# Learn Computer and Data Science using Algorithmic Trading

## Documentation for package ‘lazytrade’ version 0.3.10

- DESCRIPTION file.

## Help Pages

aml_collect_data | Function to read new data, transform data, save data for further retraining of regression model for a single currency pair
aml_make_model | Function to train Deep Learning regression model for a single currency pair
aml_score_data | Function to score new data and predict change for each single currency pair
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 aggregated trade results
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
evaluate_market_type | Function to score data and predict current market type using pre-trained classification model
generate_RL_policy | Function performs RL and generates model policy
generate_RL_policy_mt | Function performs RL and generates model policy for each Market Type
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.
import_data_mt | Import Market Type related Data to R from the Sandbox
indicator_dataset | Table with indicator dataset
indicator_dataset_big | Table with indicator dataset, 30000 rows
load_asset_data | Load and Prepare Asset Data
log_RL_progress | Function to log RL progress.
log_RL_progress_mt | Function to log RL progress, dedicated to Market Types
macd_100 | Table with indicator only used to train model, 128 rows
macd_df | Table with one column indicator dataset
macd_ML2_small | Table with indicator and market type category used to train model
mt_make_model | Function to train Deep Learning Classification model for Market Type recognition
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_factor | Calculate Profit Factor
profit_factorDF | Table with Trade results samples
profit_factor_data | Table with Trade results samples
record_policy | Record Reinforcement Learning Policy.
record_policy_mt | Record Reinforcement Learning Policy for Market Types
result_prev | Table with one column as result from the model prediction
result_R | Table with predicte price change
self_learn_ai_R | Function to train Deep Learning regression model
test_data_pattern | Table with several columns containing indicator values and Label values
test_model | Test model using independent price data.
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_generate_password | R function to generate random passwords for MT4 platform or other needs
writeCommandViaCSV | Write csv files with indicated commands to the external system
write_command_via_csv | Write csv files with indicated commands to the external system
write_control_parameters | Function to find and write the best control parameters.
write_control_parameters_mt | Function to find and write the best control parameters.
write_ini_file | Create initialization files to launch MT4 platform with specific configuration
x_test_model | Table with a dataset to test the Model