tradetestlib: A MetaTrader5 backtesting tool

tradetestlib is a backtesting library built to integrate with MetaTrader5, with the purpose being able to provide a broad overview of a trading strategy/idea, more specifically, an evaluation of a strategy. Some of the main evaluation metrics used in this project are the sharpe ratio, and profit factor.

tradetestlib also provides the option to optimize a strategy by using a Grid Search algorithm for hyperparameter tuning.

Currently, hyperparameters are limited to position sizing, and exposure.

A demonstration can be found here

Installation

tradetestlib can be installed with pip

pip install tradetestlib

Usage

A simulation instances can be created by calling the Simulation class.

symbol and timeframe are only used for naming conventions, for comparing a basket of assets.

train_raw and test_raw are dataframes that contain Open, High, Low, Close, signal, and true_signal columns.

lot is the lot size used to trade

starting_balance is the initial deposit of the simulation in USD.

from tradetestlib import Simulation

sim = Simulation(
    symbol = symbol,
    timeframe = tf,
    train_raw = train,
    test_raw = test,
    lot = 1, 
    starting_balance = 100000    
)

Optimization can be used by creating a params dictionary with the required hyperparameters.

run_grid_search returns the optimal configuration, and overall testing set.

Optimized hyperparameters may also be accessed as attributes, which can then be used to create a final simulation instance, to verify result with the test set.

from tradetestlib import Optimize

params = {
    'lot' : [1,2],
    'hold_time': [5, 10],
    'max_loss': [0.005, 0.01]
}

o = Optimize(symbol = symbol, 
    timeframe = tf, 
    train = train, 
    test = test,
    metric = 'sharpe_ratio',
    how = 'maximize')

best, df = o.run_grid_search(params)

o.optimized_lot # best lot 
o.optimized_holdtime # best holdtime
o.optimized_max_loss # best exposure