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_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 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 |