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 a number of preimplemented strategies, but it is also possible to create new strategies, as well as
to combine them. It provides a general Machine Learning strategy, which can be further tweaked to your specific needs.
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.
If you are interested in a trading bot that integrates seamlessly with this library, check out MyCryptoBot.
Installation
$ pip install stratestic
Usage
 Vectorized Backtesting
 Iterative Backtesting
 Backtesting with leverage and margin
3.1. Calculating the maximum allowed leverage  Optimization
4.1 Brute Force
4.2 Genetic Algorithm  Strategies
5.1. Combined strategies
5.2. Create new strategies
5.3. Machine Learning strategy
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 20180523 13:00:00
End Date 20230213 01:00:00
Trading Costs [%] 0.1
Exposure Time [%] 100.0
Leverage [x] 1
Equity  Initial [USDT] 1000
Equity  Final [USDT] 3215.96
Equity  Peak [USDT] 5356.87

Returns

Total Return [%] 221.6
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
Expectancy [%] 5.9

Ratios

Sharpe Ratio 0.33
Sortino Ratio 0.28
Calmar Ratio 0.35
Profit Factor 1.0
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 20180516 15:00:00
End Date 20230213 01:00:00
Trading Costs [%] 0.0
Exposure Time [%] 100.0
Leverage [x] 1
Equity  Initial [USDT] 1000
Equity  Final [USDT] 17145.68
Equity  Peak [USDT] 29566.42

Returns

Total Return [%] 1614.57
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
Expectancy [%] 1.72

Ratios

Sharpe Ratio 0.84
Sortino Ratio 0.8
Calmar Ratio 1.08
Profit Factor 1.01
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. Below an example is shown:
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=1000,
trading_costs=trading_costs,
leverage=7 # Pass the desired leverage here
)
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 20180521 11:00:00
End Date 20230213 01:00:00
Trading Costs [%] 0.05
Exposure Time [%] 100.0
Leverage [x] 7
Equity  Initial [USDT] 1000
Equity  Final [USDT] 0.0
Equity  Peak [USDT] 17012.48

Returns

Total Return [%] 100.0
Annualized Return [%] 100.0
Annualized Volatility [%] 144.81
Buy & Hold Return [%] 157.53

Drawdowns

Max Drawdown [%] 100.0
Avg Drawdown [%] 15.65
Max Drawdown Duration 4 years and 7 weeks
Avg Drawdown Duration 4 weeks and 7 hours

Trades

Total Trades 82
Win Rate [%] 28.05
Best Trade [%] 350.39
Worst Trade [%] 71.82
Avg Trade [%] 0.3
Max Trade Duration 2 weeks and 5 days
Avg Trade Duration 4 days and 10 hours
Expectancy [%] 26.08

Ratios

Sharpe Ratio 0.2
Sortino Ratio 0.45
Calmar Ratio 1.0
Profit Factor 0.86
System Quality Number 0.38

************************************************************
As evident from the results, employing a leverage of 7
led to a margin call 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 above, the result would be:
Out[2]: 5
Optimization
Brute Force
Both the iterative and vectorized backtesters offer an optimization API, enabling the discovery of parameter combinations that yield optimal performance in a backtest, with optimization focused on a specific metric.
The options for optimization metrics are: Return
, Sharpe Ratio
, Calmar Ratio
, Sortino Ratio
,
Win Rate
, Profit Factor
, System Quality Number
, Expectancy
, Volatility
, Maximum Drawdown
,
Average Drawdown
, Maximum Drawdown Duration
, Average Drawdown Duration
. The default is Return
.
The default optimization algorithm is brute force, entailing an analysis of all possible cases. Below is an example demonstrating how to utilize this API:
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) # The VectorizedBacktester class could also be used
vect.load_data() # Load the default sample data. You can pass your own DataFrame to load_data
# Pass as an argument a dictionary with the parameters as keywords
# and with a tuple with the limits to test and the desired step.
# In this case we are optimizing the strategy with the parameter 'window'
# between the values of 1000 and 1500 with a step of 10
vect.optimize(dict(window=(1000, 1500, 10)), optimization_metric='Sharpe Ratio')
This will output the best parameters and show the corresponding best result. For this example, it would be:
100% (50 of 50) ######################## Elapsed Time: 0:00:26 ETA: 0:00:00
Out[2]: ({'window': 1400.0}, 0.9786648787774422)
Genetic Algorithm
In the prior illustration, we utilized the default optimizer—a brute force optimizer—which exhaustively tests all conceivable parameter combinations. However, as the number of parameters grows, the search space expands exponentially, rendering realtime computation unfeasible for extensive input data and parameter combinations. In such scenarios, employing a genetic algorithm can significantly reduce the time required to converge towards an optimum, albeit without guaranteeing the attainment of the global optimum.
Accessing a genetic algorithm solver through this optimization API is exemplified below. This API leverages geneal under the hood. For a comprehensive understanding of the available parameters that can be passed to the solver, please refer to the documentation. All the parameters are optional, but it is recommended to play with them to achieve the best results.
from stratestic.backtesting import IterativeBacktester
from stratestic.strategies import MovingAverageCrossover
symbol = "BTCUSDT"
trading_costs = 0.1
mov_avg = MovingAverageCrossover(30, 200)
ite = IterativeBacktester(mov_avg, symbol, amount=1000, trading_costs=trading_costs)
ite.load_data()
opt_params = {
"sma_s": (800, 1200),
"sma_l": (1200, 1600)
}
ite.optimize(
opt_params,
optimizer='gen_alg',
pop_size=10, # population size (number of individuals)
max_gen=20, # maximum number of generations
mutation_rate=0.1, # mutation rate to apply to the population
selection_rate=0.6, # percentage of the population to select for mating
selection_strategy="roulette_wheel", # strategy to use for selection. see below for more details
fitness_tolerance=(1E5, 10), # Loop will be exited if the best fitness value does not change more than
# 1E5 for 10 generations
verbose=False, # Whether to print best fitness at every iteration
plot_results=True, # Whether to plot the results at the end
)
\  #  191 Elapsed Time: 0:06:38
Out[2]: ({'sma_s': 1030, 'sma_l': 1206}, 1206.09)
In the above example, we allowed a population of 10 individuals to evolve over 20 generations, resulting in approximately 200 calls to the backtester. Had we opted for the brute force algorithm, the number of calls would have skyrocketed to 160,000 (400 * 400), translating to a significantly longer processing time on a standard machine.
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, 2), sma_l=(100, 200, 1)), dict(window=(60, 80, 1))])
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 of how to implement these methods.
Machine Learning Strategy
This library implements a machine learning strategy, which can be used for backtesting in the same way as for the other strategies, but it has the difference that first a model is trained on the training part of the data, and subsequently the backtest is performed on the test set. This ensures that we're performing outofsample backtesting, but it also means that more data is required for a meaningful backtest and good model fit.
What follows is a simple example to demonstrate its usage. For more detailed options please check the docstring of this strategy.
from stratestic.backtesting import VectorizedBacktester
from stratestic.strategies import MachineLearning
symbol = "BTCUSDT"
trading_costs = 0.1 # This should be in percentage, i.e. 0.1%
ml = MachineLearning(
estimator="Random Forest", # The algorithm to use for training the model.
lag_features=["returns"], # A list of columns which we want to create lagged features.
nr_lags=5, # The number of lag periods.
test_size=0.2, # The proportion between test and training data.
verbose=True,
)
vect = VectorizedBacktester(ml, symbol, amount=1000, trading_costs=trading_costs)
vect.load_data()
vect.run()
By default, the model is saved in a directory as indicated by the parameter models_dir
. If one wants to load an
existing model, the model filename must be passed at initialization. The model will then be loaded, instead of
trained.
from stratestic.strategies import MachineLearning
ml = MachineLearning(load_model='<filename>')
The MachineLearning
strategy also provides a method for visualizing learning curves out of the box, which can be
accessed either by the strategy object or the backtester:
vect.learning_curve()
ml.learning_curve() # Same as the call above.
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