lazytrade0.5.4 package

Learn Computer and Data Science using Algorithmic Trading

util_profit_factor

Calculate Profit Factor

aml_collect_data

Function to read, transform, aggregate and save data for further retra...

aml_consolidate_results

Function to consolidate model test results

aml_make_model

Function to train Deep Learning regression model for a single asset

aml_score_data

Function to score new data and predict change for each single currency...

aml_simulation

Function to simulate multiple input structures

aml_test_model

Function to test the model and conditionally decide to update existing...

check_if_optimize

Function check_if_optimize.

create_labelled_data

Create labelled data

create_transposed_data

Create Transposed Data

decrypt_mykeys

Function that decrypt encrypted content

dlog

Create log difference distribution

encrypt_api_key

Encrypt api keys

evaluate_macroeconomic_event

Function used to evaluate market type situation by reading the file wi...

get_profit_factorDF

Function that returns the profit factors of the systems in a form of a...

import_data

Import Data file with Trade Logs to R.

mt_evaluate

Function to score data and predict current market type using pre-train...

mt_import_data

Import Market Type related Data to R from the Sandbox

mt_make_model

Function to train Deep Learning Classification model for Market Type r...

mt_stat_evaluate

Function to prepare and score data, finally predict current market typ...

mt_stat_transf

Perform Statistical transformation and clustering of Market Types on t...

opt_aggregate_results

Function to aggregate trading results from multiple folders and files

opt_create_graphs

Function to create summary graphs of the trading results

rl_generate_policy_mt

Function performs RL and generates model policy for each Market Type

rl_write_control_parameters_mt

Function to find and write the best control parameters.

rl_generate_policy

Function performs Reinforcement Learning using the past data to genera...

rl_log_progress_mt

Function to retrieve and help to log Q values during RL progress. This...

rl_log_progress

Function to retrieve and help to log Q values during RL progress.

rl_record_policy_mt

Record Reinforcement Learning Policy for Market Types

rl_record_policy

Record Reinforcement Learning Policy.

rl_write_control_parameters

Function to find and write the best control parameters.

to_m

Convert time series data to matrix with defined number of columns

util_find_file_with_code

R function to find file with specific code within it's content

util_find_pid

R function to find PID of active applications

util_generate_password

R function to generate random passwords for MT4 platform or other need...

write_command_via_csv

Write csv files with indicated commands to the external system

write_ini_file

Create initialization files to launch MT4 platform with specific confi...

Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading. Main goal is to process information within "Decision Support System" to come up with analysis or predictions. There are several utilities such as dynamic and adaptive risk management using reinforcement learning and even functions to generate predictions of price changes using pattern recognition deep regression learning. Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, Market Type research of Van Tharp Institute: <https://vantharp.com/>, Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.

  • Maintainer: Vladimir Zhbanko
  • License: MIT + file LICENSE
  • Last published: 2024-07-16