--------------------------------------------------
Sharpe Ratio 0.17
Sortino Ratio 0.8
Calmar Ratio 1.08
Profit Factor 1.01
Expectancy [%] 1.72
System Quality Number 0.16
--------------------------------------------------
**************************************************
Backtesting with leverage and margin
Both the Vectorized and Iterative backtesting classes provide users with the ability to incorporate leverage into a backtest and visualize the margin ratio evolution during the backtest. This feature enables users to identify instances where a margin call would occur, leading to a potential loss of all funds. The calculations follow the rules outlined by Binance, as detailed here and here. It's important to note that these calculations assume the selected margin is Isolated, and the position mode is One Way. To utilize this functionality, follow these steps:
from stratestic.backtesting import VectorizedBacktester
from stratestic.strategies import MovingAverageCrossover
symbol = "BTCUSDT"
trading_costs = 0.05
mov_avg = MovingAverageCrossover(20, 150)
vect = VectorizedBacktester(
mov_avg,
symbol,
amount=10000,
trading_costs=trading_costs,
include_margin=True, # This tells the backtester to include the margin calculations
leverage=8 # Here one can choose the desired leverage
)
vect.load_data()
vect.run()
This will output the following results and plot:
**************************************************
BACKTESTING RESULTS
**************************************************
Overview
--------------------------------------------------
Total Duration 4 years and 38 weeks
Start Date 2018-05-21 11:00:00
End Date 2023-02-13 00:00:00
Trading Costs [%] 0.05
Leverage [x] 8
Initial Equity [USDT] 10000
Exposed Capital [USDT] 1250.0
Exposure Time [%] 100.0
--------------------------------------------------
Returns
--------------------------------------------------
Total Return [%] 14588.33
Equity Final [USDT] 192257.96
Equity Peak [USDT] 330531.27
Annualized Return [%] 131.49
Annualized Volatility [%] 71.64
Buy & Hold Return [%] 1260.2
--------------------------------------------------
Drawdowns
--------------------------------------------------
Max Drawdown [%] -69.84
Avg Drawdown [%] -5.98
Max Drawdown Duration 1 year and 38 weeks
Avg Drawdown Duration 1 week and 2 days
--------------------------------------------------
Trades
--------------------------------------------------
Total Trades 406
Win Rate [%] 29.31
Best Trade [%] 519.44
Worst Trade [%] -83.34
Avg Trade [%] -4.63
Max Trade Duration 3 weeks and 4 days
Avg Trade Duration 4 days and 6 hours
--------------------------------------------------
Ratios
--------------------------------------------------
Sharpe Ratio 0.17
Sortino Ratio 0.84
Calmar Ratio 0.91
Profit Factor 1.07
Expectancy [%] 4.61
System Quality Number 0.33
--------------------------------------------------
**************************************************
As evident from the results, employing a leverage of 8
led to 3 margin calls during the backtest,
showing that this particular strategy would have implied a total loss of the funds, unless more margin was
added to the positions in the meantime.
Calculating the maximum allowed leverage
The backtesting class also offers an API to determine the maximum permissible leverage for a backtest, ensuring that the margin ratio remains below a specified threshold. This can be accomplished by following the steps outlined in the following example.
from stratestic.backtesting import VectorizedBacktester
from stratestic.strategies import MovingAverageCrossover
symbol = "BTCUSDT"
trading_costs = 0.05
mov_avg = MovingAverageCrossover(20, 50)
vect = VectorizedBacktester(
mov_avg,
symbol,
amount=10000,
trading_costs=trading_costs,
)
vect.load_data()
vect.maximum_leverage(margin_threshold=0.8) # The margin threshold will be the maximum margin_ratio allowed during the
# backtest. If omitted, then the default value of 0.8 is used. Must be
# # between 0 and 1.
Which will output the maximum leverage without a margin call. In the example, it would be:
Out[2]: 5
Optimization
You can use the optimization API of either the iterative or vectorized backtester in order to find the best combination of parameters for a backtest. Below is an example of how to achive this.
from stratestic.backtesting import VectorizedBacktester
from stratestic.strategies import Momentum
symbol = "BTCUSDT"
trading_costs = 0.1
mom = Momentum(30) # Initialize the strategy object with any values.
vect = VectorizedBacktester(mom, symbol=symbol, trading_costs=trading_costs) # It could also have been the
# IterativeBacktester class
vect.load_data() # Load the default sample data. You can pass your own DataFrame to load_data
vect.optimize(dict(window=(40, 90))) # Pass as an argument a dictionary with the parameters as keywords and
# with a tuple with the limits to test as the value. In this case we are
# testing the strategy with the parameter 'window' between the values of
# 40 and 90
This will output the best parameters and show the corresponding results.
Strategies
Combined strategies
It is possible to combine 2 or more strategies into one, by means of the StrategyCombiner
class. The options
for combining the strategies are Unanimous
or Majority
. The Unaninmous
option signals a buy or a sell
if all the individual strategy signals agree (unanimous), whereas the Majority
method provides a buy a
or sell signal if the majority of the individual strategy signals points in one direction.
Here's an example of how that could be achieved:
from stratestic.backtesting import VectorizedBacktester
from stratestic.strategies import MovingAverageCrossover, Momentum, BollingerBands
from stratestic.backtesting.combining import StrategyCombiner
symbol = "BTCUSDT"
trading_costs = 0.1
mov_avg = MovingAverageCrossover(30, 200)
momentum = Momentum(70)
boll_bands = BollingerBands(20, 2)
# The strategies are passed on to StrategyCombiner as list.
combined = StrategyCombiner([mov_avg, momentum, boll_bands], method='Unanimous')
vect = VectorizedBacktester(combined, symbol, amount=1000, trading_costs=trading_costs)
vect.load_data() # Load the default sample data. You can pass your own DataFrame to 'load_data'
vect.run()
This strategy combiner class can also be optimized using the same API, with the difference that the optimization parameters have to be passed in an array. See the next example:
from stratestic.backtesting import VectorizedBacktester
from stratestic.strategies import MovingAverageCrossover, Momentum
from stratestic.backtesting.combining import StrategyCombiner
symbol = "BTCUSDT"
trading_costs = 0.1
mov_avg = MovingAverageCrossover(30, 200)
momentum = Momentum(70)
# The strategies are passed on to StrategyCombiner as list.
combined = StrategyCombiner([mov_avg, momentum], method='Majority')
vect = VectorizedBacktester(combined, symbol, amount=1000, trading_costs=trading_costs)
vect.load_data() # Load the default sample data. You can pass your own DataFrame to 'load_data'
# The optimization parameters are passed as an array of dictionaries containing the
# parameter intervals and steps for each individual strategy.
vect.optimize([dict(sma_s=(20, 40), sma_l=(100, 200)), dict(window=(60, 80))])
Create new strategies
This module comes with some default strategies ready to be used, but chances are you will want
to expand this and create your own strategies. This can be easily achieved by using the template class below,
which inherits the StrategyMixin
class:
from collections import OrderedDict
from stratestic.strategies._mixin import StrategyMixin
class MyStrategy(StrategyMixin):
"""
Description of my strategy
Parameters
----------
parameter1 : type
Description of parameter1.
parameter2 : type, optional
Description of parameter2, by default default_value.
Attributes
----------
params : OrderedDict
Parameters for the strategy, by default {"parameter1": lambda x: x}
Methods
-------
__init__(self, parameter1, parameter2=default_value, **kwargs)
Initializes the strategy object.
update_data(self)
Retrieves and prepares the data.
calculate_positions(self, data)
Calculates positions based on strategy rules.
get_signal(self, row=None)
Returns signal based on current data.
"""
def __init__(
self,
parameter1: <type>,
parameter2: <type> = <some_default_value>,
data=None,
**kwargs
):
"""
Initializes the strategy object.
Parameters
----------
parameter1 : type
Description of parameter1.
parameter2 : type, optional
Description of parameter2, by default default_value.
data : pd.DataFrame, optional
Dataframe of OHLCV data, by default None.
**kwargs : dict, optional
Additional keyword arguments to be passed to parent class, by default None.
"""
self._parameter1 = parameter1 # Each specific parameter that you want to add to the strategy
# must be initalized in this manner, with a _ followed by the name
# of the parameter
self._parameter2 = parameter2
self.params = OrderedDict(
parameter1=lambda x: <type>(x),
parameter2=lambda x: <type>(x)
)
StrategyMixin.__init__(self, data, **kwargs)
def update_data(self, data):
"""
Updates the input data with additional columns required for the strategy.
Parameters
----------
data : pd.DataFrame
OHLCV data to be updated.
Returns
-------
pd.DataFrame
Updated OHLCV data containing additional columns.
"""
super().update_data(data)
# Code to update data goes here. Check the given strategies for an example.
return data
def calculate_positions(self, data):
"""
Calculates positions based on strategy rules.
Parameters
----------
data : pd.DataFrame
OHLCV data.
Returns
-------
pd.DataFrame
OHLCV data with additional 'position' column containing -1 for short, 1 for long.
"""
data["side"] = # Code to calculate side goes here
return data
def get_signal(self, row=None):
"""
Returns signal based on current data.
Parameters
----------
row : pd.Series, optional
Row of OHLCV data to generate signal for, by default None.
Returns
-------
int
Signal (-1 for short, 1 for long, 0 for neutral).
"""
# Code to generate signal goes here
return signal
You would replace MyStrategy
with the name of your strategy, and replace "Description of my strategy"
with a brief explanation of what your strategy does.
__init__()
is where you initialize your strategy parameters. In the case of our example strategy outlined
above, parameter1
and parameter2
would be replaced with the actual names of your strategy's parameter(s),
and <type>
would be replaced with the appropriate data types of your parameters.
This is very important for appropriate type checking on the frontend.
The params
attribute is an OrderedDict
that specifies the default parameters for your strategy.
The key is the parameter name, and the value is a lambda function that converts the user's input
into the appropriate data type.
Finally, we need to call StrategyMixin.init(self, data, **kwargs) in order to initialize the parent class.
update_data()
should contain code to retrieve and prepare the data for your strategy. This is where you can
add indicators or manipulate the data and create new columns that will then be used to calculate a signal.
And example if you were developing a momentum strategy would be to calculate the moving average for the selected window.
calculate_positions()
should contain code to calculate the positions for your strategy based
on the current data. This is where you input the logic of your strategy in a vectorized way. For the same example
of the momentum strategy, here you'd add the logic for getting the signal of when it was a BUY or a SELL.
Note that this may not be possible if your strategy is very complex. In that this method can be ignored, and only the IterativeBacktester can be used.
get_signal()
should contain code to generate the signal for a given row of data. The signal
should be an integer, where -1 represents a short position, 1 represents a long position,
and 0 represents a neutral position.
In any case it is highly recommended to check the existing strategies to get a better idea on how to implement these methods.
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