Learn Computer and Data Science using Algorithmic Trading

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Documentation for package ‘lazytrade’ version 0.5.3

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aml_collect_data Function to read, transform, aggregate and save data for further retraining of regression model for a single asset
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 pair
aml_simulation Function to simulate multiple input structures
aml_test_model Function to test the model and conditionally decide to update existing model for a single currency pair
check_if_optimize Function check_if_optimize.
create_labelled_data Create labelled data
create_transposed_data Create Transposed Data
data_trades Table with Trade results samples
decrypt_mykeys Function that decrypt encrypted content
DFR Table with predicted price change
dlog Create log difference distribution
encrypt_api_key Encrypt api keys
EURUSDM15X75 Table with indicator and price change dataset
evaluate_macroeconomic_event Function used to evaluate market type situation by reading the file with Macroeconomic Events and writing a trigger to the trading robot
get_profit_factorDF Function that returns the profit factors of the systems in a form of a DataFrame
import_data Import Data file with Trade Logs to R.
indicator_dataset Table with indicator dataset
macd_100 Table with indicator only used to train model, 128 col 1646 rows
macd_df Table with one column indicator dataset
macd_ML60M Table with indicator and market type category used to train model
mt_evaluate Function to score data and predict current market type using pre-trained classification model
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 recognition
mt_stat_evaluate Function to prepare and score data, finally predict current market type using pre-trained classification model
mt_stat_transf Perform Statistical transformation and clustering of Market Types on the price data
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
policy_tr_systDF Table with Market Types and sample of actual policy for those states
price_dataset Table with price dataset
price_dataset_big Table with price dataset, 30000 rows
profit_factorDF Table with Trade results samples
profit_factor_data Table with Trade results samples
result_prev Table with one column as result from the model prediction
result_R Table with predicted price change
result_R1 Table with aggregated trade results
rl_generate_policy Function performs Reinforcement Learning using the past data to generate model policy
rl_generate_policy_mt Function performs RL and generates model policy for each Market Type
rl_log_progress Function to retrieve and help to log Q values during RL progress.
rl_log_progress_mt Function to retrieve and help to log Q values during RL progress. This function is dedicated to the situations when Market Types are used as a 'states' for the Environment.
rl_record_policy Record Reinforcement Learning Policy.
rl_record_policy_mt Record Reinforcement Learning Policy for Market Types
rl_write_control_parameters Function to find and write the best control parameters.
rl_write_control_parameters_mt Function to find and write the best control parameters.
test_data_pattern Table with several columns containing indicator values and Label values
to_m Convert time series data to matrix with defined number of columns
TradeStatePolicy Table with Trade States and sample of actual policy for those states
trading_systemDF Table with trade data and joined market type info
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 needs
util_profit_factor Calculate Profit Factor
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 configuration
x_test_model Table with a dataset to test the Model
y Table with indicators and price change which is used to train model